Poly(A) code analyses reveal key determinants for tissue-specific mRNA alternative polyadenylation

PubMed Central

Weng, Lingjie; Li, Yi; Xie, Xiaohui; Shi, Yongsheng

2016-01-01

mRNA alternative polyadenylation (APA) is a critical mechanism for post-transcriptional gene regulation and is often regulated in a tissue- and/or developmental stage-specific manner. An ultimate goal for the APA field has been to be able to computationally predict APA profiles under different physiological or pathological conditions. As a first step toward this goal, we have assembled a poly(A) code for predicting tissue-specific poly(A) sites (PASs). Based on a compendium of over 600 features that have known or potential roles in PAS selection, we have generated and refined a machine-learning algorithm using multiple high-throughput sequencing-based data sets of tissue-specific and constitutive PASs. This code can predict tissue-specific PASs with >85% accuracy. Importantly, by analyzing the prediction performance based on different RNA features, we found that PAS context, including the distance between alternative PASs and the relative position of a PAS within the gene, is a key feature for determining the susceptibility of a PAS to tissue-specific regulation. Our poly(A) code provides a useful tool for not only predicting tissue-specific APA regulation, but also for studying its underlying molecular mechanisms. PMID:27095026

The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools.

PubMed

De Buck, Stefan S; Sinha, Vikash K; Fenu, Luca A; Gilissen, Ron A; Mackie, Claire E; Nijsen, Marjoleen J

2007-04-01

The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.

Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Mental models accurately predict emotion transitions

PubMed Central

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study.

PubMed

Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y

2015-07-07

To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ(2) test. A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the negative predictive

Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study

PubMed Central

Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y

2015-01-01

AIM: To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. METHODS: We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ2 test. RESULTS: A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the

Analytical prediction of sub-surface thermal history in translucent tissue phantoms during plasmonic photo-thermotherapy (PPTT).

PubMed

Dhar, Purbarun; Paul, Anup; Narasimhan, Arunn; Das, Sarit K

2016-12-01

Knowledge of thermal history and/or distribution in biological tissues during laser based hyperthermia is essential to achieve necrosis of tumour/carcinoma cells. A semi-analytical model to predict sub-surface thermal distribution in translucent, soft, tissue mimics has been proposed. The model can accurately predict the spatio-temporal temperature variations along depth and the anomalous thermal behaviour in such media, viz. occurrence of sub-surface temperature peaks. Based on optical and thermal properties, the augmented temperature and shift of the peak positions in case of gold nanostructure mediated tissue phantom hyperthermia can be predicted. Employing inverse approach, the absorption coefficient of nano-graphene infused tissue mimics is determined from the peak temperature and found to provide appreciably accurate predictions along depth. Furthermore, a simplistic, dimensionally consistent correlation to theoretically determine the position of the peak in such media is proposed and found to be consistent with experiments and computations. The model shows promise in predicting thermal distribution induced by lasers in tissues and deduction of therapeutic hyperthermia parameters, thereby assisting clinical procedures by providing a priori estimates. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize.

PubMed

Huang, Ji; Zheng, Juefei; Yuan, Hui; McGinnis, Karen

2018-06-07

Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize. Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm. By combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

NASA Astrophysics Data System (ADS)

Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

2017-03-01

Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

TSAPA: identification of tissue-specific alternative polyadenylation sites in plants.

PubMed

Ji, Guoli; Chen, Moliang; Ye, Wenbin; Zhu, Sheng; Ye, Congting; Su, Yaru; Peng, Haonan; Wu, Xiaohui

2018-06-15

Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants. https://github.com/BMILAB/TSAPA. Supplementary data are available at Bioinformatics online.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

PubMed Central

Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

Optimization of tissue physical parameters for accurate temperature estimation from finite-element simulation of radiofrequency ablation.

PubMed

Subramanian, Swetha; Mast, T Douglas

2015-10-07

Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature.

Improved Rubin-Bodner Model for the Prediction of Soft Tissue Deformations

PubMed Central

Zhang, Guangming; Xia, James J.; Liebschner, Michael; Zhang, Xiaoyan; Kim, Daeseung; Zhou, Xiaobo

2016-01-01

In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve. PMID:27717593

Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

PubMed

Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

2018-06-01

Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

PubMed Central

Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

2015-01-01

Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Identification of Novel Tissue-Specific Genes by Analysis of Microarray Databases: A Human and Mouse Model

PubMed Central

Suh, Yeunsu; Davis, Michael E.; Lee, Kichoon

2013-01-01

Understanding the tissue-specific pattern of gene expression is critical in elucidating the molecular mechanisms of tissue development, gene function, and transcriptional regulations of biological processes. Although tissue-specific gene expression information is available in several databases, follow-up strategies to integrate and use these data are limited. The objective of the current study was to identify and evaluate novel tissue-specific genes in human and mouse tissues by performing comparative microarray database analysis and semi-quantitative PCR analysis. We developed a powerful approach to predict tissue-specific genes by analyzing existing microarray data from the NCBI′s Gene Expression Omnibus (GEO) public repository. We investigated and confirmed tissue-specific gene expression in the human and mouse kidney, liver, lung, heart, muscle, and adipose tissue. Applying our novel comparative microarray approach, we confirmed 10 kidney, 11 liver, 11 lung, 11 heart, 8 muscle, and 8 adipose specific genes. The accuracy of this approach was further verified by employing semi-quantitative PCR reaction and by searching for gene function information in existing publications. Three novel tissue-specific genes were discovered by this approach including AMDHD1 (amidohydrolase domain containing 1) in the liver, PRUNE2 (prune homolog 2) in the heart, and ACVR1C (activin A receptor, type IC) in adipose tissue. We further confirmed the tissue-specific expression of these 3 novel genes by real-time PCR. Among them, ACVR1C is adipose tissue-specific and adipocyte-specific in adipose tissue, and can be used as an adipocyte developmental marker. From GEO profiles, we predicted the processes in which AMDHD1 and PRUNE2 may participate. Our approach provides a novel way to identify new sets of tissue-specific genes and to predict functions in which they may be involved. PMID:23741331

Structural modeling of tissue-specific mitochondrial alanyl-tRNA synthetase (AARS2) defects predicts differential effects on aminoacylation

PubMed Central

Euro, Liliya; Konovalova, Svetlana; Asin-Cayuela, Jorge; Tulinius, Már; Griffin, Helen; Horvath, Rita; Taylor, Robert W.; Chinnery, Patrick F.; Schara, Ulrike; Thorburn, David R.; Suomalainen, Anu; Chihade, Joseph; Tyynismaa, Henna

2015-01-01

The accuracy of mitochondrial protein synthesis is dependent on the coordinated action of nuclear-encoded mitochondrial aminoacyl-tRNA synthetases (mtARSs) and the mitochondrial DNA-encoded tRNAs. The recent advances in whole-exome sequencing have revealed the importance of the mtARS proteins for mitochondrial pathophysiology since nearly every nuclear gene for mtARS (out of 19) is now recognized as a disease gene for mitochondrial disease. Typically, defects in each mtARS have been identified in one tissue-specific disease, most commonly affecting the brain, or in one syndrome. However, mutations in the AARS2 gene for mitochondrial alanyl-tRNA synthetase (mtAlaRS) have been reported both in patients with infantile-onset cardiomyopathy and in patients with childhood to adulthood-onset leukoencephalopathy. We present here an investigation of the effects of the described mutations on the structure of the synthetase, in an effort to understand the tissue-specific outcomes of the different mutations. The mtAlaRS differs from the other mtARSs because in addition to the aminoacylation domain, it has a conserved editing domain for deacylating tRNAs that have been mischarged with incorrect amino acids. We show that the cardiomyopathy phenotype results from a single allele, causing an amino acid change R592W in the editing domain of AARS2, whereas the leukodystrophy mutations are located in other domains of the synthetase. Nevertheless, our structural analysis predicts that all mutations reduce the aminoacylation activity of the synthetase, because all mtAlaRS domains contribute to tRNA binding for aminoacylation. According to our model, the cardiomyopathy mutations severely compromise aminoacylation whereas partial activity is retained by the mutation combinations found in the leukodystrophy patients. These predictions provide a hypothesis for the molecular basis of the distinct tissue-specific phenotypic outcomes. PMID:25705216

Novel green tissue-specific synthetic promoters and cis-regulatory elements in rice.

PubMed

Wang, Rui; Zhu, Menglin; Ye, Rongjian; Liu, Zuoxiong; Zhou, Fei; Chen, Hao; Lin, Yongjun

2015-12-11

As an important part of synthetic biology, synthetic promoter has gradually become a hotspot in current biology. The purposes of the present study were to synthesize green tissue-specific promoters and to discover green tissue-specific cis-elements. We first assembled several regulatory sequences related to tissue-specific expression in different combinations, aiming to obtain novel green tissue-specific synthetic promoters. GUS assays of the transgenic plants indicated 5 synthetic promoters showed green tissue-specific expression patterns and different expression efficiencies in various tissues. Subsequently, we scanned and counted the cis-elements in different tissue-specific promoters based on the plant cis-elements database PLACE and the rice cDNA microarray database CREP for green tissue-specific cis-element discovery, resulting in 10 potential cis-elements. The flanking sequence of one potential core element (GEAT) was predicted by bioinformatics. Then, the combination of GEAT and its flanking sequence was functionally identified with synthetic promoter. GUS assays of the transgenic plants proved its green tissue-specificity. Furthermore, the function of GEAT flanking sequence was analyzed in detail with site-directed mutagenesis. Our study provides an example for the synthesis of rice tissue-specific promoters and develops a feasible method for screening and functional identification of tissue-specific cis-elements with their flanking sequences at the genome-wide level in rice.

Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

PubMed

Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

2016-10-15

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights

Identification of regulatory targets of tissue-specific transcription factors: application to retina-specific gene regulation

PubMed Central

Qian, Jiang; Esumi, Noriko; Chen, Yangjian; Wang, Qingliang; Chowers, Itay; Zack, Donald J.

2005-01-01

Identification of tissue-specific gene regulatory networks can yield insights into the molecular basis of a tissue's development, function and pathology. Here, we present a computational approach designed to identify potential regulatory target genes of photoreceptor cell-specific transcription factors (TFs). The approach is based on the hypothesis that genes related to the retina in terms of expression, disease and/or function are more likely to be the targets of retina-specific TFs than other genes. A list of genes that are preferentially expressed in retina was obtained by integrating expressed sequence tag, SAGE and microarray datasets. The regulatory targets of retina-specific TFs are enriched in this set of retina-related genes. A Bayesian approach was employed to integrate information about binding site location relative to a gene's transcription start site. Our method was applied to three retina-specific TFs, CRX, NRL and NR2E3, and a number of potential targets were predicted. To experimentally assess the validity of the bioinformatic predictions, mobility shift, transient transfection and chromatin immunoprecipitation assays were performed with five predicted CRX targets, and the results were suggestive of CRX regulation in 5/5, 3/5 and 4/5 cases, respectively. Together, these experiments strongly suggest that RP1, GUCY2D, ABCA4 are novel targets of CRX. PMID:15967807

Estimating patient-specific soft-tissue properties in a TKA knee.

PubMed

Ewing, Joseph A; Kaufman, Michelle K; Hutter, Erin E; Granger, Jeffrey F; Beal, Matthew D; Piazza, Stephen J; Siston, Robert A

2016-03-01

Surgical technique is one factor that has been identified as critical to success of total knee arthroplasty. Researchers have shown that computer simulations can aid in determining how decisions in the operating room generally affect post-operative outcomes. However, to use simulations to make clinically relevant predictions about knee forces and motions for a specific total knee patient, patient-specific models are needed. This study introduces a methodology for estimating knee soft-tissue properties of an individual total knee patient. A custom surgical navigation system and stability device were used to measure the force-displacement relationship of the knee. Soft-tissue properties were estimated using a parameter optimization that matched simulated tibiofemoral kinematics with experimental tibiofemoral kinematics. Simulations using optimized ligament properties had an average root mean square error of 3.5° across all tests while simulations using generic ligament properties taken from literature had an average root mean square error of 8.4°. Specimens showed large variability among ligament properties regardless of similarities in prosthetic component alignment and measured knee laxity. These results demonstrate the importance of soft-tissue properties in determining knee stability, and suggest that to make clinically relevant predictions of post-operative knee motions and forces using computer simulations, patient-specific soft-tissue properties are needed. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Raising an Antibody Specific to Breast Cancer Subpopulations Using Phage Display on Tissue Sections.

PubMed

Larsen, Simon Asbjørn; Meldgaard, Theresa; Fridriksdottir, Agla Jael Rubner; Lykkemark, Simon; Poulsen, Pi Camilla; Overgaard, Laura Falkensteen; Petersen, Helene Bundgaard; Petersen, Ole William; Kristensen, Peter

2016-01-01

Primary tumors display a great level of intra-tumor heterogeneity in breast cancer. The current lack of prognostic and predictive biomarkers limits accurate stratification and the ability to predict response to therapy. The aim of the present study was to select recombinant antibody fragments specific against breast cancer subpopulations, aiding the discovery of novel biomarkers. Recombinant antibody fragments were selected by phage display. A novel shadowstick technology enabled the direct selection using tissue sections of antibody fragments specific against small subpopulations of breast cancer cells. Selections were performed against a subpopulation of breast cancer cells expressing CD271+, as these previously have been indicated to be potential breast cancer stem cells. The selected antibody fragments were screened by phage ELISA on both breast cancer and myoepithelial cells. The antibody fragments were validated and evaluated by immunohistochemistry experiments. Our study revealed an antibody fragment, LH8, specific for breast cancer cells. Immunohistochemistry results indicate that this particular antibody fragment binds an antigen that exhibits differential expression in different breast cancer subpopulations. Further studies characterizing this antibody fragment, the subpopulation it binds and the cognate antigen may unearth novel biomarkers of clinical relevance. Copyright© 2016, International Institute of Anticancer Research (Dr. John G. Delinasios), All rights reserved.

Tissue-Specific Analysis of Pharmacological Pathways.

PubMed

Hao, Yun; Quinnies, Kayla; Realubit, Ronald; Karan, Charles; Tatonetti, Nicholas P

2018-06-19

Understanding the downstream consequences of pharmacologically targeted proteins is essential to drug design. Current approaches investigate molecular effects under tissue-naïve assumptions. Many target proteins, however, have tissue-specific expression. A systematic study connecting drugs to target pathways in in vivo human tissues is needed. We introduced a data-driven method that integrates drug-target relationships with gene expression, protein-protein interaction, and pathway annotation data. We applied our method to four independent genomewide expression datasets and built 467,396 connections between 1,034 drugs and 954 pathways in 259 human tissues or cell lines. We validated our results using data from L1000 and Pharmacogenomics Knowledgebase (PharmGKB), and observed high precision and recall. We predicted and tested anticoagulant effects of 22 compounds experimentally that were previously unknown, and used clinical data to validate these effects retrospectively. Our systematic study provides a better understanding of the cellular response to drugs and can be applied to many research topics in systems pharmacology. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

PubMed Central

Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eospredicted 64% of sputum Neupredict both sputum Eos and Neu accurately assigned only 41% of samples. Conclusion Despite statistically significant associations FeNO, IgE, blood Eos and Neu, FEV1%predicted, and age are poor surrogates, separately and combined, for accurately predicting sputum eosinophils and neutrophils. PMID:23706399

Predictive analysis of thermal distribution and damage in thermotherapy on biological tissue

NASA Astrophysics Data System (ADS)

Fanjul-Vélez, Félix; Arce-Diego, José Luis

2007-05-01

The use of optical techniques is increasing the possibilities and success of medical praxis in certain cases, either in tissue characterization or treatment. Photodynamic therapy (PDT) or low intensity laser treatment (LILT) are two examples of the latter. Another very interesting implementation is thermotherapy, which consists of controlling temperature increase in a pathological biological tissue. With this method it is possible to provoke an improvement on specific diseases, but a previous analysis of treatment is needed in order for the patient not to suffer any collateral damage, an essential point due to security margins in medical procedures. In this work, a predictive analysis of thermal distribution in a biological tissue irradiated by an optical source is presented. Optical propagation is based on a RTT (Radiation Transport Theory) model solved via a numerical Monte Carlo method, in a multi-layered tissue. Data obtained are included in a bio-heat equation that models heat transference, taking into account conduction, convection, radiation, blood perfusion and vaporization depending on the specific problem. Spatial-temporal differential bio-heat equation is solved via a numerical finite difference approach. Experimental temperature distributions on animal tissue irradiated by laser radiation are shown. From thermal distribution in tissue, thermal damage is studied, based on an Arrhenius analysis, as a way of predicting harmful effects. The complete model can be used for concrete treatment proposals, as a way of predicting treatment effects and consequently decide which optical source parameters are appropriate for the specific disease, mainly wavelength and optical power, with reasonable security margins in the process.

Functional MRI registration with tissue-specific patch-based functional correlation tensors.

PubMed

Zhou, Yujia; Zhang, Han; Zhang, Lichi; Cao, Xiaohuan; Yang, Ru; Feng, Qianjin; Yap, Pew-Thian; Shen, Dinggang

2018-06-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods. © 2018 Wiley Periodicals, Inc.

Antibody specific epitope prediction-emergence of a new paradigm.

PubMed

Sela-Culang, Inbal; Ofran, Yanay; Peters, Bjoern

2015-04-01

The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data. Copyright © 2015 Elsevier B.V. All rights reserved.

Accurate prediction of energy expenditure using a shoe-based activity monitor.

PubMed

Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

2011-07-01

The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poulin, Patrick, E-mail: patrick-poulin@videotron.ca; Ekins, Sean; Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201

A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V{sub ss}) in humans under in vivo conditions. Thismore » correlation method demonstrated inaccurate predictions of V{sub ss} for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V{sub ss} of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.« less

Tissue-specific Proteogenomic Analysis of Plutella xylostella Larval Midgut Using a Multialgorithm Pipeline.

PubMed

Zhu, Xun; Xie, Shangbo; Armengaud, Jean; Xie, Wen; Guo, Zhaojiang; Kang, Shi; Wu, Qingjun; Wang, Shaoli; Xia, Jixing; He, Rongjun; Zhang, Youjun

2016-06-01

The diamondback moth, Plutella xylostella (L.), is the major cosmopolitan pest of brassica and other cruciferous crops. Its larval midgut is a dynamic tissue that interfaces with a wide variety of toxicological and physiological processes. The draft sequence of the P. xylostella genome was recently released, but its annotation remains challenging because of the low sequence coverage of this branch of life and the poor description of exon/intron splicing rules for these insects. Peptide sequencing by computational assignment of tandem mass spectra to genome sequence information provides an experimental independent approach for confirming or refuting protein predictions, a concept that has been termed proteogenomics. In this study, we carried out an in-depth proteogenomic analysis to complement genome annotation of P. xylostella larval midgut based on shotgun HPLC-ESI-MS/MS data by means of a multialgorithm pipeline. A total of 876,341 tandem mass spectra were searched against the predicted P. xylostella protein sequences and a whole-genome six-frame translation database. Based on a data set comprising 2694 novel genome search specific peptides, we discovered 439 novel protein-coding genes and corrected 128 existing gene models. To get the most accurate data to seed further insect genome annotation, more than half of the novel protein-coding genes, i.e. 235 over 439, were further validated after RT-PCR amplification and sequencing of the corresponding transcripts. Furthermore, we validated 53 novel alternative splicings. Finally, a total of 6764 proteins were identified, resulting in one of the most comprehensive proteogenomic study of a nonmodel animal. As the first tissue-specific proteogenomics analysis of P. xylostella, this study provides the fundamental basis for high-throughput proteomics and functional genomics approaches aimed at deciphering the molecular mechanisms of resistance and controlling this pest. © 2016 by The American Society for Biochemistry and

Tissue-specific Proteogenomic Analysis of Plutella xylostella Larval Midgut Using a Multialgorithm Pipeline*

PubMed Central

Zhu, Xun; Xie, Shangbo; Armengaud, Jean; Xie, Wen; Guo, Zhaojiang; Kang, Shi; Wu, Qingjun; Wang, Shaoli; Xia, Jixing; He, Rongjun; Zhang, Youjun

2016-01-01

The diamondback moth, Plutella xylostella (L.), is the major cosmopolitan pest of brassica and other cruciferous crops. Its larval midgut is a dynamic tissue that interfaces with a wide variety of toxicological and physiological processes. The draft sequence of the P. xylostella genome was recently released, but its annotation remains challenging because of the low sequence coverage of this branch of life and the poor description of exon/intron splicing rules for these insects. Peptide sequencing by computational assignment of tandem mass spectra to genome sequence information provides an experimental independent approach for confirming or refuting protein predictions, a concept that has been termed proteogenomics. In this study, we carried out an in-depth proteogenomic analysis to complement genome annotation of P. xylostella larval midgut based on shotgun HPLC-ESI-MS/MS data by means of a multialgorithm pipeline. A total of 876,341 tandem mass spectra were searched against the predicted P. xylostella protein sequences and a whole-genome six-frame translation database. Based on a data set comprising 2694 novel genome search specific peptides, we discovered 439 novel protein-coding genes and corrected 128 existing gene models. To get the most accurate data to seed further insect genome annotation, more than half of the novel protein-coding genes, i.e. 235 over 439, were further validated after RT-PCR amplification and sequencing of the corresponding transcripts. Furthermore, we validated 53 novel alternative splicings. Finally, a total of 6764 proteins were identified, resulting in one of the most comprehensive proteogenomic study of a nonmodel animal. As the first tissue-specific proteogenomics analysis of P. xylostella, this study provides the fundamental basis for high-throughput proteomics and functional genomics approaches aimed at deciphering the molecular mechanisms of resistance and controlling this pest. PMID:26902207

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

PubMed

Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

2014-07-01

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. © 2013 Wiley Periodicals, Inc.

Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

PubMed Central

Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

2009-01-01

The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

Accurate identification of fear facial expressions predicts prosocial behavior.

PubMed

Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

2007-05-01

The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

Accurate Binding Free Energy Predictions in Fragment Optimization.

PubMed

Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

2015-11-23

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory.

PubMed

Xue, Yu; Li, Ao; Wang, Lirong; Feng, Huanqing; Yao, Xuebiao

2006-03-20

As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated. In this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying. Taken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc.

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

PubMed Central

Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W. F.; Jeelani, Owase; Dunaway, David J.; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face. PMID:29742139

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

PubMed

Knoops, Paul G M; Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W F; Jeelani, Owase; Dunaway, David J; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

Can phenological models predict tree phenology accurately under climate change conditions?

NASA Astrophysics Data System (ADS)

Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

2014-05-01

The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

PubMed Central

Meyer, Andrew J.; Eskinazi, Ilan; Jackson, Jennifer N.; Rao, Anil V.; Patten, Carolynn; Fregly, Benjamin J.

2016-01-01

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot–ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the

External validation of a nomogram for prediction of side-specific extracapsular extension at robotic radical prostatectomy.

PubMed

Zorn, Kevin C; Gallina, Andrea; Hutterer, Georg C; Walz, Jochen; Shalhav, Arieh L; Zagaja, Gregory P; Valiquette, Luc; Gofrit, Ofer N; Orvieto, Marcelo A; Taxy, Jerome B; Karakiewicz, Pierre I

2007-11-01

Several staging tools have been developed for open radical prostatectomy (ORP) patients. However, the validity of these tools has never been formally tested in patients treated with robot-assisted laparoscopic radical prostatectomy (RALP). We tested the accuracy of an ORP-derived nomogram in predicting the rate of extracapsular extension (ECE) in a large RALP cohort. Serum prostate specific antigen (PSA) and side-specific clinical stage and biopsy Gleason sum information were used in a previously validated nomogram predicting side-specific ECE. The nomogram-derived predictions were compared with the observed rate of ECE, and the accuracy of the predictions was quantified. Each prostate lobe was analyzed independently. As complete data were available for 576 patients, the analyses targeted 1152 prostate lobes. Median age and serum PSA concentration at radical prostatectomy were 60 years and 5.4 ng/mL, respectively. The majority of side-specific clinical stages were T(1c) (993; 86.2%). Most side-specific biopsy Gleason sums were 6 (572; 49.7%). The median side-specific percentages of positive cores and of cancer were, respectively, 20.0% and 5.0%. At final pathologic review, 107 patients (18.6%) had ECE, and side-specific ECE was present in 117 patients (20.3%). The nomogram was 89% accurate in the RALP cohort v 84% in the previously reported ORP validation. The ORP side-specific ECE nomogram is highly accurate in the RALP population, suggesting that predictive and possibly prognostic tools developed in ORP patients may be equally accurate in their RALP counterparts.

The differentiation of oral soft- and hard tissues using laser induced breakdown spectroscopy - a prospect for tissue specific laser surgery.

PubMed

Rohde, Maximilian; Mehari, Fanuel; Klämpfl, Florian; Adler, Werner; Neukam, Friedrich-Wilhelm; Schmidt, Michael; Stelzle, Florian

2017-10-01

Compared to conventional techniques, Laser surgery procedures provide a number of advantages, but may be associated with an increased risk of iatrogenic damage to important anatomical structures. The type of tissue ablated in the focus spot is unknown. Laser-Induced Breakdown-Spectroscopy (LIBS) has the potential to gain information about the type of material that is being ablated by the laser beam. This may form the basis for tissue selective laser surgery. In the present study, 7 different porcine tissues (cortical and cancellous bone, nerve, mucosa, enamel, dentine and pulp) from 6 animals were analyzed for their qualitative and semiquantitative molecular composition using LIBS. The so gathered data was used to first differentiate between the soft- and hard-tissues using a Calcium-Carbon emission based classifier. The tissues were then further classified using emission-ratio based analysis, principal component analysis (PCA) and linear discriminant analysis (LDA). The relatively higher concentration of Calcium in the hard tissues allows for an accurate first differentiation of soft- and hard tissues (100% sensitivity and specificity). The ratio based statistical differentiation approach yields results in the range from 65% (enamel-dentine pair) to 100% (nerve-pulp, cancellous bone-dentine, cancellous bone-enamel pairs) sensitivity and specificity. Experimental LIBS measuring setup. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Alternative Polyadenylation Directs Tissue-Specific miRNA Targeting in Caenorhabditis elegans Somatic Tissues

PubMed Central

Blazie, Stephen M.; Geissel, Heather C.; Wilky, Henry; Joshi, Rajan; Newbern, Jason; Mangone, Marco

2017-01-01

mRNA expression dynamics promote and maintain the identity of somatic tissues in living organisms; however, their impact in post-transcriptional gene regulation in these processes is not fully understood. Here, we applied the PAT-Seq approach to systematically isolate, sequence, and map tissue-specific mRNA from five highly studied Caenorhabditis elegans somatic tissues: GABAergic and NMDA neurons, arcade and intestinal valve cells, seam cells, and hypodermal tissues, and studied their mRNA expression dynamics. The integration of these datasets with previously profiled transcriptomes of intestine, pharynx, and body muscle tissues, precisely assigns tissue-specific expression dynamics for 60% of all annotated C. elegans protein-coding genes, providing an important resource for the scientific community. The mapping of 15,956 unique high-quality tissue-specific polyA sites in all eight somatic tissues reveals extensive tissue-specific 3′untranslated region (3′UTR) isoform switching through alternative polyadenylation (APA) . Almost all ubiquitously transcribed genes use APA and harbor miRNA targets in their 3′UTRs, which are commonly lost in a tissue-specific manner, suggesting widespread usage of post-transcriptional gene regulation modulated through APA to fine tune tissue-specific protein expression. Within this pool, the human disease gene C. elegans orthologs rack-1 and tct-1 use APA to switch to shorter 3′UTR isoforms in order to evade miRNA regulation in the body muscle tissue, resulting in increased protein expression needed for proper body muscle function. Our results highlight a major positive regulatory role for APA, allowing genes to counteract miRNA regulation on a tissue-specific basis. PMID:28348061

Alternative Polyadenylation Directs Tissue-Specific miRNA Targeting in Caenorhabditis elegans Somatic Tissues.

PubMed

Blazie, Stephen M; Geissel, Heather C; Wilky, Henry; Joshi, Rajan; Newbern, Jason; Mangone, Marco

2017-06-01

mRNA expression dynamics promote and maintain the identity of somatic tissues in living organisms; however, their impact in post-transcriptional gene regulation in these processes is not fully understood. Here, we applied the PAT-Seq approach to systematically isolate, sequence, and map tissue-specific mRNA from five highly studied Caenorhabditis elegans somatic tissues: GABAergic and NMDA neurons, arcade and intestinal valve cells, seam cells, and hypodermal tissues, and studied their mRNA expression dynamics. The integration of these datasets with previously profiled transcriptomes of intestine, pharynx, and body muscle tissues, precisely assigns tissue-specific expression dynamics for 60% of all annotated C. elegans protein-coding genes, providing an important resource for the scientific community. The mapping of 15,956 unique high-quality tissue-specific polyA sites in all eight somatic tissues reveals extensive tissue-specific 3'untranslated region (3'UTR) isoform switching through alternative polyadenylation (APA) . Almost all ubiquitously transcribed genes use APA and harbor miRNA targets in their 3'UTRs, which are commonly lost in a tissue-specific manner, suggesting widespread usage of post-transcriptional gene regulation modulated through APA to fine tune tissue-specific protein expression. Within this pool, the human disease gene C. elegans orthologs rack-1 and tct-1 use APA to switch to shorter 3'UTR isoforms in order to evade miRNA regulation in the body muscle tissue, resulting in increased protein expression needed for proper body muscle function. Our results highlight a major positive regulatory role for APA, allowing genes to counteract miRNA regulation on a tissue-specific basis. Copyright © 2017 Blazie et al.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

PubMed

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

PubMed

Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

2016-11-01

ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

Reliability of upper and lower extremity anthropometric measurements and the effect on tissue mass predictions.

PubMed

Burkhart, Timothy A; Arthurs, Katherine L; Andrews, David M

2008-01-01

Accurate modeling of soft tissue motion effects relative to bone during impact requires knowledge of the mass of soft and rigid tissues in living people. Holmes et al., [2005. Predicting in vivo soft tissue masses of the lower extremity using segment anthropometric measures and DXA. Journal of Applied Biomechanics, 21, 371-382] developed and validated regression equations to predict the individual tissue masses of lower extremity segments of young healthy adults, based on simple anthropometric measurements. However, the reliability of these measurements and the effect on predicted tissue mass estimates from the equations has yet to be determined. In the current study, two measurers were responsible for collecting two sets of unilateral measurements (25 male and 25 female subjects) for the right upper and lower extremities. These included 6 lengths, 6 circumferences, 8 breadths, and 4 skinfold thicknesses. Significant differences were found between measurers and between sexes, but these differences were relatively small in general (75-80% of between-measurer differences were <1cm). Within-measurer measurement differences were smaller and more consistent than those between measurers in most cases. Good to excellent reliability was demonstrated for all measurement types, with intra-class correlation coefficients of 0.79, 0.86, 0.85 and 0.86 for lengths, circumferences, breadth and skinfolds, respectively. Predicted tissue mass magnitudes were moderately affected by the measurement differences. The maximum mean errors between measurers ranged from 3.2% to 24.2% for bone mineral content and fat mass, for the leg and foot, and the leg segments, respectively.

Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions

NASA Astrophysics Data System (ADS)

Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian W.; Meiler, Jens

2013-12-01

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with R osetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several R osetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

Tissue-specific mRNA expression profiling in grape berry tissues

PubMed Central

Grimplet, Jerome; Deluc, Laurent G; Tillett, Richard L; Wheatley, Matthew D; Schlauch, Karen A; Cramer, Grant R; Cushman, John C

2007-01-01

Background Berries of grape (Vitis vinifera) contain three major tissue types (skin, pulp and seed) all of which contribute to the aroma, color, and flavor characters of wine. The pericarp, which is composed of the exocarp (skin) and mesocarp (pulp), not only functions to protect and feed the developing seed, but also to assist in the dispersal of the mature seed by avian and mammalian vectors. The skin provides volatile and nonvolatile aroma and color compounds, the pulp contributes organic acids and sugars, and the seeds provide condensed tannins, all of which are important to the formation of organoleptic characteristics of wine. In order to understand the transcriptional network responsible for controlling tissue-specific mRNA expression patterns, mRNA expression profiling was conducted on each tissue of mature berries of V. vinifera Cabernet Sauvignon using the Affymetrix GeneChip® Vitis oligonucleotide microarray ver. 1.0. In order to monitor the influence of water-deficit stress on tissue-specific expression patterns, mRNA expression profiles were also compared from mature berries harvested from vines subjected to well-watered or water-deficit conditions. Results Overall, berry tissues were found to express approximately 76% of genes represented on the Vitis microarray. Approximately 60% of these genes exhibited significant differential expression in one or more of the three major tissue types with more than 28% of genes showing pronounced (2-fold or greater) differences in mRNA expression. The largest difference in tissue-specific expression was observed between the seed and pulp/skin. Exocarp tissue, which is involved in pathogen defense and pigment production, showed higher mRNA abundance relative to other berry tissues for genes involved with flavonoid biosynthesis, pathogen resistance, and cell wall modification. Mesocarp tissue, which is considered a nutritive tissue, exhibited a higher mRNA abundance of genes involved in cell wall function and

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

PubMed Central

Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao; Yuan, Peng; Liu, Siting; Tang, Zhen; Zhang, Guangming; Zhou, Xiaobo; Gateno, Jaime

2017-01-01

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians’ need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation.

PubMed

Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao; Yuan, Peng; Liu, Siting; Tang, Zhen; Zhang, Guangming; Zhou, Xiaobo; Gateno, Jaime; Liebschner, Michael A K; Xia, James J

2018-04-01

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical

Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

PubMed

Kim, Scott Y H

2014-04-01

The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

Predicting survival across chronic interstitial lung disease: the ILD-GAP model.

PubMed

Ryerson, Christopher J; Vittinghoff, Eric; Ley, Brett; Lee, Joyce S; Mooney, Joshua J; Jones, Kirk D; Elicker, Brett M; Wolters, Paul J; Koth, Laura L; King, Talmadge E; Collard, Harold R

2014-04-01

Risk prediction is challenging in chronic interstitial lung disease (ILD) because of heterogeneity in disease-specific and patient-specific variables. Our objective was to determine whether mortality is accurately predicted in patients with chronic ILD using the GAP model, a clinical prediction model based on sex, age, and lung physiology, that was previously validated in patients with idiopathic pulmonary fibrosis. Patients with idiopathic pulmonary fibrosis (n=307), chronic hypersensitivity pneumonitis (n=206), connective tissue disease-associated ILD (n=281), idiopathic nonspecific interstitial pneumonia (n=45), or unclassifiable ILD (n=173) were selected from an ongoing database (N=1,012). Performance of the previously validated GAP model was compared with novel prediction models in each ILD subtype and the combined cohort. Patients with follow-up pulmonary function data were used for longitudinal model validation. The GAP model had good performance in all ILD subtypes (c-index, 74.6 in the combined cohort), which was maintained at all stages of disease severity and during follow-up evaluation. The GAP model had similar performance compared with alternative prediction models. A modified ILD-GAP Index was developed for application across all ILD subtypes to provide disease-specific survival estimates using a single risk prediction model. This was done by adding a disease subtype variable that accounted for better adjusted survival in connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia. The GAP model accurately predicts risk of death in chronic ILD. The ILD-GAP model accurately predicts mortality in major chronic ILD subtypes and at all stages of disease.

Predicting readmission risk with institution-specific prediction models.

PubMed

Yu, Shipeng; Farooq, Faisal; van Esbroeck, Alexander; Fung, Glenn; Anand, Vikram; Krishnapuram, Balaji

2015-10-01

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. We propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We have experimented with classification methods such as support vector machines, and prognosis methods such as the Cox regression. We compared our methods with industry-standard methods such as the LACE model, and showed the proposed framework is not only more flexible but also more effective. We applied our framework to patient data from three hospitals, and obtained some initial results for heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN) patients as well as patients with all conditions. On Hospital 2, the LACE model yielded AUC 0.57, 0.56, 0.53 and 0.55 for AMI, HF, PN and All Cause readmission prediction, respectively, while the proposed model yielded 0.66, 0.65, 0.63, 0.74 for the corresponding conditions, all significantly better than the LACE counterpart. The proposed models that leverage all features at discharge time is more accurate than the models that only leverage features at admission time (0.66 vs. 0.61 for AMI, 0.65 vs. 0.61 for HF, 0.63 vs. 0.56 for PN, 0.74 vs. 0.60 for All

Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

PubMed Central

Li, Dapeng; Heiling, Sven; Baldwin, Ian T.

2016-01-01

Secondary metabolite diversity is considered an important fitness determinant for plants’ biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue–metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function. PMID:27821729

Development of a 3D patient-specific planning platform for interstitial and transurethral ultrasound thermal therapy

NASA Astrophysics Data System (ADS)

Prakash, Punit; Diederich, Chris J.

2010-03-01

Interstitial and transurethral catheter-based ultrasound devices are under development for treatment of prostate cancer and BPH, uterine fibroids, liver tumors and other soft tissue disease. Accurate 3D thermal modeling is essential for designing site-specific applicators, exploring treatment delivery strategies, and integration of patient-specific treatment planning of thermal ablations. We are developing a comprehensive 3D modeling and treatment planning platform for ultrasound ablation of tissue using catheter-based applicators. We explored the applicability of assessing thermal effects in tissue using critical temperature, thermal dose and Arrhenius thermal damage thresholds and performed a comparative analysis of dynamic tissue properties critical to accurate modeling. We used the model to assess the feasibility of automatic feedback control with MR thermometry, and demonstrated the utility of the modeling platform for 3D patient-specific treatment planning. We have identified critical temperature, thermal dose and thermal damage thresholds for assessing treatment endpoint. Dynamic changes in tissue attenuation/absorption and perfusion must be included for accurate prediction of temperature profiles and extents of the ablation zone. Lastly, we demonstrated use of the modeling platform for patient-specific treatment planning.

Tissue-specific effects of peptides.

PubMed

Khavinson, V K

2001-08-01

Synthetic peptides (cytogens) Cortagen, Epithalon, Livagen, and Vilon stimulated the growth of explants from rat brain cortex, subcortical structures, liver, and thymus, respectively, in organotypic cultures. These peptides produced tissue-specific effects: they stimulated the growth of explants from tissues, whose cytomedins (peptide complexes) were used for chemical synthesis.

Tissue-Specific Regulation of Chromatin Insulator Function

PubMed Central

Matzat, Leah H.; Dale, Ryan K.; Moshkovich, Nellie; Lei, Elissa P.

2012-01-01

Chromatin insulators organize the genome into distinct transcriptional domains and contribute to cell type–specific chromatin organization. However, factors regulating tissue-specific insulator function have not yet been discovered. Here we identify the RNA recognition motif-containing protein Shep as a direct interactor of two individual components of the gypsy insulator complex in Drosophila. Mutation of shep improves gypsy-dependent enhancer blocking, indicating a role as a negative regulator of insulator activity. Unlike ubiquitously expressed core gypsy insulator proteins, Shep is highly expressed in the central nervous system (CNS) with lower expression in other tissues. We developed a novel, quantitative tissue-specific barrier assay to demonstrate that Shep functions as a negative regulator of insulator activity in the CNS but not in muscle tissue. Additionally, mutation of shep alters insulator complex nuclear localization in the CNS but has no effect in other tissues. Consistent with negative regulatory activity, ChIP–seq analysis of Shep in a CNS-derived cell line indicates substantial genome-wide colocalization with a single gypsy insulator component but limited overlap with intact insulator complexes. Taken together, these data reveal a novel, tissue-specific mode of regulation of a chromatin insulator. PMID:23209434

[Tissue-specific nucleoprotein complexes].

PubMed

Riadnova, I Iu; Shataeva, L K; Khavinson, V Kh

2000-01-01

A method of isolation of native nucleorprotein complexes from cattle cerebral cortex, thymus, and liver was developed. Compositions of these complexes were studied by means of gel-chromatography and ion-exchange chromatography. These preparations were shown to consist of several fractions of proteins and their complexes differ by molecular mass and electro-chemical properties. Native nucleoprotein complexes revealed high tissue specific activity, which was not species-specific.

Real time cancer prediction based on objective tissue compliance measurement in endoscopic surgery.

PubMed

Fakhry, Morkos; Bello, Fernando; Hanna, George B

2014-02-01

To investigate the feasibility of real time cancer tissue diagnosis intraoperatively based on in vivo tissue compliance measurements obtained by a recently developed laparoscopic smart device. Cancer tissue is stiffer than its normal counterpart. Modern forms of remote surgery such as laparoscopic and robotic surgical techniques diminish direct assessment of this important tissue property. In vivo human tissue compliance of the normal and cancer gastrointestinal tissue is unknown. A Clinical Real Time Tissue Compliance Mapping System (CRTCMS) with a predictive power comparable to the human hand and useable in routine surgical practice has been recently developed. The CRTCMS is employed in the operating theater to collect data from 50 patients undergoing intra-abdominal surgical interventions [40 men, 10 women, aged between 32 and 89 (mean = 66.4, range = 57)]. This includes 10 esophageal and 27 gastric cancer patients. A total of 1212 compliance measurements of normal and cancerous in vivo gastrointestinal tissues were taken. The data were used to calibrate the CRTCMS to predict cancerous tissue in a further 12 patients (3 cancer esophagus and 9 cancer stomach) involving 175 measurements. The system demonstrated a high prediction power to diagnose cancer tissue in real time during routine surgical procedures (sensitivity = 98.7%, specificity = 99%). An in vivo human tissue compliance data bank of the gastrointestinal tract was produced. Real time cancer diagnosis based on in vivo tissue compliance measurements is feasible. The reported data open new avenues in cancer diagnostics, surgical robotics, and development of more realistic surgical simulators.

Three-dimensional virtual planning in orthognathic surgery enhances the accuracy of soft tissue prediction.

PubMed

Van Hemelen, Geert; Van Genechten, Maarten; Renier, Lieven; Desmedt, Maria; Verbruggen, Elric; Nadjmi, Nasser

2015-07-01

Throughout the history of computing, shortening the gap between the physical and digital world behind the screen has always been strived for. Recent advances in three-dimensional (3D) virtual surgery programs have reduced this gap significantly. Although 3D assisted surgery is now widely available for orthognathic surgery, one might still argue whether a 3D virtual planning approach is a better alternative to a conventional two-dimensional (2D) planning technique. The purpose of this study was to compare the accuracy of a traditional 2D technique and a 3D computer-aided prediction method. A double blind randomised prospective study was performed to compare the prediction accuracy of a traditional 2D planning technique versus a 3D computer-aided planning approach. The accuracy of the hard and soft tissue profile predictions using both planning methods was investigated. There was a statistically significant difference between 2D and 3D soft tissue planning (p < 0.05). The statistically significant difference found between 2D and 3D planning and the actual soft tissue outcome was not confirmed by a statistically significant difference between methods. The 3D planning approach provides more accurate soft tissue planning. However, the 2D orthognathic planning is comparable to 3D planning when it comes to hard tissue planning. This study provides relevant results for choosing between 3D and 2D planning in clinical practice. Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Computation and application of tissue-specific gene set weights.

PubMed

Frost, H Robert

2018-04-06

Gene set testing, or pathway analysis, has become a critical tool for the analysis of highdimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed in a tissue agnostic fashion, which impacts statistical power and the interpretation and replication of results. To address this challenge, we have developed a bioinformatics approach to compute tissuespecific weights for individual gene sets using information on tissue-specific gene activity from the Human Protein Atlas (HPA). We used this approach to create a public repository of tissue-specific gene set weights for 37 different human tissue types from the HPA and all collections in the Molecular Signatures Database (MSigDB). To demonstrate the validity and utility of these weights, we explored three different applications: the functional characterization of human tissues, multi-tissue analysis for systemic diseases and tissue-specific gene set testing. All data used in the reported analyses is publicly available. An R implementation of the method and tissue-specific weights for MSigDB gene set collections can be downloaded at http://www.dartmouth.edu/∼hrfrost/TissueSpecificGeneSets. rob.frost@dartmouth.edu.

Sex- and Tissue-specific Functions of Drosophila Doublesex Transcription Factor Target Genes

PubMed Central

Clough, Emily; Jimenez, Erin; Kim, Yoo-Ah; Whitworth, Cale; Neville, Megan C.; Hempel, Leonie; Pavlou, Hania J.; Chen, Zhen-Xia; Sturgill, David; Dale, Ryan; Smith, Harold E.; Przytycka, Teresa M.; Goodwin, Stephen F.; Van Doren, Mark; Oliver, Brian

2014-01-01

Primary sex determination “switches” evolve rapidly, but Doublesex (DSX) related transcription factors (DMRTs) act downstream of these switches to control sexual development in most animal species. Drosophila dsx encodes female- and male-specific isoforms (DSXF and DSXM), but little is known about how dsx controls sexual development, whether DSXF and DSXM bind different targets, or how DSX proteins direct different outcomes in diverse tissues. We undertook genome-wide analyses to identify DSX targets using in vivo occupancy, binding site prediction, and evolutionary conservation. We find that DSXF and DSXM bind thousands of the same targets in multiple tissues in both sexes, yet these targets have sex- and tissue-specific functions. Interestingly, DSX targets show considerable overlap with targets identified for mouse DMRT1. DSX targets include transcription factors and signaling pathway components providing for direct and indirect regulation of sex-biased expression. PMID:25535918

Prediction of water loss and viscoelastic deformation of apple tissue using a multiscale model.

PubMed

Aregawi, Wondwosen A; Abera, Metadel K; Fanta, Solomon W; Verboven, Pieter; Nicolai, Bart

2014-11-19

A two-dimensional multiscale water transport and mechanical model was developed to predict the water loss and deformation of apple tissue (Malus × domestica Borkh. cv. 'Jonagold') during dehydration. At the macroscopic level, a continuum approach was used to construct a coupled water transport and mechanical model. Water transport in the tissue was simulated using a phenomenological approach using Fick's second law of diffusion. Mechanical deformation due to shrinkage was based on a structural mechanics model consisting of two parts: Yeoh strain energy functions to account for non-linearity and Maxwell's rheological model of visco-elasticity. Apparent parameters of the macroscale model were computed from a microscale model. The latter accounted for water exchange between different microscopic structures of the tissue (intercellular space, the cell wall network and cytoplasm) using transport laws with the water potential as the driving force for water exchange between different compartments of tissue. The microscale deformation mechanics were computed using a model where the cells were represented as a closed thin walled structure. The predicted apparent water transport properties of apple cortex tissue from the microscale model showed good agreement with the experimentally measured values. Deviations between calculated and measured mechanical properties of apple tissue were observed at strains larger than 3%, and were attributed to differences in water transport behavior between the experimental compression tests and the simulated dehydration-deformation behavior. Tissue dehydration and deformation in the high relative humidity range ( > 97% RH) could, however, be accurately predicted by the multiscale model. The multiscale model helped to understand the dynamics of the dehydration process and the importance of the different microstructural compartments (intercellular space, cell wall, membrane and cytoplasm) for water transport and mechanical deformation.

Prediction of specific damage or infarction from the measurement of tissue impedance following repetitive brain ischaemia in the rat.

PubMed

Klein, H C; Krop-Van Gastel, W; Go, K G; Korf, J

1993-02-01

The development of irreversible brain damage during repetitive periods of hypoxia and normoxia was studied in anaesthetized rats with unilateral occlusion of the carotid artery (modified Levine model). Rats were exposed to 10 min hypoxia and normoxia until severe damage developed. As indices of damage, whole striatal tissue impedance (reflecting cellular water uptake), sodium/potassium contents (due to exchange with blood). Evans Blue staining (blood-brain barrier [BBB] integrity) and silver staining (increased in irreversibly damaged neurons) were used. A substantial decrease in blood pressure was observed during the hypoxic periods possibly producing severe ischaemia. Irreversibly increased impedance, massive changes in silver staining, accumulation of whole tissue Na and loss of K occurred only after a minimum of two periods of hypoxia, but there was no disruption of the BBB. Microscopic examination of tissue sections revealed that cell death was selective with reversible impedance changes, but became massive and non-specific after irreversible increase of the impedance. The development of brain infarcts could, however, not be predicted from measurements of physiological parameters in the blood. We suggest that the development of cerebral infarction during repetitive periods of hypoxia may serve as a model for the development of brain damage in a variety of clinical conditions. Furthermore, the present model allows the screening of potential therapeutic measuring of the prevention and treatment of both infarction and selective cell death.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury.

PubMed

Myers, Risa B; Lazaridis, Christos; Jermaine, Christopher M; Robertson, Claudia S; Rusin, Craig G

2016-09-01

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury. A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The neurosurgical unit of Ben Taub Hospital (Houston, TX). Our cohort consisted of 817 subjects with severe traumatic brain injury. Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91. Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

Predicting normal tissue radiosensitivity

NASA Astrophysics Data System (ADS)

Dickson, Jeanette

Two methods of predicting normal cell radiosensitivity were investigated in different patient groups. Plasma transforming growth factor beta one (TGFbeta1) levels were measured by ELISA, using a commercially available kit. Residual DNA double strand breaks were measured in normal epidermal fibroblasts following 150 Gy. After allowing 24 hours for repair, the DNA damage was assayed using pulsed field gel electrophoresis (PFGE). Pretreatment plasma TGFbeta1 levels were investigated retrospectively in patients with carcinoma of the cervix in relation to tumour control and late morbidity following radiotherapy. Plasma TGFbeta1 levels increased with increasing disease stage. They also correlated with two other known measures of tumour burden i.e. plasma levels of carcinoma antigen 125 (CA125) and tissue polypeptide antigen (TPA). Elevated pretreatment plasma TGFbeta1 levels predicted for a poor outcome both in terms of local control and overall survival. Plasma TGF?l levels did not predict for the development of radiotherapy morbidity of any grade. In conclusion pre-treatment plasma TGFbeta1 levels predict for tumour burden and tumour outcome in patients with carcinoma of the cervix. Changes in plasma TGFbeta1 levels measured prospectively may predict for radiation morbidity and should be investigated. A prospective study was undertaken in patients with carcinoma of the head and neck region. Changes in plasma TGFbeta1 levels between the start and the end of a course of radical radiotherapy were investigated in relation to the development of acute radiation toxicity. Patients were categorised according to the pattern of response of their TGFbeta1 levels over the course of their treatment. Those patients whose TGFbeta1 levels decreased, but did not normalise during radiotherapy were assigned to category 2. Category 2 predicted for a severe acute reaction, as measured using the LENT SOMA score, with a sensitivity of 33% and a specificity of 100%. The positive predictive

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

PubMed

Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

2015-11-01

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

Predictive analysis of optical ablation in several dermatological tumoral tissues

NASA Astrophysics Data System (ADS)

Fanjul-Vélez, F.; Blanco-Gutiérrez, A.; Salas-García, I.; Ortega-Quijano, N.; Arce-Diego, J. L.

2013-06-01

Optical techniques for treatment and characterization of biological tissues are revolutionizing several branches of medical praxis, for example in ophthalmology or dermatology. The non-invasive, non-contact and non-ionizing character of optical radiation makes it specially suitable for these applications. Optical radiation can be employed in medical ablation applications, either for tissue resection or surgery. Optical ablation may provide a controlled and clean cut on a biological tissue. This is particularly relevant in tumoral tissue resection, where a small amount of cancerous cells could make the tumor appear again. A very important aspect of tissue optical ablation is then the estimation of the affected volume. In this work we propose a complete predictive model of tissue ablation that provides an estimation of the resected volume. The model is based on a Monte Carlo approach for the optical propagation of radiation inside the tissue, and a blow-off model for tissue ablation. This model is applied to several types of dermatological tumoral tissues, specifically squamous cells, basocellular and infiltrative carcinomas. The parameters of the optical source are varied and the estimated resected volume is calculated. The results for the different tumor types are presented and compared. This model can be used for surgical planning, in order to assure the complete resection of the tumoral tissue.

Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

PubMed

Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

2018-05-09

Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

PubMed

Jambusaria, Ankit; Klomp, Jeff; Hong, Zhigang; Rafii, Shahin; Dai, Yang; Malik, Asrar B; Rehman, Jalees

2018-06-07

The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

ROKU: a novel method for identification of tissue-specific genes

PubMed Central

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-01-01

Background One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. Results We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. Conclusion ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. PMID:16764735

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

NASA Astrophysics Data System (ADS)

Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

How accurate is our clinical prediction of "minimal prostate cancer"?

PubMed

Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

2013-07-01

Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

Accurate and robust genomic prediction of celiac disease using statistical learning.

PubMed

Abraham, Gad; Tye-Din, Jason A; Bhalala, Oneil G; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

2014-02-01

Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

NASA Astrophysics Data System (ADS)

Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

2018-02-01

Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

Artificial neural network prediction of ischemic tissue fate in acute stroke imaging

PubMed Central

Huang, Shiliang; Shen, Qiang; Duong, Timothy Q

2010-01-01

Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis. PMID:20424631

Profound Tissue Specificity in Proliferation Control Underlies Cancer Drivers and Aneuploidy Patterns.

PubMed

Sack, Laura Magill; Davoli, Teresa; Li, Mamie Z; Li, Yuyang; Xu, Qikai; Naxerova, Kamila; Wooten, Eric C; Bernardi, Ronald J; Martin, Timothy D; Chen, Ting; Leng, Yumei; Liang, Anthony C; Scorsone, Kathleen A; Westbrook, Thomas F; Wong, Kwok-Kin; Elledge, Stephen J

2018-04-05

Genomics has provided a detailed structural description of the cancer genome. Identifying oncogenic drivers that work primarily through dosage changes is a current challenge. Unrestrained proliferation is a critical hallmark of cancer. We constructed modular, barcoded libraries of human open reading frames (ORFs) and performed screens for proliferation regulators in multiple cell types. Approximately 10% of genes regulate proliferation, with most performing in an unexpectedly highly tissue-specific manner. Proliferation drivers in a given cell type showed specific enrichment in somatic copy number changes (SCNAs) from cognate tumors and helped predict aneuploidy patterns in those tumors, implying that tissue-type-specific genetic network architectures underlie SCNA and driver selection in different cancers. In vivo screening confirmed these results. We report a substantial contribution to the catalog of SCNA-associated cancer drivers, identifying 147 amplified and 107 deleted genes as potential drivers, and derive insights about the genetic network architecture of aneuploidy in tumors. Copyright © 2018 Elsevier Inc. All rights reserved.

Using a combined computational-experimental approach to predict antibody-specific B cell epitopes.

PubMed

Sela-Culang, Inbal; Benhnia, Mohammed Rafii-El-Idrissi; Matho, Michael H; Kaever, Thomas; Maybeno, Matt; Schlossman, Andrew; Nimrod, Guy; Li, Sheng; Xiang, Yan; Zajonc, Dirk; Crotty, Shane; Ofran, Yanay; Peters, Bjoern

2014-04-08

Antibody epitope mapping is crucial for understanding B cell-mediated immunity and required for characterizing therapeutic antibodies. In contrast to T cell epitope mapping, no computational tools are in widespread use for prediction of B cell epitopes. Here, we show that, utilizing the sequence of an antibody, it is possible to identify discontinuous epitopes on its cognate antigen. The predictions are based on residue-pairing preferences and other interface characteristics. We combined these antibody-specific predictions with results of cross-blocking experiments that identify groups of antibodies with overlapping epitopes to improve the predictions. We validate the high performance of this approach by mapping the epitopes of a set of antibodies against the previously uncharacterized D8 antigen, using complementary techniques to reduce method-specific biases (X-ray crystallography, peptide ELISA, deuterium exchange, and site-directed mutagenesis). These results suggest that antibody-specific computational predictions and simple cross-blocking experiments allow for accurate prediction of residues in conformational B cell epitopes. Copyright © 2014 Elsevier Ltd. All rights reserved.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of

HdhQ111 Mice Exhibit Tissue Specific Metabolite Profiles that Include Striatal Lipid Accumulation

PubMed Central

Carroll, Jeffrey B.; Deik, Amy; Fossale, Elisa; Weston, Rory M.; Guide, Jolene R.; Arjomand, Jamshid; Kwak, Seung; Clish, Clary B.; MacDonald, Marcy E.

2015-01-01

The HTT CAG expansion mutation causes Huntington’s Disease and is associated with a wide range of cellular consequences, including altered metabolism. The mutant allele is expressed widely, in all tissues, but the striatum and cortex are especially vulnerable to its effects. To more fully understand this tissue-specificity, early in the disease process, we asked whether the metabolic impact of the mutant CAG expanded allele in heterozygous B6.HdhQ111/+ mice would be common across tissues, or whether tissues would have tissue-specific responses and whether such changes may be affected by diet. Specifically, we cross-sectionally examined steady state metabolite concentrations from a range of tissues (plasma, brown adipose tissue, cerebellum, striatum, liver, white adipose tissue), using an established liquid chromatography-mass spectrometry pipeline, from cohorts of 8 month old mutant and wild-type littermate mice that were fed one of two different high-fat diets. The differential response to diet highlighted a proportion of metabolites in all tissues, ranging from 3% (7/219) in the striatum to 12% (25/212) in white adipose tissue. By contrast, the mutant CAG-expanded allele primarily affected brain metabolites, with 14% (30/219) of metabolites significantly altered, compared to wild-type, in striatum and 11% (25/224) in the cerebellum. In general, diet and the CAG-expanded allele both elicited metabolite changes that were predominantly tissue-specific and non-overlapping, with evidence for mutation-by-diet interaction in peripheral tissues most affected by diet. Machine-learning approaches highlighted the accumulation of diverse lipid species as the most genotype-predictive metabolite changes in the striatum. Validation experiments in cell culture demonstrated that lipid accumulation was also a defining feature of mutant HdhQ111 striatal progenitor cells. Thus, metabolite-level responses to the CAG expansion mutation in vivo were tissue specific and most evident

Accuracy of three-dimensional soft tissue prediction for Le Fort I osteotomy using Dolphin 3D software: a pilot study.

PubMed

Resnick, C M; Dang, R R; Glick, S J; Padwa, B L

2017-03-01

Three-dimensional (3D) soft tissue prediction is replacing two-dimensional analysis in planning for orthognathic surgery. The accuracy of different computational models to predict soft tissue changes in 3D, however, is unclear. A retrospective pilot study was implemented to assess the accuracy of Dolphin 3D software in making these predictions. Seven patients who had a single-segment Le Fort I osteotomy and had preoperative (T 0 ) and >6-month postoperative (T 1 ) cone beam computed tomography (CBCT) scans and 3D photographs were included. The actual skeletal change was determined by subtracting the T 0 from the T 1 CBCT. 3D photographs were overlaid onto the T 0 CBCT and virtual skeletal movements equivalent to the achieved repositioning were applied using Dolphin 3D planner. A 3D soft tissue prediction (T P ) was generated and differences between the T P and T 1 images (error) were measured at 14 points and at the nasolabial angle. A mean linear prediction error of 2.91±2.16mm was found. The mean error at the nasolabial angle was 8.1±5.6°. In conclusion, the ability to accurately predict 3D soft tissue changes after Le Fort I osteotomy using Dolphin 3D software is limited. Copyright © 2016 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Establishment of a tissue-specific RNAi system in C. elegans.

PubMed

Qadota, Hiroshi; Inoue, Makiko; Hikita, Takao; Köppen, Mathias; Hardin, Jeffrey D; Amano, Mutsuki; Moerman, Donald G; Kaibuchi, Kozo

2007-10-01

In C. elegans, mosaic analysis is a powerful genetic tool for determining in which tissue or specific cells a gene of interest is required. For traditional mosaic analysis, a loss-of-function mutant and a genomic fragment that can rescue the mutant phenotype are required. Here we establish an easy and rapid mosaic system using RNAi (RNA mediated interference), using a rde-1 mutant that is resistant to RNAi. Tissue-specific expression of the wild type rde-1 cDNA in rde-1 mutants limits RNAi sensitivity to a specific tissue. We established hypodermal-and muscle-specific RNAi systems by expressing rde-1 cDNA under the control of the lin-26 and hlh-1 promoters, respectively. We confirmed tissue-specific RNAi using two assays: (1) tissue-specific knockdown of GFP expression, and (2) phenocopy of mutations in essential genes that were previously known to function in a tissue-specific manner. We also applied this system to an essential gene, ajm-1, expressed in hypodermis and gut, and show that lethality in ajm-1 mutants is due to loss of expression in hypodermal cells. Although we demonstrate tissue-specific RNAi in hypodermis and muscle, this method could be easily applied to other tissues.

Establishment of a tissue-specific RNAi system in C. elegans

PubMed Central

Qadota, Hiroshi; Inoue, Makiko; Hikita, Takao; Köppen, Mathias; Hardin, Jeffrey D.; Amano, Mutsuki; Moerman, Donald G.; Kaibuchi, Kozo

2011-01-01

In C. elegans, mosaic analysis is a powerful genetic tool for determining in which tissue or specific cells a gene of interest is required. For traditional mosaic analysis, a loss-of-function mutant and a genomic fragment that can rescue the mutant phenotype are required. Here we establish an easy and rapid mosaic system using RNAi (RNA mediated interference), using a rde-1 mutant that is resistant to RNAi. Tissue-specific expression of the wild type rde-1 cDNA in rde-1 mutants limits RNAi sensitivity to a specific tissue. We established hypodermal- and muscle-specific RNAi systems by expressing rde-1 cDNA under the control of the lin-26 and hlh-1 promoters, respectively. We confirmed tissue-specific RNAi using two assays: (1) tissue-specific knockdown of GFP expression, and (2) phenocopy of mutations in essential genes that were previously known to function in a tissue-specific manner. We also applied this system to an essential gene, ajm-1, expressed in hypodermis and gut, and show that lethality in ajm-1 mutants is due to loss of expression in hypodermal cells. Although we demonstrate tissue-specific RNAi in hypodermis and muscle, this method could be easily applied to other tissues. PMID:17681718

Accurate tissue characterization in low-dose CT imaging with pure iterative reconstruction.

PubMed

Murphy, Kevin P; McLaughlin, Patrick D; Twomey, Maria; Chan, Vincent E; Moloney, Fiachra; Fung, Adrian J; Chan, Faimee E; Kao, Tafline; O'Neill, Siobhan B; Watson, Benjamin; O'Connor, Owen J; Maher, Michael M

2017-04-01

We assess the ability of low-dose hybrid iterative reconstruction (IR) and 'pure' model-based IR (MBIR) images to maintain accurate Hounsfield unit (HU)-determined tissue characterization. Standard-protocol (SP) and low-dose modified-protocol (MP) CTs were contemporaneously acquired in 34 Crohn's disease patients referred for CT. SP image reconstruction was via the manufacturer's recommendations (60% FBP, filtered back projection; 40% ASiR, Adaptive Statistical iterative Reconstruction; SP-ASiR40). MP data sets underwent four reconstructions (100% FBP; 40% ASiR; 70% ASiR; MBIR). Three observers measured tissue volumes using HU thresholds for fat, soft tissue and bone/contrast on each data set. Analysis was via SPSS. Inter-observer agreement was strong for 1530 datapoints (rs > 0.9). MP-MBIR tissue volume measurement was superior to other MP reconstructions and closely correlated with the reference SP-ASiR40 images for all tissue types. MP-MBIR superiority was most marked for fat volume calculation - close SP-ASiR40 and MP-MBIR Bland-Altman plot correlation was seen with the lowest average difference (336 cm 3 ) when compared with other MP reconstructions. Hounsfield unit-determined tissue volume calculations from MP-MBIR images resulted in values comparable to SP-ASiR40 calculations and values that are superior to MP-ASiR images. Accuracy of estimation of volume of tissues (e.g. fat) using segmentation software on low-dose CT images appears optimal when reconstructed with pure IR. © 2016 The Royal Australian and New Zealand College of Radiologists.

A PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODEL FOR TRICHLOROETHYLENE WITH SPECIFICITY FOR THE LONG EVANS RAT

EPA Science Inventory

A PBPK model for TCE with specificity for the male LE rat that accurately predicts TCE tissue time-course data has not been developed, although other PBPK models for TCE exist. Development of such a model was the present aim. The PBPK model consisted of 5 compartments: fat; slowl...

Toward a patient-specific tissue engineered vascular graft

PubMed Central

Best, Cameron; Strouse, Robert; Hor, Kan; Pepper, Victoria; Tipton, Amy; Kelly, John; Shinoka, Toshiharu; Breuer, Christopher

2018-01-01

Integrating three-dimensional printing with the creation of tissue-engineered vascular grafts could provide a readily available, patient-specific, autologous tissue source that could significantly improve outcomes in newborns with congenital heart disease. Here, we present the recent case of a candidate for our tissue-engineered vascular graft clinical trial deemed ineligible due to complex anatomical requirements and consider the application of three-dimensional printing technologies for a patient-specific graft. We 3D-printed a closed-disposable seeding device and validated that it performed equivalently to the traditional open seeding technique using ovine bone marrow–derived mononuclear cells. Next, our candidate’s preoperative imaging was reviewed to propose a patient-specific graft. A seeding apparatus was then designed to accommodate the custom graft and 3D-printed on a commodity fused deposition modeler. This exploratory feasibility study represents an important proof of concept advancing progress toward a rationally designed patient-specific tissue-engineered vascular graft for clinical application. PMID:29568478

Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases.

PubMed

Barshir, Ruth; Shwartz, Omer; Smoly, Ilan Y; Yeger-Lotem, Esti

2014-06-01

An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases.

Electrosurgical vessel sealing tissue temperature: experimental measurement and finite element modeling.

PubMed

Chen, Roland K; Chastagner, Matthew W; Dodde, Robert E; Shih, Albert J

2013-02-01

The temporal and spatial tissue temperature profile in electrosurgical vessel sealing was experimentally measured and modeled using finite element modeling (FEM). Vessel sealing procedures are often performed near the neurovascular bundle and may cause collateral neural thermal damage. Therefore, the heat generated during electrosurgical vessel sealing is of concern among surgeons. Tissue temperature in an in vivo porcine femoral artery sealed using a bipolar electrosurgical device was studied. Three FEM techniques were incorporated to model the tissue evaporation, water loss, and fusion by manipulating the specific heat, electrical conductivity, and electrical contact resistance, respectively. These three techniques enable the FEM to accurately predict the vessel sealing tissue temperature profile. The averaged discrepancy between the experimentally measured temperature and the FEM predicted temperature at three thermistor locations is less than 7%. The maximum error is 23.9%. Effects of the three FEM techniques are also quantified.

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

PubMed

Khalifé, Maya; Fernandez, Brice; Jaubert, Olivier; Soussan, Michael; Brulon, Vincent; Buvat, Irène; Comtat, Claude

2017-09-21

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units ([Formula: see text]) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into [Formula: see text] was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of [Formula: see text] corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

NASA Astrophysics Data System (ADS)

Khalifé, Maya; Fernandez, Brice; Jaubert, Olivier; Soussan, Michael; Brulon, Vincent; Buvat, Irène; Comtat, Claude

2017-10-01

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units (HU ) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into HU was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of 4~mm corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

PubMed

Chi, Sang-Mun; Nam, Dougu

2012-04-01

We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

PubMed

Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

2017-11-15

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among

Recommendations for Accurate Resolution of Gene and Isoform Allele-Specific Expression in RNA-Seq Data

PubMed Central

Wood, David L. A.; Nones, Katia; Steptoe, Anita; Christ, Angelika; Harliwong, Ivon; Newell, Felicity; Bruxner, Timothy J. C.; Miller, David; Cloonan, Nicole; Grimmond, Sean M.

2015-01-01

Genetic variation modulates gene expression transcriptionally or post-transcriptionally, and can profoundly alter an individual’s phenotype. Measuring allelic differential expression at heterozygous loci within an individual, a phenomenon called allele-specific expression (ASE), can assist in identifying such factors. Massively parallel DNA and RNA sequencing and advances in bioinformatic methodologies provide an outstanding opportunity to measure ASE genome-wide. In this study, matched DNA and RNA sequencing, genotyping arrays and computationally phased haplotypes were integrated to comprehensively and conservatively quantify ASE in a single human brain and liver tissue sample. We describe a methodological evaluation and assessment of common bioinformatic steps for ASE quantification, and recommend a robust approach to accurately measure SNP, gene and isoform ASE through the use of personalized haplotype genome alignment, strict alignment quality control and intragenic SNP aggregation. Our results indicate that accurate ASE quantification requires careful bioinformatic analyses and is adversely affected by sample specific alignment confounders and random sampling even at moderate sequence depths. We identified multiple known and several novel ASE genes in liver, including WDR72, DSP and UBD, as well as genes that contained ASE SNPs with imbalance direction discordant with haplotype phase, explainable by annotated transcript structure, suggesting isoform derived ASE. The methods evaluated in this study will be of use to researchers performing highly conservative quantification of ASE, and the genes and isoforms identified as ASE of interest to researchers studying those loci. PMID:25965996

PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

PubMed Central

Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

2014-01-01

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

A grammar inference approach for predicting kinase specific phosphorylation sites.

PubMed

Datta, Sutapa; Mukhopadhyay, Subhasis

2015-01-01

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.

Repressor-mediated tissue-specific gene expression in plants

DOEpatents

Meagher, Richard B [Athens, GA; Balish, Rebecca S [Oxford, OH; Tehryung, Kim [Athens, GA; McKinney, Elizabeth C [Athens, GA

2009-02-17

Plant tissue specific gene expression by way of repressor-operator complexes, has enabled outcomes including, without limitation, male sterility and engineered plants having root-specific gene expression of relevant proteins to clean environmental pollutants from soil and water. A mercury hyperaccumulation strategy requires that mercuric ion reductase coding sequence is strongly expressed. The actin promoter vector, A2pot, engineered to contain bacterial lac operator sequences, directed strong expression in all plant vegetative organs and tissues. In contrast, the expression from the A2pot construct was restricted primarily to root tissues when a modified bacterial repressor (LacIn) was coexpressed from the light-regulated rubisco small subunit promoter in above-ground tissues. Also provided are analogous repressor operator complexes for selective expression in other plant tissues, for example, to produce male sterile plants.

Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

PubMed

Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

2015-09-01

There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

Tissue specificity of the hormonal response in sex accessory tissues is associated with nuclear matrix protein patterns.

PubMed

Getzenberg, R H; Coffey, D S

1990-09-01

The DNA of interphase nuclei have very specific three-dimensional organizations that are different in different cell types, and it is possible that this varying DNA organization is responsible for the tissue specificity of gene expression. The nuclear matrix organizes the three-dimensional structure of the DNA and is believed to be involved in the control of gene expression. This study compares the nuclear structural proteins between two sex accessory tissues in the same animal responding to the same androgen stimulation by the differential expression of major tissue-specific secretory proteins. We demonstrate here that the nuclear matrix is tissue specific in the rat ventral prostate and seminal vesicle, and undergoes characteristic alterations in its protein composition upon androgen withdrawal. Three types of nuclear matrix proteins were observed: 1) nuclear matrix proteins that are different and tissue specific in the rat ventral prostate and seminal vesicle, 2) a set of nuclear matrix proteins that either appear or disappear upon androgen withdrawal, and 3) a set of proteins that are common to both the ventral prostate and seminal vesicle and do not change with the hormonal state of the animal. Since the nuclear matrix is known to bind androgen receptors in a tissue- and steroid-specific manner, we propose that the tissue specificity of the nuclear matrix arranges the DNA in a unique conformation, which may be involved in the specific interaction of transcription factors with DNA sequences, resulting in tissue-specific patterns of secretory protein expression.

Prediction of brain tissue temperature using near-infrared spectroscopy.

PubMed

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-04-01

Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications.

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

PubMed

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results

Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

PubMed

Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

2013-12-15

Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

A convex optimization approach for identification of human tissue-specific interactomes.

PubMed

Mohammadi, Shahin; Grama, Ananth

2016-06-15

Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes. We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer's and Parkinson's diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets. http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html mohammadi@purdue.edu. © The Author 2016. Published by Oxford University Press.

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

PubMed Central

Burger, Lukas; van Nimwegen, Erik

2008-01-01

Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

Cell-specific prediction and application of drug-induced gene expression profiles.

PubMed

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Cell-specific prediction and application of drug-induced gene expression profiles

PubMed Central

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R.; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David

2017-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes. PMID:29218867

A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

2004-12-01

We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

Accurate Characterization of Benign and Cancerous Breast Tissues: Aspecific Patient Studies using Piezoresistive Microcantilevers

PubMed Central

PANDYA, HARDIK J.; ROY, RAJARSHI; CHEN, WENJIN; CHEKMAREVA, MARINA A.; FORAN, DAVID J.; DESAI, JAYDEV P.

2014-01-01

Breast cancer is the largest detected cancer amongst women in the US. In this work, our team reports on the development of piezoresistive microcantilevers (PMCs) to investigate their potential use in the accurate detection and characterization of benign and diseased breast tissues by performing indentations on the micro-scale tissue specimens. The PMCs used in these experiments have been fabricated using laboratory-made silicon-on-insulator (SOI) substrate, which significantly reduces the fabrication costs. The PMCs are 260 μm long, 35 μm wide and 2 μm thick with resistivity of order 1.316 X 10−3 Ω-cm obtained by using boron diffusion technique. For indenting the tissue, we utilized 8 μm thick cylindrical SU-8 tip. The PMC was calibrated against a known AFM probe. Breast tissue cores from seven different specimens were indented using PMC to identify benign and cancerous tissue cores. Furthermore, field emission scanning electron microscopy (FE-SEM) of benign and cancerous specimens showed marked differences in the tissue morphology, which further validates our observed experimental data with the PMCs. While these patient aspecific feasibility studies clearly demonstrate the ability to discriminate between benign and cancerous breast tissues, further investigation is necessary to perform automated mechano-phenotyping (classification) of breast cancer: from onset to disease progression. PMID:25128621

Oscillator networks with tissue-specific circadian clocks in plants.

PubMed

Inoue, Keisuke; Araki, Takashi; Endo, Motomu

2017-09-08

Many organisms rely on circadian clocks to synchronize their biological processes with the 24-h rotation of the earth. In mammals, the circadian clock consists of a central clock in the suprachiasmatic nucleus and peripheral clocks in other tissues. The central clock is tightly coupled to synchronize rhythmicity and can organize peripheral clocks through neural and hormonal signals. In contrast to mammals, it has long been assumed that the circadian clocks in each plant cell is able to be entrained by external light, and they are only weakly coupled to each other. Recently, however, several reports have demonstrated that plants have unique oscillator networks with tissue-specific circadian clocks. Here, we introduce our current view regarding tissue-specific properties and oscillator networks of plant circadian clocks. Accumulating evidence suggests that plants have multiple oscillators, which show distinct properties and reside in different tissues. A direct tissue-isolation technique and micrografting have clearly demonstrated that plants have hierarchical oscillator networks consisting of multiple tissue-specific clocks. Copyright © 2017. Published by Elsevier Ltd.

Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms

PubMed Central

Park, Christopher Y.; Krishnan, Arjun; Zhu, Qian; Wong, Aaron K.; Lee, Young-Suk; Troyanskaya, Olga G.

2015-01-01

Motivation: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses. Results: We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry). Availability and implementation: An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/libsleipnir.bitbucket.org. Contact: ogt@cs.princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25431329

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Epigenetic regulation of depot-specific gene expression in adipose tissue.

PubMed

Gehrke, Sandra; Brueckner, Bodo; Schepky, Andreas; Klein, Johannes; Iwen, Alexander; Bosch, Thomas C G; Wenck, Horst; Winnefeld, Marc; Hagemann, Sabine

2013-01-01

In humans, adipose tissue is distributed in subcutaneous abdominal and subcutaneous gluteal depots that comprise a variety of functional differences. Whereas energy storage in gluteal adipose tissue has been shown to mediate a protective effect, an increase of abdominal adipose tissue is associated with metabolic disorders. However, the molecular basis of depot-specific characteristics is not completely understood yet. Using array-based analyses of transcription profiles, we identified a specific set of genes that was differentially expressed between subcutaneous abdominal and gluteal adipose tissue. To investigate the role of epigenetic regulation in depot-specific gene expression, we additionally analyzed genome-wide DNA methylation patterns in abdominal and gluteal depots. By combining both data sets, we identified a highly significant set of depot-specifically expressed genes that appear to be epigenetically regulated. Interestingly, the majority of these genes form part of the homeobox gene family. Moreover, genes involved in fatty acid metabolism were also differentially expressed. Therefore we suppose that changes in gene expression profiles might account for depot-specific differences in lipid composition. Indeed, triglycerides and fatty acids of abdominal adipose tissue were more saturated compared to triglycerides and fatty acids in gluteal adipose tissue. Taken together, our results uncover clear differences between abdominal and gluteal adipose tissue on the gene expression and DNA methylation level as well as in fatty acid composition. Therefore, a detailed molecular characterization of adipose tissue depots will be essential to develop new treatment strategies for metabolic syndrome associated complications.

Are EMS call volume predictions based on demand pattern analysis accurate?

PubMed

Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

2007-01-01

Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over

Tissue-specific contribution of macrophages to wound healing.

PubMed

Minutti, Carlos M; Knipper, Johanna A; Allen, Judith E; Zaiss, Dietmar M W

2017-01-01

Macrophages are present in all tissues, either as resident cells or monocyte-derived cells that infiltrate into tissues. The tissue site largely determines the phenotype of tissue-resident cells, which help to maintain tissue homeostasis and act as sentinels of injury. Both tissue resident and recruited macrophages make a substantial contribution to wound healing following injury. In this review, we evaluate how macrophages in two fundamentally distinct tissues, i.e. the lung and the skin, differentially contribute to the process of wound healing. We highlight the commonalities of macrophage functions during repair and contrast them with distinct, tissue-specific functions that macrophages fulfill during the different stages of wound healing. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

PubMed

Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

2012-01-01

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

Mapping the mouse Allelome reveals tissue-specific regulation of allelic expression

PubMed Central

Andergassen, Daniel; Dotter, Christoph P; Wenzel, Daniel; Sigl, Verena; Bammer, Philipp C; Muckenhuber, Markus; Mayer, Daniela; Kulinski, Tomasz M; Theussl, Hans-Christian; Penninger, Josef M; Bock, Christoph; Barlow, Denise P; Pauler, Florian M; Hudson, Quanah J

2017-01-01

To determine the dynamics of allelic-specific expression during mouse development, we analyzed RNA-seq data from 23 F1 tissues from different developmental stages, including 19 female tissues allowing X chromosome inactivation (XCI) escapers to also be detected. We demonstrate that allelic expression arising from genetic or epigenetic differences is highly tissue-specific. We find that tissue-specific strain-biased gene expression may be regulated by tissue-specific enhancers or by post-transcriptional differences in stability between the alleles. We also find that escape from X-inactivation is tissue-specific, with leg muscle showing an unexpectedly high rate of XCI escapers. By surveying a range of tissues during development, and performing extensive validation, we are able to provide a high confidence list of mouse imprinted genes including 18 novel genes. This shows that cluster size varies dynamically during development and can be substantially larger than previously thought, with the Igf2r cluster extending over 10 Mb in placenta. DOI: http://dx.doi.org/10.7554/eLife.25125.001 PMID:28806168

Spectroscopy of Multilayered Biological Tissues for Diabetes Care

NASA Astrophysics Data System (ADS)

Yudovsky, Dmitry

Neurological and vascular complications of diabetes mellitus are known to cause foot ulceration in diabetic patients. Present clinical screening techniques enable the diabetes care provider to triage treatment by identifying diabetic patients at risk of foot ulceration. However, these techniques cannot effectively identify specific areas of the foot at risk of ulceration. This study aims to develop non-invasive optical techniques for accurate assessment of tissue health and viability with spatial resolution on the order of 1 mm². The thesis can be divided into three parts: (1) the use of hyperspectral tissue oximetry to detect microcirculatory changes prior to ulcer formation, (2) development of a two-layer tissue spectroscopy algorithm and its application to detection of callus formation or epidermal degradation prior to ulceration, and (3) multi-layered tissue fluorescence modeling for identification of bacterial growth in existing diabetic foot wounds. The first part of the dissertation describes a clinical study in which hyperspectral tissue oximetry was performed on multiple diabetic subjects at risk of ulceration. Tissue oxyhemoglobin and deoxyhemoglobin concentrations were estimated using the Modified Beer-Lambert law. Then, an ulcer prediction algorithm was developed based on retrospective analysis of oxyhemoglobin and deoxyhemoglobin concentrations in sites that were known to ulcerate. The ulcer prediction algorithm exhibited a large sensitivity but low specificity of 95 and 80%, respectively. The second part of the dissertation revisited the hyperspectral data presented in part one with a new and novel two-layer tissue spectroscopy algorithm. This algorithm was able to detect not only oxyhemoglobin and deoxyhemoglobin concentrations, but also the thickness of the epidermis, and the tissue's scattering coefficient. Specifically, change in epidermal thickness provided insight into the formation of diabetic foot ulcers over time. Indeed, callus formation or

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

PubMed Central

Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

2017-01-01

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

Prediction of brain tissue temperature using near-infrared spectroscopy

PubMed Central

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-01-01

Abstract. Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of R2=0.713±0.157 (animal dataset) and R2=0.798±0.087 (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of 0.436±0.283°C (animal dataset) and 0.162±0.149°C (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications. PMID:28630878

Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

PubMed

Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

2017-02-14

Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

Mammalian transcriptional hotspots are enriched for tissue specific enhancers near cell type specific highly expressed genes and are predicted to act as transcriptional activator hubs.

PubMed

Joshi, Anagha

2014-12-30

Transcriptional hotspots are defined as genomic regions bound by multiple factors. They have been identified recently as cell type specific enhancers regulating developmentally essential genes in many species such as worm, fly and humans. The in-depth analysis of hotspots across multiple cell types in same species still remains to be explored and can bring new biological insights. We therefore collected 108 transcription-related factor (TF) ChIP sequencing data sets in ten murine cell types and classified the peaks in each cell type in three groups according to binding occupancy as singletons (low-occupancy), combinatorials (mid-occupancy) and hotspots (high-occupancy). The peaks in the three groups clustered largely according to the occupancy, suggesting priming of genomic loci for mid occupancy irrespective of cell type. We then characterized hotspots for diverse structural functional properties. The genes neighbouring hotspots had a small overlap with hotspot genes in other cell types and were highly enriched for cell type specific function. Hotspots were enriched for sequence motifs of key TFs in that cell type and more than 90% of hotspots were occupied by pioneering factors. Though we did not find any sequence signature in the three groups, the H3K4me1 binding profile had bimodal peaks at hotspots, distinguishing hotspots from mono-modal H3K4me1 singletons. In ES cells, differentially expressed genes after perturbation of activators were enriched for hotspot genes suggesting hotspots primarily act as transcriptional activator hubs. Finally, we proposed that ES hotspots might be under control of SetDB1 and not DNMT for silencing. Transcriptional hotspots are enriched for tissue specific enhancers near cell type specific highly expressed genes. In ES cells, they are predicted to act as transcriptional activator hubs and might be under SetDB1 control for silencing.

Quantifying dynamic mechanical properties of human placenta tissue using optimization techniques with specimen-specific finite-element models.

PubMed

Hu, Jingwen; Klinich, Kathleen D; Miller, Carl S; Nazmi, Giseli; Pearlman, Mark D; Schneider, Lawrence W; Rupp, Jonathan D

2009-11-13

Motor-vehicle crashes are the leading cause of fetal deaths resulting from maternal trauma in the United States, and placental abruption is the most common cause of these deaths. To minimize this injury, new assessment tools, such as crash-test dummies and computational models of pregnant women, are needed to evaluate vehicle restraint systems with respect to reducing the risk of placental abruption. Developing these models requires accurate material properties for tissues in the pregnant abdomen under dynamic loading conditions that can occur in crashes. A method has been developed for determining dynamic material properties of human soft tissues that combines results from uniaxial tensile tests, specimen-specific finite-element models based on laser scans that accurately capture non-uniform tissue-specimen geometry, and optimization techniques. The current study applies this method to characterizing material properties of placental tissue. For 21 placenta specimens tested at a strain rate of 12/s, the mean failure strain is 0.472+/-0.097 and the mean failure stress is 34.80+/-12.62 kPa. A first-order Ogden material model with ground-state shear modulus (mu) of 23.97+/-5.52 kPa and exponent (alpha(1)) of 3.66+/-1.90 best fits the test results. The new method provides a nearly 40% error reduction (p<0.001) compared to traditional curve-fitting methods by considering detailed specimen geometry, loading conditions, and dynamic effects from high-speed loading. The proposed method can be applied to determine mechanical properties of other soft biological tissues.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE PAGES

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-12-28

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

On the effects of alternative optima in context-specific metabolic model predictions

PubMed Central

Nikoloski, Zoran

2017-01-01

The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed—generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative

On the effects of alternative optima in context-specific metabolic model predictions.

PubMed

Robaina-Estévez, Semidán; Nikoloski, Zoran

2017-05-01

The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed-generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative

Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

PubMed

Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

2015-01-01

Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

PubMed Central

Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

2015-01-01

Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106

Measuring the value of accurate link prediction for network seeding.

PubMed

Wei, Yijin; Spencer, Gwen

2017-01-01

The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

Biomimetic three-dimensional tissue models for advanced high-throughput drug screening

PubMed Central

Nam, Ki-Hwan; Smith, Alec S.T.; Lone, Saifullah; Kwon, Sunghoon; Kim, Deok-Ho

2015-01-01

Most current drug screening assays used to identify new drug candidates are 2D cell-based systems, even though such in vitro assays do not adequately recreate the in vivo complexity of 3D tissues. Inadequate representation of the human tissue environment during a preclinical test can result in inaccurate predictions of compound effects on overall tissue functionality. Screening for compound efficacy by focusing on a single pathway or protein target, coupled with difficulties in maintaining long-term 2D monolayers, can serve to exacerbate these issues when utilizing such simplistic model systems for physiological drug screening applications. Numerous studies have shown that cell responses to drugs in 3D culture are improved from those in 2D, with respect to modeling in vivo tissue functionality, which highlights the advantages of using 3D-based models for preclinical drug screens. In this review, we discuss the development of microengineered 3D tissue models which accurately mimic the physiological properties of native tissue samples, and highlight the advantages of using such 3D micro-tissue models over conventional cell-based assays for future drug screening applications. We also discuss biomimetic 3D environments, based-on engineered tissues as potential preclinical models for the development of more predictive drug screening assays for specific disease models. PMID:25385716

Deep learning based tissue analysis predicts outcome in colorectal cancer.

PubMed

Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan

2018-02-21

Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.

Sensitivity and specificity of radiographic methods for predicting insertion torque of dental implants.

PubMed

Cortes, Arthur Rodriguez Gonzalez; Eimar, Hazem; Barbosa, Jorge de Sá; Costa, Claudio; Arita, Emiko Saito; Tamimi, Faleh

2015-05-01

Subjective radiographic classifications of alveolar bone have been proposed and correlated with implant insertion torque (IT). The present diagnostic study aims to identify quantitative bone features influencing IT and to use these findings to develop an objective radiographic classification for predicting IT. Demographics, panoramic radiographs (taken at the beginning of dental treatment), and cone-beam computed tomographic scans (taken for implant surgical planning) of 25 patients receiving 31 implants were analyzed. Bone samples retrieved from implant sites were assessed with dual x-ray absorptiometry, microcomputed tomography, and histology. Odds ratio, sensitivity, and specificity of all variables to predict high peak IT were assessed. A ridge cortical thickness >0.75 mm and a normal appearance of the inferior mandibular cortex were the most sensitive variables for predicting high peak IT (87.5% and 75%, respectively). A classification based on the combination of both variables presented high sensitivity (90.9%) and specificity (100%) for predicting IT. Within the limitations of this study, the results suggest that it is possible to predict IT accurately based on radiographic findings of the patient. This could be useful in the treatment plan of immediate loading cases.

Discriminative prediction of mammalian enhancers from DNA sequence

PubMed Central

Lee, Dongwon; Karchin, Rachel; Beer, Michael A.

2011-01-01

Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers. PMID:21875935

Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

PubMed

Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

2013-01-02

Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

PubMed

Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

2017-05-01

physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

The gastric/pancreatic amylase ratio predicts postoperative pancreatic fistula with high sensitivity and specificity.

PubMed

Jin, Shuo; Shi, Xiao-Ju; Sun, Xiao-Dong; Zhang, Ping; Lv, Guo-Yue; Du, Xiao-Hong; Wang, Si-Yuan; Wang, Guang-Yi

2015-01-01

This article aims to identify risk factors for postoperative pancreatic fistula (POPF) and evaluate the gastric/pancreatic amylase ratio (GPAR) on postoperative day (POD) 3 as a POPF predictor in patients who undergo pancreaticoduodenectomy (PD).POPF significantly contributes to mortality and morbidity in patients who undergo PD. Previously identified predictors for POPF often have low predictive accuracy. Therefore, accurate POPF predictors are needed.In this prospective cohort study, we measured the clinical and biochemical factors of 61 patients who underwent PD and diagnosed POPF according to the definition of the International Study Group of Pancreatic Fistula. We analyzed the association between POPF and various factors, identified POPF risk factors, and evaluated the predictive power of the GPAR on POD3 and the levels of serum and ascites amylase.Of the 61 patients, 21 developed POPF. The color of the pancreatic drain fluid, POD1 serum, POD1 median output of pancreatic drain fluid volume, and GPAR were significantly associated with POPF. The color of the pancreatic drain fluid and high GPAR were independent risk factors. Although serum and ascites amylase did not predict POPF accurately, the cutoff value was 1.24, and GPAR predicted POPF with high sensitivity and specificity.This is the first report demonstrating that high GPAR on POD3 is a risk factor for POPF and showing that GPAR is a more accurate predictor of POPF than the previously reported amylase markers.

The Gastric/Pancreatic Amylase Ratio Predicts Postoperative Pancreatic Fistula With High Sensitivity and Specificity

PubMed Central

Jin, Shuo; Shi, Xiao-Ju; Sun, Xiao-Dong; Zhang, Ping; Lv, Guo-Yue; Du, Xiao-Hong; Wang, Si-Yuan; Wang, Guang-Yi

2015-01-01

Abstract This article aims to identify risk factors for postoperative pancreatic fistula (POPF) and evaluate the gastric/pancreatic amylase ratio (GPAR) on postoperative day (POD) 3 as a POPF predictor in patients who undergo pancreaticoduodenectomy (PD). POPF significantly contributes to mortality and morbidity in patients who undergo PD. Previously identified predictors for POPF often have low predictive accuracy. Therefore, accurate POPF predictors are needed. In this prospective cohort study, we measured the clinical and biochemical factors of 61 patients who underwent PD and diagnosed POPF according to the definition of the International Study Group of Pancreatic Fistula. We analyzed the association between POPF and various factors, identified POPF risk factors, and evaluated the predictive power of the GPAR on POD3 and the levels of serum and ascites amylase. Of the 61 patients, 21 developed POPF. The color of the pancreatic drain fluid, POD1 serum, POD1 median output of pancreatic drain fluid volume, and GPAR were significantly associated with POPF. The color of the pancreatic drain fluid and high GPAR were independent risk factors. Although serum and ascites amylase did not predict POPF accurately, the cutoff value was 1.24, and GPAR predicted POPF with high sensitivity and specificity. This is the first report demonstrating that high GPAR on POD3 is a risk factor for POPF and showing that GPAR is a more accurate predictor of POPF than the previously reported amylase markers. PMID:25621676

Bioprinting Cellularized Constructs Using a Tissue-specific Hydrogel Bioink

PubMed Central

Skardal, Aleksander; Devarasetty, Mahesh; Kang, Hyun-Wook; Seol, Young-Joon; Forsythe, Steven D.; Bishop, Colin; Shupe, Thomas; Soker, Shay; Atala, Anthony

2016-01-01

Bioprinting has emerged as a versatile biofabrication approach for creating tissue engineered organ constructs. These constructs have potential use as organ replacements for implantation in patients, and also, when created on a smaller size scale as model "organoids" that can be used in in vitro systems for drug and toxicology screening. Despite development of a wide variety of bioprinting devices, application of bioprinting technology can be limited by the availability of materials that both expedite bioprinting procedures and support cell viability and function by providing tissue-specific cues. Here we describe a versatile hyaluronic acid (HA) and gelatin-based hydrogel system comprised of a multi-crosslinker, 2-stage crosslinking protocol, which can provide tissue specific biochemical signals and mimic the mechanical properties of in vivo tissues. Biochemical factors are provided by incorporating tissue-derived extracellular matrix materials, which include potent growth factors. Tissue mechanical properties are controlled combinations of PEG-based crosslinkers with varying molecular weights, geometries (linear or multi-arm), and functional groups to yield extrudable bioinks and final construct shear stiffness values over a wide range (100 Pa to 20 kPa). Using these parameters, hydrogel bioinks were used to bioprint primary liver spheroids in a liver-specific bioink to create in vitro liver constructs with high cell viability and measurable functional albumin and urea output. This methodology provides a general framework that can be adapted for future customization of hydrogels for biofabrication of a wide range of tissue construct types. PMID:27166839

Bioprinting Cellularized Constructs Using a Tissue-specific Hydrogel Bioink.

PubMed

Skardal, Aleksander; Devarasetty, Mahesh; Kang, Hyun-Wook; Seol, Young-Joon; Forsythe, Steven D; Bishop, Colin; Shupe, Thomas; Soker, Shay; Atala, Anthony

2016-04-21

Bioprinting has emerged as a versatile biofabrication approach for creating tissue engineered organ constructs. These constructs have potential use as organ replacements for implantation in patients, and also, when created on a smaller size scale as model "organoids" that can be used in in vitro systems for drug and toxicology screening. Despite development of a wide variety of bioprinting devices, application of bioprinting technology can be limited by the availability of materials that both expedite bioprinting procedures and support cell viability and function by providing tissue-specific cues. Here we describe a versatile hyaluronic acid (HA) and gelatin-based hydrogel system comprised of a multi-crosslinker, 2-stage crosslinking protocol, which can provide tissue specific biochemical signals and mimic the mechanical properties of in vivo tissues. Biochemical factors are provided by incorporating tissue-derived extracellular matrix materials, which include potent growth factors. Tissue mechanical properties are controlled combinations of PEG-based crosslinkers with varying molecular weights, geometries (linear or multi-arm), and functional groups to yield extrudable bioinks and final construct shear stiffness values over a wide range (100 Pa to 20 kPa). Using these parameters, hydrogel bioinks were used to bioprint primary liver spheroids in a liver-specific bioink to create in vitro liver constructs with high cell viability and measurable functional albumin and urea output. This methodology provides a general framework that can be adapted for future customization of hydrogels for biofabrication of a wide range of tissue construct types.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

PubMed Central

Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

2017-01-01

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

Tissue- and Time-Specific Expression of Otherwise Identical tRNA Genes

PubMed Central

Adir, Idan; Dahan, Orna; Broday, Limor; Pilpel, Yitzhak; Rechavi, Oded

2016-01-01

Codon usage bias affects protein translation because tRNAs that recognize synonymous codons differ in their abundance. Although the current dogma states that tRNA expression is exclusively regulated by intrinsic control elements (A- and B-box sequences), we revealed, using a reporter that monitors the levels of individual tRNA genes in Caenorhabditis elegans, that eight tryptophan tRNA genes, 100% identical in sequence, are expressed in different tissues and change their expression dynamically. Furthermore, the expression levels of the sup-7 tRNA gene at day 6 were found to predict the animal’s lifespan. We discovered that the expression of tRNAs that reside within introns of protein-coding genes is affected by the host gene’s promoter. Pairing between specific Pol II genes and the tRNAs that are contained in their introns is most likely adaptive, since a genome-wide analysis revealed that the presence of specific intronic tRNAs within specific orthologous genes is conserved across Caenorhabditis species. PMID:27560950

CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data

PubMed Central

O'Connor, Timothy; Bodén, Mikael

2017-01-01

Abstract Identifying the genomic regions and regulatory factors that control the transcription of genes is an important, unsolved problem. The current method of choice predicts transcription factor (TF) binding sites using chromatin immunoprecipitation followed by sequencing (ChIP-seq), and then links the binding sites to putative target genes solely on the basis of the genomic distance between them. Evidence from chromatin conformation capture experiments shows that this approach is inadequate due to long-distance regulation via chromatin looping. We present CisMapper, which predicts the regulatory targets of a TF using the correlation between a histone mark at the TF's bound sites and the expression of each gene across a panel of tissues. Using both chromatin conformation capture and differential expression data, we show that CisMapper is more accurate at predicting the target genes of a TF than the distance-based approaches currently used, and is particularly advantageous for predicting the long-range regulatory interactions typical of tissue-specific gene expression. CisMapper also predicts which TF binding sites regulate a given gene more accurately than using genomic distance. Unlike distance-based methods, CisMapper can predict which transcription start site of a gene is regulated by a particular binding site of the TF. PMID:28204599

Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

PubMed

Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

2018-03-01

Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

PubMed Central

Schultz, André; Qutub, Amina A.

2016-01-01

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

Cell-specific dysregulation of microRNA expression in obese white adipose tissue.

PubMed

Oger, Frédérik; Gheeraert, Celine; Mogilenko, Denis; Benomar, Yacir; Molendi-Coste, Olivier; Bouchaert, Emmanuel; Caron, Sandrine; Dombrowicz, David; Pattou, François; Duez, Hélène; Eeckhoute, Jérome; Staels, Bart; Lefebvre, Philippe

2014-08-01

Obesity is characterized by the excessive accumulation of dysfunctional white adipose tissue (WAT), leading to a strong perturbation of metabolic regulations. However, the molecular events underlying this process are not fully understood. MicroRNAs (miRNAs) are small noncoding RNAs acting as posttranscriptional regulators of gene expression in multiple tissues and organs. However, their expression and roles in WAT cell subtypes, which include not only adipocytes but also immune, endothelial, and mesenchymal stem cells as well as preadipocytes, have not been characterized. Design/Results: By applying differential miRNome analysis, we demonstrate that the expression of several miRNAs is dysregulated in epididymal WAT from ob/ob and high-fat diet-fed mice. Adipose tissue-specific down-regulation of miR-200a and miR-200b and the up-regulation of miR-342-3p, miR-335-5p, and miR-335-3p were observed. Importantly, a similarly altered expression of miR-200a and miR-200b was observed in obese diabetic patients. Furthermore, cell fractionation of mouse adipose tissue revealed that miRNAs are differentially expressed in adipocytes and in subpopulations from the stromal vascular fraction. Finally, integration of transcriptomic data showed that bioinformatically predicted miRNA target genes rarely showed anticorrelated expression with that of targeting miRNA, in contrast to experimentally validated target genes. Taken together, our data indicate that the dysregulated expression of miRNAs occurs in distinct cell types and is likely to affect cell-specific function(s) of obese WAT.

Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

DOE PAGES

Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

2013-03-07

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

PubMed

Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

2017-01-09

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

An accurate model for predicting high frequency noise of nanoscale NMOS SOI transistors

NASA Astrophysics Data System (ADS)

Shen, Yanfei; Cui, Jie; Mohammadi, Saeed

2017-05-01

A nonlinear and scalable model suitable for predicting high frequency noise of N-type Metal Oxide Semiconductor (NMOS) transistors is presented. The model is developed for a commercial 45 nm CMOS SOI technology and its accuracy is validated through comparison with measured performance of a microwave low noise amplifier. The model employs the virtual source nonlinear core and adds parasitic elements to accurately simulate the RF behavior of multi-finger NMOS transistors up to 40 GHz. For the first time, the traditional long-channel thermal noise model is supplemented with an injection noise model to accurately represent the noise behavior of these short-channel transistors up to 26 GHz. The developed model is simple and easy to extract, yet very accurate.

Identification of species- and tissue-specific proteins using proteomic strategy

NASA Astrophysics Data System (ADS)

Chernukha, I. M.; Vostrikova, N. L.; Kovalev, L. I.; Shishkin, S. S.; Kovaleva, M. A.; Manukhin, Y. S.

2017-09-01

Proteomic technologies have proven to be very effective for detecting biochemical changes in meat products, such as changes in tissue- and species-specific proteins. In the tissues of cattle, pig, horse and camel M. longissimus dorsi both tissue- and species specific proteins were detected using two dimensional electrophoresis. Species-specific isoforms of several muscle proteins were also identified. The identified and described proteins of cattle, pig, horse and camel skeletal muscles (including mass spectra of the tryptic peptides) were added to the national free access database “Muscle organ proteomics”. This research has enabled the development of new highly sensitive technologies for meat product quality control against food fraud.

Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

NASA Astrophysics Data System (ADS)

Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

2017-04-01

Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

Seasonal, tissue-specific regulation of Akt/protein kinase B and glycogen synthase in hibernators.

PubMed

Hoehn, Kyle L; Hudachek, Susan F; Summers, Scott A; Florant, Gregory L

2004-03-01

Yellow-bellied marmots (Marmota flaviventris) exhibit a circannual cycle of hyperphagia and nutrient storage in the summer followed by hibernation in the winter. This annual cycle of body mass gain and loss is primarily due to large-scale accumulation of lipid in the summer, which is then mobilized and oxidized for energy during winter. The rapid and predictable change in body mass makes these animals ideal for studies investigating the molecular basis for body weight regulation. In the study described herein, we monitored seasonal changes in the protein levels and activity of a central regulator of anabolic metabolism, the serine-threonine kinase Akt-protein kinase B (Akt/PKB), during the months accompanying maximal weight gain and entry into hibernation (June-November). Interestingly, under fasting conditions, Akt/PKB demonstrated a tissue-specific seasonal activation. Specifically, although Akt/PKB levels did not change, the activity of Akt/PKB (isoforms 1/alpha and 2/beta) in white adipose tissue (WAT) increased significantly in July. Moreover, glycogen synthase, which lies downstream of Akt/PKB on a linear pathway linking the enzyme to the stimulation of glycogen synthesis, demonstrated a similar pattern of seasonal activation. By contrast, Akt/PKB activity in skeletal muscle peaked much later (i.e., September). These data suggest the existence of a novel, tissue-specific mechanism regulating Akt/PKB activation during periods of marked anabolism.

Ovule development: identification of stage-specific and tissue-specific cDNAs.

PubMed Central

Nadeau, J A; Zhang, X S; Li, J; O'Neill, S D

1996-01-01

A differential screening approach was used to identify seven ovule-specific cDNAs representing genes that are expressed in a stage-specific manner during ovule development. The Phalaenopsis orchid takes 80 days to complete the sequence of ovule developmental events, making it a good system to isolate stage-specific ovule genes. We constructed cDNA libraries from orchid ovule tissue during archesporial cell differentiation, megasporocyte formation, and the transition to meiosis, as well as during the final mitotic divisions of female gametophyte development. RNA gel blot hybridization analysis revealed that four clones were stage specific and expressed solely in ovule tissue, whereas one clone was specific to pollen tubes. Two other clones were not ovule specific. Sequence analysis and in situ hybridization revealed the identities and domain of expression of several of the cDNAs. O39 encodes a putative homeobox transcription factor that is expressed early in the differentiation of the ovule primordium; O40 encodes a cytochrome P450 monooxygenase (CYP78A2) that is pollen tube specific. O108 encodes a protein of unknown function that is expressed exclusively in the outer layer of the outer integument and in the female gametophyte of mature ovules. O126 encodes a glycine-rich protein that is expressed in mature ovules, and O141 encodes a cysteine proteinase that is expressed in the outer integument of ovules during seed formation. Sequences homologous to these ovule clones can now be isolated from other organisms, and this should facilitate their functional characterization. PMID:8742709

Competitive Abilities in Experimental Microcosms Are Accurately Predicted by a Demographic Index for R*

PubMed Central

Murrell, Ebony G.; Juliano, Steven A.

2012-01-01

Resource competition theory predicts that R*, the equilibrium resource amount yielding zero growth of a consumer population, should predict species' competitive abilities for that resource. This concept has been supported for unicellular organisms, but has not been well-tested for metazoans, probably due to the difficulty of raising experimental populations to equilibrium and measuring population growth rates for species with long or complex life cycles. We developed an index (Rindex) of R* based on demography of one insect cohort, growing from egg to adult in a non-equilibrium setting, and tested whether Rindex yielded accurate predictions of competitive abilities using mosquitoes as a model system. We estimated finite rate of increase (λ′) from demographic data for cohorts of three mosquito species raised with different detritus amounts, and estimated each species' Rindex using nonlinear regressions of λ′ vs. initial detritus amount. All three species' Rindex differed significantly, and accurately predicted competitive hierarchy of the species determined in simultaneous pairwise competition experiments. Our Rindex could provide estimates and rigorous statistical comparisons of competitive ability for organisms for which typical chemostat methods and equilibrium population conditions are impractical. PMID:22970128

ROCK I Has More Accurate Prognostic Value than MET in Predicting Patient Survival in Colorectal Cancer.

PubMed

Li, Jian; Bharadwaj, Shruthi S; Guzman, Grace; Vishnubhotla, Ramana; Glover, Sarah C

2015-06-01

Colorectal cancer remains the second leading cause of death in the United States despite improvements in incidence rates and advancements in screening. The present study evaluated the prognostic value of two tumor markers, MET and ROCK I, which have been noted in other cancers to provide more accurate prognoses of patient outcomes than tumor staging alone. We constructed a tissue microarray from surgical specimens of adenocarcinomas from 108 colorectal cancer patients. Using immunohistochemistry, we examined the expression levels of tumor markers MET and ROCK I, with a pathologist blinded to patient identities and clinical outcomes providing the scoring of MET and ROCK I expression. We then used retrospective analysis of patients' survival data to provide correlations with expression levels of MET and ROCK I. Both MET and ROCK I were significantly over-expressed in colorectal cancer tissues, relative to the unaffected adjacent mucosa. Kaplan-Meier survival analysis revealed that patients' 5-year survival was inversely correlated with levels of expression of ROCK I. In contrast, MET was less strongly correlated with five-year survival. ROCK I provides better efficacy in predicting patient outcomes, compared to either tumor staging or MET expression. As a result, ROCK I may provide a less invasive method of assessing patient prognoses and directing therapeutic interventions. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE PAGES

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

2015-05-15

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

PubMed

Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

2013-12-23

Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

Predictive model of thrombospondin-1 and vascular endothelial growth factor in breast tumor tissue.

PubMed

Rohrs, Jennifer A; Sulistio, Christopher D; Finley, Stacey D

2016-01-01

Angiogenesis, the formation of new blood capillaries from pre-existing vessels, is a hallmark of cancer. Thus far, strategies for reducing tumor angiogenesis have focused on inhibiting pro-angiogenic factors, while less is known about the therapeutic effects of mimicking the actions of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important endogenous inhibitor of angiogenesis that has been investigated as an anti-angiogenic agent. TSP1 impedes the growth of new blood vessels in many ways, including crosstalk with pro-angiogenic factors. Due to the complexity of TSP1 signaling, a predictive systems biology model would provide quantitative understanding of the angiogenic balance in tumor tissue. Therefore, we have developed a molecular-detailed, mechanistic model of TSP1 and vascular endothelial growth factor (VEGF), a promoter of angiogenesis, in breast tumor tissue. The model predicts the distribution of the angiogenic factors in tumor tissue, revealing that TSP1 is primarily in an inactive, cleaved form due to the action of proteases, rather than bound to its cellular receptors or to VEGF. The model also predicts the effects of enhancing TSP1's interactions with its receptors and with VEGF. To provide additional predictions that can guide the development of new anti-angiogenic drugs, we simulate administration of exogenous TSP1 mimetics that bind specific targets. The model predicts that the CD47-binding TSP1 mimetic dramatically decreases the ratio of receptor-bound VEGF to receptor-bound TSP1, in favor of anti-angiogenesis. Thus, we have established a model that provides a quantitative framework to study the response to TSP1 mimetics.

Accurate perception of negative emotions predicts functional capacity in schizophrenia.

PubMed

Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

2014-04-30

Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Microwave thermal ablation: Effects of tissue properties variations on predictive models for treatment planning.

PubMed

Lopresto, Vanni; Pinto, Rosanna; Farina, Laura; Cavagnaro, Marta

2017-08-01

Microwave thermal ablation (MTA) therapy for cancer treatments relies on the absorption of electromagnetic energy at microwave frequencies to induce a very high and localized temperature increase, which causes an irreversible thermal damage in the target zone. Treatment planning in MTA is based on experimental observations of ablation zones in ex vivo tissue, while predicting the treatment outcomes could be greatly improved by reliable numerical models. In this work, a fully dynamical simulation model is exploited to look at effects of temperature-dependent variations in the dielectric and thermal properties of the targeted tissue on the prediction of the temperature increase and the extension of the thermally coagulated zone. In particular, the influence of measurement uncertainty of tissue parameters on the numerical results is investigated. Numerical data were compared with data from MTA experiments performed on ex vivo bovine liver tissue at 2.45GHz, with a power of 60W applied for 10min. By including in the simulation model an uncertainty budget (CI=95%) of ±25% in the properties of the tissue due to inaccuracy of measurements, numerical results were achieved in the range of experimental data. Obtained results also showed that the specific heat especially influences the extension of the thermally coagulated zone, with an increase of 27% in length and 7% in diameter when a variation of -25% is considered with respect to the value of the reference simulation model. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

PubMed Central

2014-01-01

Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

NASA Astrophysics Data System (ADS)

Rahmati, Mehdi

2017-08-01

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

Sensitivity and specificity of procalcitonin in predicting bacterial infections in patients with renal impairment.

PubMed

El-Sayed, Dena; Grotts, Jonathan; Golgert, William A; Sugar, Alan M

2014-09-01

It is unclear whether procalcitonin is an accurate predictor of bacterial infections in patients with renal impairment, although it is used as a biomarker for early diagnosis of sepsis. We determined the sensitivity, specificity, positive and negative predictive values, accuracy and best predictive value of procalcitonin for predicting bacterial infection in adult patients with severe renal impairment. Retrospective study at a single-center community teaching hospital involving 473 patients, ages 18-65, with Modification of Diet in Renal Disease eGFR ≤30 ml/min per 1.73 m(2), admitted between January 2009 and June 2012, with 660 independent hospital visits. A positive or negative culture (blood or identifiable focus of infection) was paired to the highest procalcitonin result performed 48 hours before or after collecting the culture. The sensitivity and specificity to predict bacterial infection, using a procalcitonin level threshold of 0.5 ng/mL, was 0.80 and 0.35 respectively. When isolating for presence of bacteremia, the sensitivity and specificity were 0.89 and 0.35 respectively. An equation adjusting for optimum thresholds of procalcitonin levels for predicting bacterial infection at different levels of eGFR had a sensitivity and specificity of 0.55 and 0.80 respectively. Procalcitonin is not a reliably sensitive or specific predictor of bacterial infection in patients with renal impairment when using a single threshold. Perhaps two thresholds should be employed, where below the lower threshold (i.e. 0.5 ng/mL) bacterial infection is unlikely with a sensitivity of 0.80, and above the higher threshold (i.e. 3.2 ng/mL) bacterial infection is very likely with a specificity of 0.75.

Cell-Type-Specific Predictive Network Yields Novel Insights into Mouse Embryonic Stem Cell Self-Renewal and Cell Fate

PubMed Central

Dowell, Karen G.; Simons, Allen K.; Wang, Zack Z.; Yun, Kyuson; Hibbs, Matthew A.

2013-01-01

Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation. PMID:23468881

Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

PubMed

Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

2018-02-01

The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

Orbital and maxillofacial computer aided surgery: patient-specific finite element models to predict surgical outcomes.

PubMed

Luboz, Vincent; Chabanas, Matthieu; Swider, Pascal; Payan, Yohan

2005-08-01

This paper addresses an important issue raised for the clinical relevance of Computer-Assisted Surgical applications, namely the methodology used to automatically build patient-specific finite element (FE) models of anatomical structures. From this perspective, a method is proposed, based on a technique called the mesh-matching method, followed by a process that corrects mesh irregularities. The mesh-matching algorithm generates patient-specific volume meshes from an existing generic model. The mesh regularization process is based on the Jacobian matrix transform related to the FE reference element and the current element. This method for generating patient-specific FE models is first applied to computer-assisted maxillofacial surgery, and more precisely, to the FE elastic modelling of patient facial soft tissues. For each patient, the planned bone osteotomies (mandible, maxilla, chin) are used as boundary conditions to deform the FE face model, in order to predict the aesthetic outcome of the surgery. Seven FE patient-specific models were successfully generated by our method. For one patient, the prediction of the FE model is qualitatively compared with the patient's post-operative appearance, measured from a computer tomography scan. Then, our methodology is applied to computer-assisted orbital surgery. It is, therefore, evaluated for the generation of 11 patient-specific FE poroelastic models of the orbital soft tissues. These models are used to predict the consequences of the surgical decompression of the orbit. More precisely, an average law is extrapolated from the simulations carried out for each patient model. This law links the size of the osteotomy (i.e. the surgical gesture) and the backward displacement of the eyeball (the consequence of the surgical gesture).

Learning Instance-Specific Predictive Models

PubMed Central

Visweswaran, Shyam; Cooper, Gregory F.

2013-01-01

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable models to average over. We call this method the instance-specific Markov blanket (ISMB) algorithm. The ISMB algorithm was evaluated on 21 UCI data sets using five different performance measures and its performance was compared to that of several commonly used predictive algorithms, including nave Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor, Lazy Bayesian Rules, and AdaBoost. Over all the data sets, the ISMB algorithm performed better on average on all performance measures against all the comparison algorithms. PMID:25045325

Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.

PubMed

Uttam, Shikhar; Pham, Hoa V; LaFace, Justin; Leibowitz, Brian; Yu, Jian; Brand, Randall E; Hartman, Douglas J; Liu, Yang

2015-11-15

Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here, we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict "future" cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 of 15 patients who eventually developed colon cancer and 15 of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk and suggest that further validation in a multicenter study with larger cohorts may eventually advance this method to become a routine clinical test. ©2015 American Association for Cancer Research.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

PubMed

Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

2016-01-25

Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in <1h compared to >3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required. Copyright © 2015 Elsevier Ltd. All rights reserved.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Tissue-specific patterns of allelically-skewed DNA methylation

PubMed Central

Marzi, Sarah J.; Meaburn, Emma L.; Dempster, Emma L.; Lunnon, Katie; Paya-Cano, Jose L.; Smith, Rebecca G.; Volta, Manuela; Troakes, Claire; Schalkwyk, Leonard C.; Mill, Jonathan

2016-01-01

ABSTRACT While DNA methylation is usually thought to be symmetrical across both alleles, there are some notable exceptions. Genomic imprinting and X chromosome inactivation are two well-studied sources of allele-specific methylation (ASM), but recent research has indicated a more complex pattern in which genotypic variation can be associated with allelically-skewed DNA methylation in cis. Given the known heterogeneity of DNA methylation across tissues and cell types we explored inter- and intra-individual variation in ASM across several regions of the human brain and whole blood from multiple individuals. Consistent with previous studies, we find widespread ASM with > 4% of the ∼220,000 loci interrogated showing evidence of allelically-skewed DNA methylation. We identify ASM flanking known imprinted regions, and show that ASM sites are enriched in DNase I hypersensitivity sites and often located in an extended genomic context of intermediate DNA methylation. We also detect examples of genotype-driven ASM, some of which are tissue-specific. These findings contribute to our understanding of the nature of differential DNA methylation across tissues and have important implications for genetic studies of complex disease. As a resource to the community, ASM patterns across each of the tissues studied are available in a searchable online database: http://epigenetics.essex.ac.uk/ASMBrainBlood. PMID:26786711

Sensitivity and Specificity of Procalcitonin in Predicting Bacterial Infections in Patients With Renal Impairment

PubMed Central

El-sayed, Dena; Grotts, Jonathan; Golgert, William A.; Sugar, Alan M.

2014-01-01

Background  It is unclear whether procalcitonin is an accurate predictor of bacterial infections in patients with renal impairment, although it is used as a biomarker for early diagnosis of sepsis. We determined the sensitivity, specificity, positive and negative predictive values, accuracy and best predictive value of procalcitonin for predicting bacterial infection in adult patients with severe renal impairment. Methods  Retrospective study at a single-center community teaching hospital involving 473 patients, ages 18–65, with Modification of Diet in Renal Disease eGFR ≤30 ml/min per 1.73 m2, admitted between January 2009 and June 2012, with 660 independent hospital visits. A positive or negative culture (blood or identifiable focus of infection) was paired to the highest procalcitonin result performed 48 hours before or after collecting the culture. Results  The sensitivity and specificity to predict bacterial infection, using a procalcitonin level threshold of 0.5 ng/mL, was 0.80 and 0.35 respectively. When isolating for presence of bacteremia, the sensitivity and specificity were 0.89 and 0.35 respectively. An equation adjusting for optimum thresholds of procalcitonin levels for predicting bacterial infection at different levels of eGFR had a sensitivity and specificity of 0.55 and 0.80 respectively. Conclusions  Procalcitonin is not a reliably sensitive or specific predictor of bacterial infection in patients with renal impairment when using a single threshold. Perhaps two thresholds should be employed, where below the lower threshold (i.e. 0.5 ng/mL) bacterial infection is unlikely with a sensitivity of 0.80, and above the higher threshold (i.e. 3.2 ng/mL) bacterial infection is very likely with a specificity of 0.75. PMID:25734138

Prediction Equation for Lower Limbs Lean Soft Tissue in Circumpubertal Boys Using Anthropometry and Biological Maturation

PubMed Central

Valente-dos-Santos, João; Coelho-e-Silva, Manuel J.; Machado-Rodrigues, Aristides M.; Elferink-Gemser, Marije T.; Malina, Robert M.; Petroski, Édio L.; Minderico, Cláudia S.; Silva, Analiza M.; Baptista, Fátima; Sardinha, Luís B.

2014-01-01

Lean soft tissue (LST), a surrogate of skeletal muscle mass, is largely limited to appendicular body regions. Simple and accurate methods to estimate lower limbs LST are often used in attempts to partition out the influence of body size on performance outputs. The aim of the current study was to develop and cross-validate a new model to predict lower limbs LST in boys aged 10–13 years, using dual-energy X-ray absorptiometry (DXA) as the reference method. Total body and segmental (lower limbs) composition were assessed with a Hologic Explorer-W QDR DXA scanner in a cross-sectional sample of 75 Portuguese boys (144.8±6.4 cm; 40.2±9.0 kg). Skinfolds were measured at the anterior and posterior mid-thigh, and medial calf. Circumferences were measured at the proximal, mid and distal thigh. Leg length was estimated as stature minus sitting height. Current stature expressed as a percentage of attained predicted mature stature (PMS) was used as an estimate of biological maturity status. Backward proportional allometric models were used to identify the model with the best statistical fit: ln (lower limbs LST)  = 0.838× ln (body mass) +0.476× ln (leg length) – 0.135× ln (mid-thigh circumference) – 0.053× ln (anterior mid-thigh skinfold) – 0.098× ln (medial calf skinfold) – 2.680+0.010× (percentage of attained PMS) (R = 0.95). The obtained equation was cross-validated using the predicted residuals sum of squares statistics (PRESS) method (R 2 PRESS = 0.90). Deming repression analysis between predicted and current lower limbs LST showed a standard error of estimation of 0.52 kg (95% limits of agreement: 0.77 to −1.27 kg). The new model accurately predicts lower limbs LST in circumpubertal boys. PMID:25229472

The reliability, validity, sensitivity, specificity and predictive values of the Chinese version of the Rowland Universal Dementia Assessment Scale.

PubMed

Chen, Chia-Wei; Chu, Hsin; Tsai, Chia-Fen; Yang, Hui-Ling; Tsai, Jui-Chen; Chung, Min-Huey; Liao, Yuan-Mei; Chi, Mei-Ju; Chou, Kuei-Ru

2015-11-01

The purpose of this study was to translate the Rowland Universal Dementia Assessment Scale into Chinese and to evaluate the psychometric properties (reliability and validity) and the diagnostic properties (sensitivity, specificity and predictive values) of the Chinese version of the Rowland Universal Dementia Assessment Scale. The accurate detection of early dementia requires screening tools with favourable cross-cultural linguistic and appropriate sensitivity, specificity, and predictive values, particularly for Chinese-speaking populations. This was a cross-sectional, descriptive study. Overall, 130 participants suspected to have cognitive impairment were enrolled in the study. A test-retest for determining reliability was scheduled four weeks after the initial test. Content validity was determined by five experts, whereas construct validity was established by using contrasted group technique. The participants' clinical diagnoses were used as the standard in calculating the sensitivity, specificity, positive predictive value and negative predictive value. The study revealed that the Chinese version of the Rowland Universal Dementia Assessment Scale exhibited a test-retest reliability of 0.90, an internal consistency reliability of 0.71, an inter-rater reliability (kappa value) of 0.88 and a content validity index of 0.97. Both the patients and healthy contrast group exhibited significant differences in their cognitive ability. The optimal cut-off points for the Chinese version of the Rowland Universal Dementia Assessment Scale in the test for mild cognitive impairment and dementia were 24 and 22, respectively; moreover, for these two conditions, the sensitivities of the scale were 0.79 and 0.76, the specificities were 0.91 and 0.81, the areas under the curve were 0.85 and 0.78, the positive predictive values were 0.99 and 0.83 and the negative predictive values were 0.96 and 0.91 respectively. The Chinese version of the Rowland Universal Dementia Assessment Scale

Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

NASA Astrophysics Data System (ADS)

Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

2017-07-01

A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

A family of tissue-specific resistin-like molecules

PubMed Central

Steppan, Claire M.; Brown, Elizabeth J.; Wright, Christopher M.; Bhat, Savitha; Banerjee, Ronadip R.; Dai, Charlotte Y.; Enders, Gregory H.; Silberg, Debra G.; Wen, Xiaoming; Wu, Gary D.; Lazar, Mitchell A.

2001-01-01

We have identified a family of resistin-like molecules (RELMs) in rodents and humans. Resistin is a hormone produced by fat cells. RELMα is a secreted protein that has a restricted tissue distribution with highest levels in adipose tissue. Another family member, RELMβ, is a secreted protein expressed only in the gastrointestinal tract, particularly the colon, in both mouse and human. RELMβ gene expression is highest in proliferative epithelial cells and is markedly increased in tumors, suggesting a role in intestinal proliferation. Resistin and the RELMs share a cysteine composition and other signature features. Thus, the RELMs together with resistin comprise a class of tissue-specific signaling molecules. PMID:11209052

A family of tissue-specific resistin-like molecules.

PubMed

Steppan, C M; Brown, E J; Wright, C M; Bhat, S; Banerjee, R R; Dai, C Y; Enders, G H; Silberg, D G; Wen, X; Wu, G D; Lazar, M A

2001-01-16

We have identified a family of resistin-like molecules (RELMs) in rodents and humans. Resistin is a hormone produced by fat cells. RELMalpha is a secreted protein that has a restricted tissue distribution with highest levels in adipose tissue. Another family member, RELMbeta, is a secreted protein expressed only in the gastrointestinal tract, particularly the colon, in both mouse and human. RELMbeta gene expression is highest in proliferative epithelial cells and is markedly increased in tumors, suggesting a role in intestinal proliferation. Resistin and the RELMs share a cysteine composition and other signature features. Thus, the RELMs together with resistin comprise a class of tissue-specific signaling molecules.

Tissue-Specific Venom Composition and Differential Gene Expression in Sea Anemones

PubMed Central

Macrander, Jason; Broe, Michael; Daly, Marymegan

2016-01-01

Cnidarians represent one of the few groups of venomous animals that lack a centralized venom transmission system. Instead, they are equipped with stinging capsules collectively known as nematocysts. Nematocysts vary in abundance and type across different tissues; however, the venom composition in most species remains unknown. Depending on the tissue type, the venom composition in sea anemones may be vital for predation, defense, or digestion. Using a tissue-specific RNA-seq approach, we characterize the venom assemblage in the tentacles, mesenterial filaments, and column for three species of sea anemone (Anemonia sulcata, Heteractis crispa, and Megalactis griffithsi). These taxa vary with regard to inferred venom potency, symbiont abundance, and nematocyst diversity. We show that there is significant variation in abundance of toxin-like genes across tissues and species. Although the cumulative toxin abundance for the column was consistently the lowest, contributions to the overall toxin assemblage varied considerably among tissues for different toxin types. Our gene ontology (GO) analyses also show sharp contrasts between conserved GO groups emerging from whole transcriptome analysis and tissue-specific expression among GO groups in our differential expression analysis. This study provides a framework for future characterization of tissue-specific venom and other functionally important genes in this lineage of simple bodied animals. PMID:27389690

CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva.

PubMed

van Dijk, Lisanne V; Brouwer, Charlotte L; van der Schaaf, Arjen; Burgerhof, Johannes G M; Beukinga, Roelof J; Langendijk, Johannes A; Sijtsema, Nanna M; Steenbakkers, Roel J H M

2017-02-01

Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER 12m ) and sticky saliva (STIC 12m ) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XER base ) or sticky saliva (STIC base ) scores. The purpose is to improve prediction of XER 12m and STIC 12m with patient-specific characteristics, based on CT image biomarkers (IBMs). Planning CT-scans and patient-rated outcome measures were prospectively collected for 249 head and neck cancer patients treated with definitive radiotherapy with or without systemic treatment. The potential IBMs represent geometric, CT intensity and textural characteristics of the parotid and submandibular glands. Lasso regularisation was used to create multivariable logistic regression models, which were internally validated by bootstrapping. The prediction of XER 12m could be improved significantly by adding the IBM "Short Run Emphasis" (SRE), which quantifies heterogeneity of parotid tissue, to a model with mean contra-lateral parotid gland dose and XER base . For STIC 12m , the IBM maximum CT intensity of the submandibular gland was selected in addition to STIC base and mean dose to submandibular glands. Prediction of XER 12m and STIC 12m was improved by including IBMs representing heterogeneity and density of the salivary glands, respectively. These IBMs could guide additional research to the patient-specific response of healthy tissue to radiation dose. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, Zhenhua; Chan, Maria K. Y.; Zhao, Zhi-Jian

2015-08-13

Electrochemical potential/pH (Pourbaix) diagrams underpin many aqueous electrochemical processes and are central to the identification of stable phases of metals for processes ranging from electrocatalysis to corrosion. Even though standard DFT calculations are potentially powerful tools for the prediction of such diagrams, inherent errors in the description of transition metal (hydroxy)oxides, together with neglect of van der Waals interactions, have limited the reliability of such predictions for even the simplest pure metal bulk compounds, and corresponding predictions for more complex alloy or surface structures are even more challenging. In the present work, through synergistic use of a Hubbard U correction,more » a state-of-the-art dispersion correction, and a water-based bulk reference state for the calculations, these errors are systematically corrected. The approach describes the weak binding that occurs between hydroxyl-containing functional groups in certain compounds in Pourbaix diagrams, corrects for self-interaction errors in transition metal compounds, and reduces residual errors on oxygen atoms by preserving a consistent oxidation state between the reference state, water, and the relevant bulk phases. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxides, oxyhydroxides, binary, and ternary oxides, where the corresponding thermodynamics of redox and (de)hydration are described with standard errors of 0.04 eV per (reaction) formula unit. The approach further preserves accurate descriptions of the overall thermodynamics of electrochemically-relevant bulk reactions, such as water formation, which is an essential condition for facilitating accurate analysis of reaction energies for electrochemical processes on surfaces. The overall generality and transferability of the scheme suggests that it may find useful application in the construction of a broad array of electrochemical phase diagrams, including

Prostate specific antigen density to predict prostate cancer upgrading in a contemporary radical prostatectomy series: a single center experience.

PubMed

Magheli, Ahmed; Hinz, Stefan; Hege, Claudia; Stephan, Carsten; Jung, Klaus; Miller, Kurt; Lein, Michael

2010-01-01

We investigated the value of pretreatment prostate specific antigen density to predict Gleason score upgrading in light of significant changes in grading routine in the last 2 decades. Of 1,061 consecutive men who underwent radical prostatectomy between 1999 and 2004, 843 were eligible for study. Prostate specific antigen density was calculated and a cutoff for highest accuracy to predict Gleason upgrading was determined using ROC curve analysis. The predictive accuracy of prostate specific antigen and prostate specific antigen density to predict Gleason upgrading was evaluated using ROC curve analysis based on predicted probabilities from logistic regression models. Prostate specific antigen and prostate specific antigen density predicted Gleason upgrading on univariate analysis (as continuous variables OR 1.07 and 7.21, each p <0.001) and on multivariate analysis (as continuous variables with prostate specific antigen density adjusted for prostate specific antigen OR 1.07, p <0.001 and OR 4.89, p = 0.037, respectively). When prostate specific antigen density was added to the model including prostate specific antigen and other Gleason upgrading predictors, prostate specific antigen lost its predictive value (OR 1.02, p = 0.423), while prostate specific antigen density remained an independent predictor (OR 4.89, p = 0.037). Prostate specific antigen density was more accurate than prostate specific antigen to predict Gleason upgrading (AUC 0.61 vs 0.57, p = 0.030). Prostate specific antigen density is a significant independent predictor of Gleason upgrading even when accounting for prostate specific antigen. This could be especially important in patients with low risk prostate cancer who seek less invasive therapy such as active surveillance since potentially life threatening disease may be underestimated. Further studies are warranted to help evaluate the role of prostate specific antigen density in Gleason upgrading and its significance for biochemical outcome.

The prediction of blood-tissue partitions, water-skin partitions and skin permeation for agrochemicals.

PubMed

Abraham, Michael H; Gola, Joelle M R; Ibrahim, Adam; Acree, William E; Liu, Xiangli

2014-07-01

There is considerable interest in the blood-tissue distribution of agrochemicals, and a number of researchers have developed experimental methods for in vitro distribution. These methods involve the determination of saline-blood and saline-tissue partitions; not only are they indirect, but they do not yield the required in vivo distribution. The authors set out equations for gas-tissue and blood-tissue distribution, for partition from water into skin and for permeation from water through human skin. Together with Abraham descriptors for the agrochemicals, these equations can be used to predict values for all of these processes. The present predictions compare favourably with experimental in vivo blood-tissue distribution where available. The predictions require no more than simple arithmetic. The present method represents a much easier and much more economic way of estimating blood-tissue partitions than the method that uses saline-blood and saline-tissue partitions. It has the added advantages of yielding the required in vivo partitions and being easily extended to the prediction of partition of agrochemicals from water into skin and permeation from water through skin. © 2013 Society of Chemical Industry.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

PubMed

Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

2015-07-05

Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

PubMed

Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

2016-06-01

In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

PubMed Central

Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

2016-01-01

In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

Rapid and accurate peripheral nerve detection using multipoint Raman imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kumamoto, Yasuaki; Minamikawa, Takeo; Kawamura, Akinori; Matsumura, Junichi; Tsuda, Yuichiro; Ukon, Juichiro; Harada, Yoshinori; Tanaka, Hideo; Takamatsu, Tetsuro

2017-02-01

Nerve-sparing surgery is essential to avoid functional deficits of the limbs and organs. Raman scattering, a label-free, minimally invasive, and accurate modality, is one of the best candidate technologies to detect nerves for nerve-sparing surgery. However, Raman scattering imaging is too time-consuming to be employed in surgery. Here we present a rapid and accurate nerve visualization method using a multipoint Raman imaging technique that has enabled simultaneous spectra measurement from different locations (n=32) of a sample. Five sec is sufficient for measuring n=32 spectra with good S/N from a given tissue. Principal component regression discriminant analysis discriminated spectra obtained from peripheral nerves (n=863 from n=161 myelinated nerves) and connective tissue (n=828 from n=121 tendons) with sensitivity and specificity of 88.3% and 94.8%, respectively. To compensate the spatial information of a multipoint-Raman-derived tissue discrimination image that is too sparse to visualize nerve arrangement, we used morphological information obtained from a bright-field image. When merged with the sparse tissue discrimination image, a morphological image of a sample shows what portion of Raman measurement points in arbitrary structure is determined as nerve. Setting a nerve detection criterion on the portion of "nerve" points in the structure as 40% or more, myelinated nerves (n=161) and tendons (n=121) were discriminated with sensitivity and specificity of 97.5%. The presented technique utilizing a sparse multipoint Raman image and a bright-field image has enabled rapid, safe, and accurate detection of peripheral nerves.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

PubMed

Wittek, Adam; Joldes, Grand; Couton, Mathieu; Warfield, Simon K; Miller, Karol

2010-12-01

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer. Copyright © 2010 Elsevier Ltd. All rights reserved.

Optimization and real-time control for laser treatment of heterogeneous soft tissues.

PubMed

Feng, Yusheng; Fuentes, David; Hawkins, Andrea; Bass, Jon M; Rylander, Marissa Nichole

2009-01-01

Predicting the outcome of thermotherapies in cancer treatment requires an accurate characterization of the bioheat transfer processes in soft tissues. Due to the biological and structural complexity of tumor (soft tissue) composition and vasculature, it is often very difficult to obtain reliable tissue properties that is one of the key factors for the accurate treatment outcome prediction. Efficient algorithms employing in vivo thermal measurements to determine heterogeneous thermal tissues properties in conjunction with a detailed sensitivity analysis can produce essential information for model development and optimal control. The goals of this paper are to present a general formulation of the bioheat transfer equation for heterogeneous soft tissues, review models and algorithms developed for cell damage, heat shock proteins, and soft tissues with nanoparticle inclusion, and demonstrate an overall computational strategy for developing a laser treatment framework with the ability to perform real-time robust calibrations and optimal control. This computational strategy can be applied to other thermotherapies using the heat source such as radio frequency or high intensity focused ultrasound.

High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

NASA Astrophysics Data System (ADS)

Chen, Y.; Toth, G.; Gombosi, T. I.

2014-12-01

BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

DNA methylation-based age prediction from various tissues and body fluids

PubMed Central

Jung, Sang-Eun; Shin, Kyoung-Jin; Lee, Hwan Young

2017-01-01

Aging is a natural and gradual process in human life. It is influenced by heredity, environment, lifestyle, and disease. DNA methylation varies with age, and the ability to predict the age of donor using DNA from evidence materials at a crime scene is of considerable value in forensic investigations. Recently, many studies have reported age prediction models based on DNA methylation from various tissues and body fluids. Those models seem to be very promising because of their high prediction accuracies. In this review, the changes of age-associated DNA methylation and the age prediction models for various tissues and body fluids were examined, and then the applicability of the DNA methylation-based age prediction method to the forensic investigations was discussed. This will improve the understandings about DNA methylation markers and their potential to be used as biomarkers in the forensic field, as well as the clinical field. PMID:28946940

Yki/YAP, Sd/TEAD and Hth/MEIS Control Tissue Specification in the Drosophila Eye Disc Epithelium

PubMed Central

Pignoni, Francesca

2011-01-01

During animal development, accurate control of tissue specification and growth are critical to generate organisms of reproducible shape and size. The eye-antennal disc epithelium of Drosophila is a powerful model system to identify the signaling pathway and transcription factors that mediate and coordinate these processes. We show here that the Yorkie (Yki) pathway plays a major role in tissue specification within the developing fly eye disc epithelium at a time when organ primordia and regional identity domains are specified. RNAi-mediated inactivation of Yki, or its partner Scalloped (Sd), or increased activity of the upstream negative regulators of Yki cause a dramatic reorganization of the eye disc fate map leading to specification of the entire disc epithelium into retina. On the contrary, constitutive expression of Yki suppresses eye formation in a Sd-dependent fashion. We also show that knockdown of the transcription factor Homothorax (Hth), known to partner Yki in some developmental contexts, also induces an ectopic retina domain, that Yki and Scalloped regulate Hth expression, and that the gain-of-function activity of Yki is partially dependent on Hth. Our results support a critical role for Yki- and its partners Sd and Hth - in shaping the fate map of the eye epithelium independently of its universal role as a regulator of proliferation and survival. PMID:21811580

Tissue-specific exosome biomarkers for noninvasively monitoring immunologic rejection of transplanted tissue

PubMed Central

Korutla, Laxminarayana; Habertheuer, Andreas; Yu, Ming; Rostami, Susan; Yuan, Chao-Xing; Reddy, Sanjana; Korutla, Varun; Koeberlein, Brigitte; Trofe-Clark, Jennifer; Rickels, Michael R.; Naji, Ali

2017-01-01

In transplantation, there is a critical need for noninvasive biomarker platforms for monitoring immunologic rejection. We hypothesized that transplanted tissues release donor-specific exosomes into recipient circulation and that the quantitation and profiling of donor intra-exosomal cargoes may constitute a biomarker platform for monitoring rejection. Here, we have tested this hypothesis in a human-into-mouse xenogeneic islet transplant model and validated the concept in clinical settings of islet and renal transplantation. In the xenogeneic model, we quantified islet transplant exosomes in recipient blood over long-term follow-up using anti-HLA antibody, which was detectable only in xenoislet recipients of human islets. Transplant islet exosomes were purified using anti-HLA antibody–conjugated beads, and their cargoes contained the islet endocrine hormone markers insulin, glucagon, and somatostatin. Rejection led to a marked decrease in transplant islet exosome signal along with distinct changes in exosomal microRNA and proteomic profiles prior to appearance of hyperglycemia. In the clinical settings of islet and renal transplantation, donor exosomes with respective tissue specificity for islet β cells and renal epithelial cells were reliably characterized in recipient plasma over follow-up periods of up to 5 years. Collectively, these findings demonstrate the biomarker potential of transplant exosome characterization for providing a noninvasive window into the conditional state of transplant tissue. PMID:28319051

Deoxynucleoside salvage enzymes and tissue specific mitochondrial DNA depletion.

PubMed

Wang, L

2010-06-01

Adequate mitochondrial DNA (mtDNA) copies are required for normal mitochondria function and reductions in mtDNA copy number due to genetic alterations cause tissue-specific mtDNA depletion syndrome (MDS). There are eight nuclear genes, directly or indirectly involved in mtDNA replication and mtDNA precursor synthesis, which have been identified as the cause of MDS. However, the tissue specific pathology of these nuclear gene mutations is not well understood. Here, mtDNA synthesis, mtDNA copy number control, and mtDNA turnover, as well as the synthesis of mtDNA precursors in relation to the levels of salvage enzymes are discussed. The question why MDS caused by TK2 and p53R2 mutations are predominantly muscle specific while dGK deficiency affected mainly liver will be addressed.

RBFOX2 Is an Important Regulator of Mesenchymal Tissue-Specific Splicing in both Normal and Cancer Tissues

PubMed Central

Venables, Julian P.; Brosseau, Jean-Philippe; Gadea, Gilles; Klinck, Roscoe; Prinos, Panagiotis; Beaulieu, Jean-François; Lapointe, Elvy; Durand, Mathieu; Thibault, Philippe; Tremblay, Karine; Rousset, François; Tazi, Jamal; Abou Elela, Sherif

2013-01-01

Alternative splicing provides a critical and flexible layer of regulation intervening in many biological processes to regulate the diversity of proteins and impact cell phenotype. To identify alternative splicing differences that distinguish epithelial from mesenchymal tissues, we have investigated hundreds of cassette exons using a high-throughput reverse transcription-PCR (RT-PCR) platform. Extensive changes in splicing were noted between epithelial and mesenchymal tissues in both human colon and ovarian tissues, with many changes from mostly one splice variant to predominantly the other. Remarkably, many of the splicing differences that distinguish normal mesenchymal from normal epithelial tissues matched those that differentiate normal ovarian tissues from ovarian cancer. Furthermore, because splicing profiling could classify cancer cell lines according to their epithelial/mesenchymal characteristics, we used these cancer cell lines to identify regulators for these specific splicing signatures. By knocking down 78 potential splicing factors in five cell lines, we provide an extensive view of the complex regulatory landscape associated with the epithelial and mesenchymal states, thus revealing that RBFOX2 is an important driver of mesenchymal tissue-specific splicing. PMID:23149937

Mechanistic, Mathematical Model to Predict the Dynamics of Tissue Genesis in Bone Defects via Mechanical Feedback and Mediation of Biochemical Factors

PubMed Central

Moore, Shannon R.; Saidel, Gerald M.; Knothe, Ulf; Knothe Tate, Melissa L.

2014-01-01

The link between mechanics and biology in the generation and the adaptation of bone has been well studied in context of skeletal development and fracture healing. Yet, the prediction of tissue genesis within - and the spatiotemporal healing of - postnatal defects, necessitates a quantitative evaluation of mechano-biological interactions using experimental and clinical parameters. To address this current gap in knowledge, this study aims to develop a mechanistic mathematical model of tissue genesis using bone morphogenetic protein (BMP) to represent of a class of factors that may coordinate bone healing. Specifically, we developed a mechanistic, mathematical model to predict the dynamics of tissue genesis by periosteal progenitor cells within a long bone defect surrounded by periosteum and stabilized via an intramedullary nail. The emergent material properties and mechanical environment associated with nascent tissue genesis influence the strain stimulus sensed by progenitor cells within the periosteum. Using a mechanical finite element model, periosteal surface strains are predicted as a function of emergent, nascent tissue properties. Strains are then input to a mechanistic mathematical model, where mechanical regulation of BMP-2 production mediates rates of cellular proliferation, differentiation and tissue production, to predict healing outcomes. A parametric approach enables the spatial and temporal prediction of endochondral tissue regeneration, assessed as areas of cartilage and mineralized bone, as functions of radial distance from the periosteum and time. Comparing model results to histological outcomes from two previous studies of periosteum-mediated bone regeneration in a common ovine model, it was shown that mechanistic models incorporating mechanical feedback successfully predict patterns (spatial) and trends (temporal) of bone tissue regeneration. The novel model framework presented here integrates a mechanistic feedback system based on the

A Course Specific Perspective in the Prediction of Academic Success.

ERIC Educational Resources Information Center

Beaulieu, R. P.

1990-01-01

Students (N=94) enrolled in a senior-level management course over six semesters were used to investigate the ability of four measures from two industrial tests to predict course performance. The resulting multiple regression equation with four predictors could accurately predict achievement of males, but not of females. (Author/TE)

Accurate high-throughput structure mapping and prediction with transition metal ion FRET

PubMed Central

Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

2013-01-01

Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.

PubMed

Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2008-06-01

Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

Computational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Data.

PubMed

Badr, Eman; ElHefnawi, Mahmoud; Heath, Lenwood S

2016-01-01

Alternative splicing is a vital process for regulating gene expression and promoting proteomic diversity. It plays a key role in tissue-specific expressed genes. This specificity is mainly regulated by splicing factors that bind to specific sequences called splicing regulatory elements (SREs). Here, we report a genome-wide analysis to study alternative splicing on multiple tissues, including brain, heart, liver, and muscle. We propose a pipeline to identify differential exons across tissues and hence tissue-specific SREs. In our pipeline, we utilize the DEXSeq package along with our previously reported algorithms. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, we identified 28,100 differentially used exons across the four tissues. We identified tissue-specific exonic splicing enhancers that overlap with various previously published experimental and computational databases. A complicated exonic enhancer regulatory network was revealed, where multiple exonic enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the exonic enhancers are found to be co-occurring with multiple exonic silencers and vice versa, which demonstrates a complicated relationship between tissue-specific exonic enhancers and silencers.

The tissue microarray data exchange specification: A community-based, open source tool for sharing tissue microarray data

PubMed Central

Berman, Jules J; Edgerton, Mary E; Friedman, Bruce A

2003-01-01

Background Tissue Microarrays (TMAs) allow researchers to examine hundreds of small tissue samples on a single glass slide. The information held in a single TMA slide may easily involve Gigabytes of data. To benefit from TMA technology, the scientific community needs an open source TMA data exchange specification that will convey all of the data in a TMA experiment in a format that is understandable to both humans and computers. A data exchange specification for TMAs allows researchers to submit their data to journals and to public data repositories and to share or merge data from different laboratories. In May 2001, the Association of Pathology Informatics (API) hosted the first in a series of four workshops, co-sponsored by the National Cancer Institute, to develop an open, community-supported TMA data exchange specification. Methods A draft tissue microarray data exchange specification was developed through workshop meetings. The first workshop confirmed community support for the effort and urged the creation of an open XML-based specification. This was to evolve in steps with approval for each step coming from the stakeholders in the user community during open workshops. By the fourth workshop, held October, 2002, a set of Common Data Elements (CDEs) was established as well as a basic strategy for organizing TMA data in self-describing XML documents. Results The TMA data exchange specification is a well-formed XML document with four required sections: 1) Header, containing the specification Dublin Core identifiers, 2) Block, describing the paraffin-embedded array of tissues, 3)Slide, describing the glass slides produced from the Block, and 4) Core, containing all data related to the individual tissue samples contained in the array. Eighty CDEs, conforming to the ISO-11179 specification for data elements constitute XML tags used in the TMA data exchange specification. A set of six simple semantic rules describe the complete data exchange specification. Anyone

Magnetic Resonance Imaging Predicts Adverse Local Tissue Reaction Histologic Severity in Modular Neck Total Hip Arthroplasty.

PubMed

Barlow, Brian T; Ortiz, Philippe A; Fields, Kara G; Burge, Alissa J; Potter, Hollis G; Westrich, Geoffrey H

2016-10-01

The association between advanced imaging, serum metal ion levels, and histologic adverse local tissue reaction (ALTR) severity has not been previously reported for Rejuvenate modular neck femoral stems. A cohort of 90 patients with 98 Rejuvenate modular neck femoral stems was revised by a single surgeon from July 2011 to December 2014. Before revision, patients underwent multiacquisition variable resonance image combination sequence magnetic resonance imaging (MRI), and serum cobalt and chromium ion levels were measured. Histologic samples from the revision surgery were scored for synovial lining, inflammatory infiltrate, and tissue organization as proposed by Campbell. Regression based on the generalized estimating equations approach was used to assess the univariate association between each MRI, demographic, and metal ion measure and ALTR severity while accounting for the correlation between bilateral hips. Random forest analysis was then used to determine the relative importance of MRI characteristics, demographics, and metal ion levels in predicting ALTR severity. Synovial thickness as measured on MRI was found to be the strongest predictor of ALTR histologic severity in a recalled modular neck femoral stem. MRI can accurately describe ALTR in modular femoral neck total hip arthroplasty. MRI characteristics, particularly maximal synovial thickness and synovitis volume, predicted histologic severity. Serum metal ion levels do not correlate with histologic severity in Rejuvenate modular neck total hip arthroplasty. Copyright © 2016 Elsevier Inc. All rights reserved.

Tissue specific specialization of the nanoscale architecture of Arabidopsis.

PubMed

Liu, Jiliang; Inouye, Hideyo; Venugopalan, Nagarajan; Fischetti, Robert F; Gleber, S Charlotte; Vogt, Stefan; Cusumano, Joanne C; Kim, Jeong Im; Chapple, Clint; Makowski, Lee

2013-11-01

The Arabidopsis stem is composed of five tissues - the pith, xylem, phloem, cortex and epidermis - each of which fulfills specific roles in support of the growth and survival of the organism. The lignocellulosic scaffolding of cell walls is specialized to provide optimal support for the diverse functional roles of these layers, but little is known about this specialization. X-ray scattering can be used to study this tissue-specific diversity because the cellulosic components of the cell walls give rise to recognizable scattering features interpretable in terms of the underlying molecular architecture and distinct from the largely unoriented scatter from other constituents. Here we use scanning X-ray microdiffraction from thin sections to characterize the diversity of molecular architecture in the Arabidopsis stem and correlate that diversity to the functional roles the distinct tissues of the stem play in the growth and survival of the organism. Copyright © 2013. Published by Elsevier Inc.

Tissue shrinkage in microwave ablation of liver: an ex vivo predictive model.

PubMed

Amabile, Claudio; Farina, Laura; Lopresto, Vanni; Pinto, Rosanna; Cassarino, Simone; Tosoratti, Nevio; Goldberg, S Nahum; Cavagnaro, Marta

2017-02-01

The aim of this study was to develop a predictive model of the shrinkage of liver tissues in microwave ablation. Thirty-seven cuboid specimens of ex vivo bovine liver of size ranging from 2 cm to 8 cm were heated exploiting different techniques: 1) using a microwave oven (2.45 GHz) operated at 420 W, 500 W and 700 W for 8 to 20 min, achieving complete carbonisation of the specimens, 2) using a radiofrequency ablation apparatus (450 kHz) operated at 70 W for a time ranging from 6 to 7.5 min obtaining white coagulation of the specimens, and 3) using a microwave (2.45 GHz) ablation apparatus operated at 60 W for 10 min. Measurements of specimen dimensions, carbonised and coagulated regions were performed using a ruler with an accuracy of 1 mm. Based on the results of the first two experiments a predictive model for the contraction of liver tissue from microwave ablation was constructed and compared to the result of the third experiment. For carbonised tissue, a linear contraction of 31 ± 6% was obtained independently of the heating source, power and operation time. Radiofrequency experiments determined that the average percentage linear contraction of white coagulated tissue was 12 ± 5%. The average accuracy of our model was determined to be 3 mm (5%). The proposed model allows the prediction of the shrinkage of liver tissues upon microwave ablation given the extension of the carbonised and coagulated zones. This may be useful in helping to predict whether sufficient tissue volume is ablated in clinical practice.

Tissue-specifically regulated site-specific excision of selectable marker genes in bivalent insecticidal, genetically-modified rice.

PubMed

Hu, Zhan; Ding, Xuezhi; Hu, Shengbiao; Sun, Yunjun; Xia, Liqiu

2013-12-01

Marker-free, genetically-modified rice was created by the tissue-specifically regulated Cre/loxP system, in which the Cre recombinase gene and hygromycin phosphotransferase gene (hpt) were flanked by two directly oriented loxP sites. Cre expression was activated by the tissue-specific promoter OsMADS45 in flower or napin in seed, resulting in simultaneous excision of the recombinase and marker genes. Segregation of T1 progeny was performed to select recombined plants. The excision was confirmed by PCR, Southern blot and sequence analyses indicating that efficiency varied from 10 to 53 % for OsMADS45 and from 12 to 36 % for napin. The expression of cry1Ac and vip3A was detected by RT-PCR analysis in marker-free transgenic rice. These results suggested that our tissue-specifically regulated Cre/loxP system could auto-excise marker genes from transgenic rice and alleviate public concerns about the security of GM crops.

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

PubMed

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

PubMed

Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

2016-10-01

The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. © 2016 John Wiley & Sons Ltd.

Verification of predicted specimen-specific natural and implanted patellofemoral kinematics during simulated deep knee bend.

PubMed

Baldwin, Mark A; Clary, Chadd; Maletsky, Lorin P; Rullkoetter, Paul J

2009-10-16

Verified computational models represent an efficient method for studying the relationship between articular geometry, soft-tissue constraint, and patellofemoral (PF) mechanics. The current study was performed to evaluate an explicit finite element (FE) modeling approach for predicting PF kinematics in the natural and implanted knee. Experimental three-dimensional kinematic data were collected on four healthy cadaver specimens in their natural state and after total knee replacement in the Kansas knee simulator during a simulated deep knee bend activity. Specimen-specific FE models were created from medical images and CAD implant geometry, and included soft-tissue structures representing medial-lateral PF ligaments and the quadriceps tendon. Measured quadriceps loads and prescribed tibiofemoral kinematics were used to predict dynamic kinematics of an isolated PF joint between 10 degrees and 110 degrees femoral flexion. Model sensitivity analyses were performed to determine the effect of rigid or deformable patellar representations and perturbed PF ligament mechanical properties (pre-tension and stiffness) on model predictions and computational efficiency. Predicted PF kinematics from the deformable analyses showed average root mean square (RMS) differences for the natural and implanted states of less than 3.1 degrees and 1.7 mm for all rotations and translations. Kinematic predictions with rigid bodies increased average RMS values slightly to 3.7 degrees and 1.9 mm with a five-fold decrease in computational time. Two-fold increases and decreases in PF ligament initial strain and linear stiffness were found to most adversely affect kinematic predictions for flexion, internal-external tilt and inferior-superior translation in both natural and implanted states. The verified models could be used to further investigate the effects of component alignment or soft-tissue variability on natural and implant PF mechanics.

A flexible and accurate digital volume correlation method applicable to high-resolution volumetric images

NASA Astrophysics Data System (ADS)

Pan, Bing; Wang, Bo

2017-10-01

Digital volume correlation (DVC) is a powerful technique for quantifying interior deformation within solid opaque materials and biological tissues. In the last two decades, great efforts have been made to improve the accuracy and efficiency of the DVC algorithm. However, there is still a lack of a flexible, robust and accurate version that can be efficiently implemented in personal computers with limited RAM. This paper proposes an advanced DVC method that can realize accurate full-field internal deformation measurement applicable to high-resolution volume images with up to billions of voxels. Specifically, a novel layer-wise reliability-guided displacement tracking strategy combined with dynamic data management is presented to guide the DVC computation from slice to slice. The displacements at specified calculation points in each layer are computed using the advanced 3D inverse-compositional Gauss-Newton algorithm with the complete initial guess of the deformation vector accurately predicted from the computed calculation points. Since only limited slices of interest in the reference and deformed volume images rather than the whole volume images are required, the DVC calculation can thus be efficiently implemented on personal computers. The flexibility, accuracy and efficiency of the presented DVC approach are demonstrated by analyzing computer-simulated and experimentally obtained high-resolution volume images.

Evaluation of endogenous control genes for gene expression studies across multiple tissues and in the specific sets of fat- and muscle-type samples of the pig.

PubMed

Gu, Y R; Li, M Z; Zhang, K; Chen, L; Jiang, A A; Wang, J Y; Li, X W

2011-08-01

To normalize a set of quantitative real-time PCR (q-PCR) data, it is essential to determine an optimal number/set of housekeeping genes, as the abundance of housekeeping genes can vary across tissues or cells during different developmental stages, or even under certain environmental conditions. In this study, of the 20 commonly used endogenous control genes, 13, 18 and 17 genes exhibited credible stability in 56 different tissues, 10 types of adipose tissue and five types of muscle tissue, respectively. Our analysis clearly showed that three optimal housekeeping genes are adequate for an accurate normalization, which correlated well with the theoretical optimal number (r ≥ 0.94). In terms of economical and experimental feasibility, we recommend the use of the three most stable housekeeping genes for calculating the normalization factor. Based on our results, the three most stable housekeeping genes in all analysed samples (TOP2B, HSPCB and YWHAZ) are recommended for accurate normalization of q-PCR data. We also suggest that two different sets of housekeeping genes are appropriate for 10 types of adipose tissue (the HSPCB, ALDOA and GAPDH genes) and five types of muscle tissue (the TOP2B, HSPCB and YWHAZ genes), respectively. Our report will serve as a valuable reference for other studies aimed at measuring tissue-specific mRNA abundance in porcine samples. © 2011 Blackwell Verlag GmbH.

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues.

PubMed

Wong, Aaron K; Krishnan, Arjun; Troyanskaya, Olga G

2018-05-25

GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. The precise actions of genes are frequently dependent on their tissue context, yet direct assay of tissue-specific protein function and interactions remains infeasible in many normal human tissues and cell-types. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses. Additionally, the NetWAS approach available through the server uses tissue-specific/cell-type networks predicted by GIANT2 to re-prioritize statistical associations from GWAS studies and identify disease-associated genes. GIANT2 predicts tissue-specific interactions by integrating diverse functional genomics data from now over 61 400 experiments for 283 diverse tissues and cell-types. GIANT2 does not require any registration or installation and is freely available for use at http://giant-v2.princeton.edu.

Tissue-specific autophagy responses to aging and stress in C. elegans.

PubMed

Chapin, Hannah C; Okada, Megan; Merz, Alexey J; Miller, Dana L

2015-06-01

Cellular function relies on a balance between protein synthesis and breakdown. Macromolecular breakdown through autophagy is broadly required for cellular and tissue development, function, and recovery from stress. While Caenorhabditis elegans is frequently used to explore cellular responses to development and stress, the most common assays for autophagy in this system lack tissue-level resolution. Different tissues within an organism have unique functional characteristics and likely vary in their reliance on autophagy under different conditions. To generate a tissue-specific map of autophagy in C. elegans we used a dual fluorescent protein (dFP) tag that releases monomeric fluorescent protein (mFP) upon arrival at the lysosome. Tissue-specific expression of dFP::LGG-1 revealed autophagic flux in all tissues, but mFP accumulation was most dramatic in the intestine. We also observed variable responses to stress: starvation increased autophagic mFP release in all tissues, whereas anoxia primarily increased intestinal autophagic flux. We observed autophagic flux with tagged LGG-1, LGG-2, and two autophagic cargo reporters: a soluble cytoplasmic protein, and mitochondrial TOMM-7. Finally, an increase in mFP in older worms was consistent with an age-dependent shift in proteostasis. These novel measures of autophagic flux in C. elegans reveal heterogeneity in autophagic response across tissues during stress and aging.

Porcine Tissue-Specific Regulatory Networks Derived from Meta-Analysis of the Transcriptome

PubMed Central

Pérez-Montarelo, Dafne; Hudson, Nicholas J.; Fernández, Ana I.; Ramayo-Caldas, Yuliaxis; Dalrymple, Brian P.; Reverter, Antonio

2012-01-01

The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species. PMID:23049964

Identification of Transposable Elements Contributing to Tissue-Specific Expression of Long Non-Coding RNAs

PubMed Central

Chishima, Takafumi; Iwakiri, Junichi

2018-01-01

It has been recently suggested that transposable elements (TEs) are re-used as functional elements of long non-coding RNAs (lncRNAs). This is supported by some examples such as the human endogenous retrovirus subfamily H (HERVH) elements contained within lncRNAs and expressed specifically in human embryonic stem cells (hESCs), as required to maintain hESC identity. There are at least two unanswered questions about all lncRNAs. How many TEs are re-used within lncRNAs? Are there any other TEs that affect tissue specificity of lncRNA expression? To answer these questions, we comprehensively identify TEs that are significantly related to tissue-specific expression levels of lncRNAs. We downloaded lncRNA expression data corresponding to normal human tissue from the Expression Atlas and transformed the data into tissue specificity estimates. Then, Fisher’s exact tests were performed to verify whether the presence or absence of TE-derived sequences influences the tissue specificity of lncRNA expression. Many TE–tissue pairs associated with tissue-specific expression of lncRNAs were detected, indicating that multiple TE families can be re-used as functional domains or regulatory sequences of lncRNAs. In particular, we found that the antisense promoter region of L1PA2, a LINE-1 subfamily, appears to act as a promoter for lncRNAs with placenta-specific expression. PMID:29315213

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

DOE PAGES

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

2015-06-04

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

PubMed Central

2015-01-01

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

PERFORMANCE, RELIABILITY, AND IMPROVEMENT OF A TISSUE-SPECIFIC METABOLIC SIMULATOR

EPA Science Inventory

A methodology is described that has been used to build and enhance a simulator for rat liver metabolism providing reliable predictions within a large chemical domain. The tissue metabolism simulator (TIMES) utilizes a heuristic algorithm to generate plausible metabolic maps using...

Finasteride Treatment Alters Tissue Specific Androgen Receptor Expression in Prostate Tissues

PubMed Central

Bauman, Tyler M.; Sehgal, Priyanka D.; Johnson, Karen A.; Pier, Thomas; Bruskewitz, Reginald C.; Ricke, William A.; Huang, Wei

2014-01-01

BACKGROUND Normal and pathologic growth of the prostate is dependent on the synthesis of dihydrotestosterone (DHT) from testosterone by 5α-reductase. Finasteride is a selective inhibitor of 5α-reductase 2, one isozyme of 5α-reductase found in abundance in the human prostate. The objective of this study was to investigate the effects of finasteride on androgen receptor expression and tissue morphology in human benign prostatic hyperplasia specimens. METHODS Patients undergoing transurethral resection of the prostate and either treated or not treated with finasteride between 2004 and 2010 at the University of Wisconsin-Hospital were retrospectively identified using an institutional database. Prostate specimens from each patient were triple-stained for androgen receptor, prostate-specific antigen, and basal marker cytokeratin 5. Morphometric analysis was performed using the multispectral imaging, and results were compared between groups of finasteride treated and non-treated patients. RESULTS Epithelial androgen receptor but not stromal androgen receptor expression was significantly lower in patients treated with finasteride than in non-treated patients. Androgen receptor-regulated prostate-specific antigen was not significantly decreased in finasteride-treated patients. Significant luminal epithelial atrophy and basal cell hyperplasia were prevalent in finasteride treated patients. Epithelial androgen receptor expression was highly correlated to the level of luminal epithelial atrophy. CONCLUSIONS In this study, finasteride decreased the expression of epithelial androgen receptor in a tissue specific manner. The correlation between epithelial androgen receptor and the extent of luminal epithelial atrophy suggests that epithelial androgen receptor may be directly regulating the atrophic effects observed with finasteride treatment. PMID:24789081

Prediction of cartilaginous tissue repair after knee joint distraction.

PubMed

van der Woude, J A D; Welsing, P M; van Roermund, P M; Custers, R J H; Kuchuk, N O; Lafeber, F P J G G

2016-10-01

For young patients (<65years), knee joint distraction (KJD) may be a joint-saving treatment option for end-stage knee osteoarthritis. Distracting the femur from the tibia by five millimeters for six to eight weeks using an external fixation frame results in cartilaginous tissue repair, in addition to clinical benefits. This study is a first attempt to predict the degree of cartilaginous tissue repair after KJD. Fifty-seven consecutive patients received KJD. At baseline and at one year of follow-up, mean and minimum joint space width (JSW) of the most-affected compartment was determined on standardized radiographs. To evaluate the predictive ability of baseline characteristics for JSW at one year of follow-up, multivariable linear regression analysis was performed. Mean JSW±SD of the most affected compartment increased by 0.95±1.23mm to 3.08±1.43mm at one year (P<0.001). The minimum JSW increased by 0.94±1.03mm to 1.63±1.21mm at one year of follow-up (P<0.001). For a larger mean JSW one year after KJD, only Kellgren & Lawrence grade (KLG) at baseline was predictive (Regression coefficient (β)=0.47, 95% CI=0.18 to 0.77, P=0.002). For a larger minimum JSW, KLG (β=0.46, 95% CI=0.19 to 0.73, P=0.001) and male gender (β=0.52, 95% CI=0.06 to 0.99, P=0.028) were statistically predictive. Eight weeks of distraction time neared significance (β=0.44, 95% CI=-0.05 to 0.93, P=0.080). In our cohort of patients treated with KJD, males with higher KLG had the best chance of cartilaginous tissue repair by distraction. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel, tissue-specific, Drosophila homeobox gene.

PubMed

Barad, M; Jack, T; Chadwick, R; McGinnis, W

1988-07-01

The homeobox gene family of Drosophila appears to control a variety of position-specific patterning decisions during embryonic and imaginal development. Most of these patterning decisions determine groups of cells on the anterior-posterior axis of the Drosophila germ band. We have isolated a novel homeobox gene from Drosophila, designated H2.0. H2.0 has the most diverged homeobox so far characterized in metazoa, and, in contrast to all previously isolated homeobox genes, H2.0 exhibits a tissue-specific pattern of expression. The cells that accumulate transcripts for this novel gene correspond to the visceral musculature and its anlagen.

Age-dependent tissue-specific exposure of cell phone users.

PubMed

Christ, Andreas; Gosselin, Marie-Christine; Christopoulou, Maria; Kühn, Sven; Kuster, Niels

2010-04-07

The peak spatial specific absorption rate (SAR) assessed with the standardized specific anthropometric mannequin head phantom has been shown to yield a conservative exposure estimate for both adults and children using mobile phones. There are, however, questions remaining concerning the impact of age-dependent dielectric tissue properties and age-dependent proportions of the skull, face and ear on the global and local absorption, in particular in the brain tissues. In this study, we compare the absorption in various parts of the cortex for different magnetic resonance imaging-based head phantoms of adults and children exposed to different models of mobile phones. The results show that the locally induced fields in children can be significantly higher (>3 dB) in subregions of the brain (cortex, hippocampus and hypothalamus) and the eye due to the closer proximity of the phone to these tissues. The increase is even larger for bone marrow (>10 dB) as a result of its significantly high conductivity. Tissues such as the pineal gland show no increase since their distances to the phone are not a function of age. This study, however, confirms previous findings saying that there are no age-dependent changes of the peak spatial SAR when averaged over the entire head.

Age-dependent tissue-specific exposure of cell phone users

NASA Astrophysics Data System (ADS)

Christ, Andreas; Gosselin, Marie-Christine; Christopoulou, Maria; Kühn, Sven; Kuster, Niels

2010-04-01

The peak spatial specific absorption rate (SAR) assessed with the standardized specific anthropometric mannequin head phantom has been shown to yield a conservative exposure estimate for both adults and children using mobile phones. There are, however, questions remaining concerning the impact of age-dependent dielectric tissue properties and age-dependent proportions of the skull, face and ear on the global and local absorption, in particular in the brain tissues. In this study, we compare the absorption in various parts of the cortex for different magnetic resonance imaging-based head phantoms of adults and children exposed to different models of mobile phones. The results show that the locally induced fields in children can be significantly higher (>3 dB) in subregions of the brain (cortex, hippocampus and hypothalamus) and the eye due to the closer proximity of the phone to these tissues. The increase is even larger for bone marrow (>10 dB) as a result of its significantly high conductivity. Tissues such as the pineal gland show no increase since their distances to the phone are not a function of age. This study, however, confirms previous findings saying that there are no age-dependent changes of the peak spatial SAR when averaged over the entire head.

Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum

PubMed Central

Takahashi, Yuji K.; Langdon, Angela J.; Niv, Yael; Schoenbaum, Geoffrey

2016-01-01

Summary Dopamine neurons signal reward prediction errors. This requires accurate reward predictions. It has been suggested that the ventral striatum provides these predictions. Here we tested this hypothesis by recording from putative dopamine neurons in the VTA of rats performing a task in which prediction errors were induced by shifting reward timing or number. In controls, the neurons exhibited error signals in response to both manipulations. However, dopamine neurons in rats with ipsilateral ventral striatal lesions exhibited errors only to changes in number and failed to respond to changes in timing of reward. These results, supported by computational modeling, indicate that predictions about the temporal specificity and the number of expected rewards are dissociable, and that dopaminergic prediction-error signals rely on the ventral striatum for the former but not the latter. PMID:27292535

A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

PubMed

Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

2016-05-01

Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone <18 ng/dL, and either no or bilateral tumor found on computed tomography (CT) imaging had perfect estimated specificity (and thus 100% positive predictive value) for bilateral PA, saving an estimated 16% of the patients (11/67) from unnecessary AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level <18 ng/dL alone, which yielded 74% sensitivity and 75% specificity for predicting nonlateralized AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

Epigenome-wide cross-tissue predictive modeling and comparison of cord blood and placental methylation in a birth cohort

PubMed Central

De Carli, Margherita M; Baccarelli, Andrea A; Trevisi, Letizia; Pantic, Ivan; Brennan, Kasey JM; Hacker, Michele R; Loudon, Holly; Brunst, Kelly J; Wright, Robert O; Wright, Rosalind J; Just, Allan C

2017-01-01

Aim: We compared predictive modeling approaches to estimate placental methylation using cord blood methylation. Materials & methods: We performed locus-specific methylation prediction using both linear regression and support vector machine models with 174 matched pairs of 450k arrays. Results: At most CpG sites, both approaches gave poor predictions in spite of a misleading improvement in array-wide correlation. CpG islands and gene promoters, but not enhancers, were the genomic contexts where the correlation between measured and predicted placental methylation levels achieved higher values. We provide a list of 714 sites where both models achieved an R2 ≥0.75. Conclusion: The present study indicates the need for caution in interpreting cross-tissue predictions. Few methylation sites can be predicted between cord blood and placenta. PMID:28234020

Identification of tissue-specific cell death using methylation patterns of circulating DNA

PubMed Central

Lehmann-Werman, Roni; Neiman, Daniel; Zemmour, Hai; Moss, Joshua; Magenheim, Judith; Vaknin-Dembinsky, Adi; Rubertsson, Sten; Nellgård, Bengt; Blennow, Kaj; Zetterberg, Henrik; Spalding, Kirsty; Haller, Michael J.; Wasserfall, Clive H.; Schatz, Desmond A.; Greenbaum, Carla J.; Dorrell, Craig; Grompe, Markus; Zick, Aviad; Hubert, Ayala; Maoz, Myriam; Fendrich, Volker; Bartsch, Detlef K.; Golan, Talia; Ben Sasson, Shmuel A.; Zamir, Gideon; Razin, Aharon; Cedar, Howard; Shapiro, A. M. James; Glaser, Benjamin; Shemer, Ruth; Dor, Yuval

2016-01-01

Minimally invasive detection of cell death could prove an invaluable resource in many physiologic and pathologic situations. Cell-free circulating DNA (cfDNA) released from dying cells is emerging as a diagnostic tool for monitoring cancer dynamics and graft failure. However, existing methods rely on differences in DNA sequences in source tissues, so that cell death cannot be identified in tissues with a normal genome. We developed a method of detecting tissue-specific cell death in humans based on tissue-specific methylation patterns in cfDNA. We interrogated tissue-specific methylome databases to identify cell type-specific DNA methylation signatures and developed a method to detect these signatures in mixed DNA samples. We isolated cfDNA from plasma or serum of donors, treated the cfDNA with bisulfite, PCR-amplified the cfDNA, and sequenced it to quantify cfDNA carrying the methylation markers of the cell type of interest. Pancreatic β-cell DNA was identified in the circulation of patients with recently diagnosed type-1 diabetes and islet-graft recipients; oligodendrocyte DNA was identified in patients with relapsing multiple sclerosis; neuronal/glial DNA was identified in patients after traumatic brain injury or cardiac arrest; and exocrine pancreas DNA was identified in patients with pancreatic cancer or pancreatitis. This proof-of-concept study demonstrates that the tissue origins of cfDNA and thus the rate of death of specific cell types can be determined in humans. The approach can be adapted to identify cfDNA derived from any cell type in the body, offering a minimally invasive window for diagnosing and monitoring a broad spectrum of human pathologies as well as providing a better understanding of normal tissue dynamics. PMID:26976580

How accurate are resting energy expenditure prediction equations in obese trauma and burn patients?

PubMed

Stucky, Chee-Chee H; Moncure, Michael; Hise, Mary; Gossage, Clint M; Northrop, David

2008-01-01

While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

Development and validation of a MRgHIFU non-invasive tissue acoustic property estimation technique.

PubMed

Johnson, Sara L; Dillon, Christopher; Odéen, Henrik; Parker, Dennis; Christensen, Douglas; Payne, Allison

2016-11-01

MR-guided high-intensity focussed ultrasound (MRgHIFU) non-invasive ablative surgeries have advanced into clinical trials for treating many pathologies and cancers. A remaining challenge of these surgeries is accurately planning and monitoring tissue heating in the face of patient-specific and dynamic acoustic properties of tissues. Currently, non-invasive measurements of acoustic properties have not been implemented in MRgHIFU treatment planning and monitoring procedures. This methods-driven study presents a technique using MR temperature imaging (MRTI) during low-temperature HIFU sonications to non-invasively estimate sample-specific acoustic absorption and speed of sound values in tissue-mimicking phantoms. Using measured thermal properties, specific absorption rate (SAR) patterns are calculated from the MRTI data and compared to simulated SAR patterns iteratively generated via the Hybrid Angular Spectrum (HAS) method. Once the error between the simulated and measured patterns is minimised, the estimated acoustic property values are compared to the true phantom values obtained via an independent technique. The estimated values are then used to simulate temperature profiles in the phantoms, and compared to experimental temperature profiles. This study demonstrates that trends in acoustic absorption and speed of sound can be non-invasively estimated with average errors of 21% and 1%, respectively. Additionally, temperature predictions using the estimated properties on average match within 1.2 °C of the experimental peak temperature rises in the phantoms. The positive results achieved in tissue-mimicking phantoms presented in this study indicate that this technique may be extended to in vivo applications, improving HIFU sonication temperature rise predictions and treatment assessment.

Sensitivity and Specificity of Cardiac Tissue Discrimination Using Fiber-Optics Confocal Microscopy.

PubMed

Huang, Chao; Sachse, Frank B; Hitchcock, Robert W; Kaza, Aditya K

2016-01-01

Disturbances of the cardiac conduction system constitute a major risk after surgical repair of complex cases of congenital heart disease. Intraoperative identification of the conduction system may reduce the incidence of these disturbances. We previously developed an approach to identify cardiac tissue types using fiber-optics confocal microscopy and extracellular fluorophores. Here, we applied this approach to investigate sensitivity and specificity of human and automated classification in discriminating images of atrial working myocardium and specialized tissue of the conduction system. Two-dimensional image sequences from atrial working myocardium and nodal tissue of isolated perfused rodent hearts were acquired using a fiber-optics confocal microscope (Leica FCM1000). We compared two methods for local application of extracellular fluorophores: topical via pipette and with a dye carrier. Eight blinded examiners evaluated 162 randomly selected images of atrial working myocardium (n = 81) and nodal tissue (n = 81). In addition, we evaluated the images using automated classification. Blinded examiners achieved a sensitivity and specificity of 99.2 ± 0.3% and 98.0 ± 0.7%, respectively, with the dye carrier method of dye application. Sensitivity and specificity was similar for dye application via a pipette (99.2 ± 0.3% and 94.0 ± 2.4%, respectively). Sensitivity and specificity for automated methods of tissue discrimination were similarly high. Human and automated classification achieved high sensitivity and specificity in discriminating atrial working myocardium and nodal tissue. We suggest that our findings facilitate clinical translation of fiber-optics confocal microscopy as an intraoperative imaging modality to reduce the incidence of conduction disturbances during surgical correction of congenital heart disease.

Digital sorting of complex tissues for cell type-specific gene expression profiles.

PubMed

Zhong, Yi; Wan, Ying-Wooi; Pang, Kaifang; Chow, Lionel M L; Liu, Zhandong

2013-03-07

Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles. Furthermore, these algorithms tend to report biased estimations. Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies. The results suggest that DSA is a specific and sensitivity algorithm in gene expression profile deconvolution and will be useful in studying individual cell types of complex tissues.

Range prediction for tissue mixtures based on dual-energy CT

NASA Astrophysics Data System (ADS)

Möhler, Christian; Wohlfahrt, Patrick; Richter, Christian; Greilich, Steffen

2016-06-01

The use of dual-energy CT (DECT) potentially decreases range uncertainties in proton and ion therapy treatment planning via determination of the involved physical target quantities. For eventual clinical application, the correct treatment of tissue mixtures and heterogeneities is an essential feature, as they naturally occur within a patient’s CT. Here, we present how existing methods for DECT-based ion-range prediction can be modified in order to incorporate proper mixing behavior on several structural levels. Our approach is based on the factorization of the stopping-power ratio into the relative electron density and the relative stopping number. The latter is confined for tissue between about 0.95 and 1.02 at a therapeutic beam energy of 200 MeV u-1 and depends on the I-value. We show that convenient mixing and averaging properties arise by relating the relative stopping number to the relative cross section obtained by DECT. From this, a maximum uncertainty of the stopping-power ratio prediction below 1% is suggested for arbitrary mixtures of human body tissues.

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure.

PubMed

Nielsen, Morten; Justesen, Sune; Lund, Ole; Lundegaard, Claus; Buus, Søren

2010-11-13

Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option. Here, we present an MHC-II binding prediction algorithm aiming at dealing with these challenges. The method is a pan-specific version of the earlier published allele-specific NN-align algorithm and does not require any pre-alignment of the input data. This allows the method to benefit also from information from alleles covered by limited binding data. The method is evaluated on a large and diverse set of benchmark data, and is shown to significantly out-perform state-of-the-art MHC-II prediction methods. In particular, the method is found to boost the performance for alleles characterized by limited binding data where conventional allele-specific methods tend to achieve poor prediction accuracy. The method thus shows great potential for efficient boosting the accuracy of MHC-II binding prediction, as accurate predictions can be

Fourier and non-Fourier bio-heat transfer models to predict ex vivo temperature response to focused ultrasound heating

NASA Astrophysics Data System (ADS)

Li, Chenghai; Miao, Jiaming; Yang, Kexin; Guo, Xiasheng; Tu, Juan; Huang, Pintong; Zhang, Dong

2018-05-01

Although predicting temperature variation is important for designing treatment plans for thermal therapies, research in this area is yet to investigate the applicability of prevalent thermal conduction models, such as the Pennes equation, the thermal wave model of bio-heat transfer, and the dual phase lag (DPL) model. To address this shortcoming, we heated a tissue phantom and ex vivo bovine liver tissues with focused ultrasound (FU), measured the temperature response, and compared the results with those predicted by these models. The findings show that, for a homogeneous-tissue phantom, the initial temperature increase is accurately predicted by the Pennes equation at the onset of FU irradiation, although the prediction deviates from the measured temperature with increasing FU irradiation time. For heterogeneous liver tissues, the predicted response is closer to the measured temperature for the non-Fourier models, especially the DPL model. Furthermore, the DPL model accurately predicts the temperature response in biological tissues because it increases the phase lag, which characterizes microstructural thermal interactions. These findings should help to establish more precise clinical treatment plans for thermal therapies.

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

PubMed

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Tissue-specific alternative splicing of TCF7L2

PubMed Central

Prokunina-Olsson, Ludmila; Welch, Cullan; Hansson, Ola; Adhikari, Neeta; Scott, Laura J.; Usher, Nicolle; Tong, Maurine; Sprau, Andrew; Swift, Amy; Bonnycastle, Lori L.; Erdos, Michael R.; He, Zhi; Saxena, Richa; Harmon, Brennan; Kotova, Olga; Hoffman, Eric P.; Altshuler, David; Groop, Leif; Boehnke, Michael; Collins, Francis S.; Hall, Jennifer L.

2009-01-01

Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been identified as the strongest genetic risk factors for type 2 diabetes (T2D). However, the mechanisms by which these non-coding variants increase risk for T2D are not well-established. We used 13 expression assays to survey mRNA expression of multiple TCF7L2 splicing forms in up to 380 samples from eight types of human tissue (pancreas, pancreatic islets, colon, liver, monocytes, skeletal muscle, subcutaneous adipose tissue and lymphoblastoid cell lines) and observed a tissue-specific pattern of alternative splicing. We tested whether the expression of TCF7L2 splicing forms was associated with single nucleotide polymorphisms (SNPs), rs7903146 and rs12255372, located within introns 3 and 4 of the gene and most strongly associated with T2D. Expression of two splicing forms was lower in pancreatic islets with increasing counts of T2D-associated alleles of the SNPs: a ubiquitous splicing form (P = 0.018 for rs7903146 and P = 0.020 for rs12255372) and a splicing form found in pancreatic islets, pancreas and colon but not in other tissues tested here (P = 0.009 for rs12255372 and P = 0.053 for rs7903146). Expression of this form in glucose-stimulated pancreatic islets correlated with expression of proinsulin (r2 = 0.84–0.90, P < 0.00063). In summary, we identified a tissue-specific pattern of alternative splicing of TCF7L2. After adjustment for multiple tests, no association between expression of TCF7L2 in eight types of human tissue samples and T2D-associated genetic variants remained significant. Alternative splicing of TCF7L2 in pancreatic islets warrants future studies. GenBank Accession Numbers: FJ010164–FJ010174. PMID:19602480

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

PubMed

Darrington, Richard T; Jiao, Jim

2004-04-01

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

A toolkit for GFP-mediated tissue-specific protein degradation in C. elegans.

PubMed

Wang, Shaohe; Tang, Ngang Heok; Lara-Gonzalez, Pablo; Zhao, Zhiling; Cheerambathur, Dhanya K; Prevo, Bram; Chisholm, Andrew D; Desai, Arshad; Oegema, Karen

2017-07-15

Proteins that are essential for embryo production, cell division and early embryonic events are frequently reused later in embryogenesis, during organismal development or in the adult. Examining protein function across these different biological contexts requires tissue-specific perturbation. Here, we describe a method that uses expression of a fusion between a GFP-targeting nanobody and a SOCS-box containing ubiquitin ligase adaptor to target GFP-tagged proteins for degradation. When combined with endogenous locus GFP tagging by CRISPR-Cas9 or with rescue of a null mutant with a GFP fusion, this approach enables routine and efficient tissue-specific protein ablation. We show that this approach works in multiple tissues - the epidermis, intestine, body wall muscle, ciliated sensory neurons and touch receptor neurons - where it recapitulates expected loss-of-function mutant phenotypes. The transgene toolkit and the strain set described here will complement existing approaches to enable routine analysis of the tissue-specific roles of C. elegans proteins. © 2017. Published by The Company of Biologists Ltd.

Tissue-specific NETs alter genome organization and regulation even in a heterologous system.

PubMed

de Las Heras, Jose I; Zuleger, Nikolaj; Batrakou, Dzmitry G; Czapiewski, Rafal; Kerr, Alastair R W; Schirmer, Eric C

2017-01-02

Different cell types exhibit distinct patterns of 3D genome organization that correlate with changes in gene expression in tissue and differentiation systems. Several tissue-specific nuclear envelope transmembrane proteins (NETs) have been found to influence the spatial positioning of genes and chromosomes that normally occurs during tissue differentiation. Here we study 3 such NETs: NET29, NET39, and NET47, which are expressed preferentially in fat, muscle and liver, respectively. We found that even when exogenously expressed in a heterologous system they can specify particular genome organization patterns and alter gene expression. Each NET affected largely different subsets of genes. Notably, the liver-specific NET47 upregulated many genes in HT1080 fibroblast cells that are normally upregulated in hepatogenesis, showing that tissue-specific NETs can favor expression patterns associated with the tissue where the NET is normally expressed. Similarly, global profiling of peripheral chromatin after exogenous expression of these NETs using lamin B1 DamID revealed that each NET affected the nuclear positioning of distinct sets of genomic regions with a significant tissue-specific component. Thus NET influences on genome organization can contribute to gene expression changes associated with differentiation even in the absence of other factors and overt cellular differentiation changes.

Quantitative Prediction of Paravalvular Leak in Transcatheter Aortic Valve Replacement Based on Tissue-Mimicking 3D Printing.

PubMed

Qian, Zhen; Wang, Kan; Liu, Shizhen; Zhou, Xiao; Rajagopal, Vivek; Meduri, Christopher; Kauten, James R; Chang, Yung-Hang; Wu, Changsheng; Zhang, Chuck; Wang, Ben; Vannan, Mani A

2017-07-01

This study aimed to develop a procedure simulation platform for in vitro transcatheter aortic valve replacement (TAVR) using patient-specific 3-dimensional (3D) printed tissue-mimicking phantoms. We investigated the feasibility of using these 3D printed phantoms to quantitatively predict the occurrence, severity, and location of any degree of post-TAVR paravalvular leaks (PVL). We have previously shown that metamaterial 3D printing technique can be used to create patient-specific phantoms that mimic the mechanical properties of biological tissue. This may have applications in procedural planning for cardiovascular interventions. This retrospective study looked at 18 patients who underwent TAVR. Patient-specific aortic root phantoms were created using the tissue-mimicking 3D printing technique using pre-TAVR computed tomography. The CoreValve (self-expanding valve) prostheses were deployed in the phantoms to simulate the TAVR procedure, from which post-TAVR aortic root strain was quantified in vitro. A novel index, the annular bulge index, was measured to assess the post-TAVR annular strain unevenness in the phantoms. We tested the comparative predictive value of the bulge index and other known predictors of post-TAVR PVL. The maximum annular bulge index was significantly different among patient subgroups that had no PVL, trace-to-mild PVL, and moderate-to-severe PVL (p = 0.001). Compared with other known PVL predictors, bulge index was the only significant predictor of moderate-severe PVL (area under the curve = 95%; p < 0.0001). Also, in 12 patients with post-TAVR PVL, the annular bulge index predicted the major PVL location in 9 patients (accuracy = 75%). In this proof-of-concept study, we have demonstrated the feasibility of using 3D printed tissue-mimicking phantoms to quantitatively assess the post-TAVR aortic root strain in vitro. A novel indicator of the post-TAVR annular strain unevenness, the annular bulge index, outperformed the other

Testis-specific ATP synthase peripheral stalk subunits required for tissue-specific mitochondrial morphogenesis in Drosophila.

PubMed

Sawyer, Eric M; Brunner, Elizabeth C; Hwang, Yihharn; Ivey, Lauren E; Brown, Olivia; Bannon, Megan; Akrobetu, Dennis; Sheaffer, Kelsey E; Morgan, Oshauna; Field, Conroy O; Suresh, Nishita; Gordon, M Grace; Gunnell, E Taylor; Regruto, Lindsay A; Wood, Cricket G; Fuller, Margaret T; Hales, Karen G

2017-03-23

In Drosophila early post-meiotic spermatids, mitochondria undergo dramatic shaping into the Nebenkern, a spherical body with complex internal structure that contains two interwrapped giant mitochondrial derivatives. The purpose of this study was to elucidate genetic and molecular mechanisms underlying the shaping of this structure. The knotted onions (knon) gene encodes an unconventionally large testis-specific paralog of ATP synthase subunit d and is required for internal structure of the Nebenkern as well as its subsequent disassembly and elongation. Knon localizes to spermatid mitochondria and, when exogenously expressed in flight muscle, alters the ratio of ATP synthase complex dimers to monomers. By RNAi knockdown we uncovered mitochondrial shaping roles for other testis-expressed ATP synthase subunits. We demonstrate the first known instance of a tissue-specific ATP synthase subunit affecting tissue-specific mitochondrial morphogenesis. Since ATP synthase dimerization is known to affect the degree of inner mitochondrial membrane curvature in other systems, the effect of Knon and other testis-specific paralogs of ATP synthase subunits may be to mediate differential membrane curvature within the Nebenkern.

The role of the endocrine system in feeding-induced tissue-specific circadian entrainment.

PubMed

Sato, Miho; Murakami, Mariko; Node, Koichi; Matsumura, Ritsuko; Akashi, Makoto

2014-07-24

The circadian clock is entrained to environmental cycles by external cue-mediated phase adjustment. Although the light input pathway has been well defined, the mechanism of feeding-induced phase resetting remains unclear. The tissue-specific sensitivity of peripheral entrainment to feeding suggests the involvement of multiple pathways, including humoral and neuronal signals. Previous in vitro studies with cultured cells indicate that endocrine factors may function as entrainment cues for peripheral clocks. However, blood-borne factors that are well characterized in actual feeding-induced resetting have yet to be identified. Here, we report that insulin may be involved in feeding-induced tissue-type-dependent entrainment in vivo. In ex vivo culture experiments, insulin-induced phase shift in peripheral clocks was dependent on tissue type, which was consistent with tissue-specific insulin sensitivity, and peripheral entrainment in insulin-sensitive tissues involved PI3K- and MAPK-mediated signaling pathways. These results suggest that insulin may be an immediate early factor in feeding-mediated tissue-specific entrainment. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Tissue-specific tumorigenesis – Context matters

PubMed Central

Schneider, Günter; Schmidt-Supprian, Marc; Rad, Roland; Saur, Dieter

2018-01-01

Preface How can we treat cancer more effectively? Traditionally, tumours from the same anatomical site are treated as one tumour entity. This concept has been challenged by recent breakthroughs in cancer genomics and translational research enabling molecular tumour profiling. The identification and validation of cancer drivers, which are shared between different tumour types, spurred the new paradigm to target driver pathways across anatomical sites by off-label drug use, or within so called “basket or umbrella trials”, which are designed to test whether molecular alterations in one tumour entity can be extrapolated to all others. However, recent clinical and preclinical studies suggest that there are tissue- and cell type-specific differences in tumourigenesis and the organization of oncogenic signalling pathways. In this Opinion article, we focus on the molecular, cellular, systemic and environmental determinants of organ-specific tumourigenesis and mechanisms of context-specific oncogenic signalling outputs. Investigation, recognition and in-depth biological understanding of these differences will be vital for the design of next-generation clinical trials and the implementation of molecularly-guided cancer therapies in the future. PMID:28256574

Complex Tissue-Specific Patterns and Distribution of Multiple RAGE Splice Variants in Different Mammals

PubMed Central

López-Díez, Raquel; Rastrojo, Alberto; Villate, Olatz; Aguado, Begoña

2013-01-01

The receptor for advanced glycosylation end products (RAGE) is a multiligand receptor involved in diverse cell signaling pathways. Previous studies show that this gene expresses several splice variants in human, mouse, and dog. Alternative splicing (AS) plays an important role in expanding transcriptomic and proteomic diversity, and it has been related to disease. AS is also one of the main evolutionary mechanisms in mammalian genomes. However, limited information is available regarding the AS of RAGE in a wide context of mammalian tissues. In this study, we examined in detail the different RAGE mRNAs generated by AS from six mammals, including two primates (human and monkey), two artiodactyla (cow and pig), and two rodentia (mouse and rat) in 6–18 different tissues including fetal, adult, and tumor. By nested reverse transcription-polymerase chain reaction (RT-PCR) we identified a high number of splice variants including noncoding transcripts and predicted coding ones with different potential protein modifications affecting mainly the transmembrane and ligand-binding domains that could influence their biological function. However, analysis of RNA-seq data enabled detecting only the most abundant splice variants. More than 80% of the detected RT-PCR variants (87 of 101 transcripts) are novel (different exon/intron structure to the previously described ones), and interestingly, 20–60% of the total transcripts (depending on the species) are noncoding ones that present tissue specificity. Our results suggest that RAGE undergoes extensive AS in mammals, with different expression patterns among adult, fetal, and tumor tissues. Moreover, most splice variants seem to be species specific, especially the noncoding variants, with only two (canonical human Tv1-RAGE, and human N-truncated or Tv10-RAGE) conserved among the six different species. This could indicate a special evolution pattern of this gene at mRNA level. PMID:24273313

Kinetic approach to degradation mechanisms in polymer solar cells and their accurate lifetime predictions

NASA Astrophysics Data System (ADS)

Arshad, Muhammad Azeem; Maaroufi, AbdelKrim

2018-07-01

A beginning has been made in the present study regarding the accurate lifetime predictions of polymer solar cells. Certain reservations about the conventionally employed temperature accelerated lifetime measurements test for its unworthiness of predicting reliable lifetimes of polymer solar cells are brought into light. Critical issues concerning the accelerated lifetime testing include, assuming reaction mechanism instead of determining it, and relying solely on the temperature acceleration of a single property of material. An advanced approach comprising a set of theoretical models to estimate the accurate lifetimes of polymer solar cells is therefore suggested in order to suitably alternate the accelerated lifetime testing. This approach takes into account systematic kinetic modeling of various possible polymer degradation mechanisms under natural weathering conditions. The proposed kinetic approach is substantiated by its applications on experimental aging data-sets of polymer solar materials/solar cells including, P3HT polymer film, bulk heterojunction (MDMO-PPV:PCBM) and dye-sensitized solar cells. Based on the suggested approach, an efficacious lifetime determination formula for polymer solar cells is derived and tested on dye-sensitized solar cells. Some important merits of the proposed method are also pointed out and its prospective applications are discussed.

Timing of Tissue-specific Cell Division Requires a Differential Onset of Zygotic Transcription during Metazoan Embryogenesis*

PubMed Central

Wong, Ming-Kin; Guan, Daogang; Ng, Kaoru Hon Chun; Ho, Vincy Wing Sze; An, Xiaomeng; Li, Runsheng; Ren, Xiaoliang

2016-01-01

Metazoan development demands not only precise cell fate differentiation but also accurate timing of cell division to ensure proper development. How cell divisions are temporally coordinated during development is poorly understood. Caenorhabditis elegans embryogenesis provides an excellent opportunity to study this coordination due to its invariant development and widespread division asynchronies. One of the most pronounced asynchronies is a significant delay of cell division in two endoderm progenitor cells, Ea and Ep, hereafter referred to as E2, relative to its cousins that mainly develop into mesoderm organs and tissues. To unravel the genetic control over the endoderm-specific E2 division timing, a total of 822 essential and conserved genes were knocked down using RNAi followed by quantification of cell cycle lengths using in toto imaging of C. elegans embryogenesis and automated lineage. Intriguingly, knockdown of numerous genes encoding the components of general transcription pathway or its regulatory factors leads to a significant reduction in the E2 cell cycle length but an increase in cell cycle length of the remaining cells, indicating a differential requirement of transcription for division timing between the two. Analysis of lineage-specific RNA-seq data demonstrates an earlier onset of transcription in endoderm than in other germ layers, the timing of which coincides with the birth of E2, supporting the notion that the endoderm-specific delay in E2 division timing demands robust zygotic transcription. The reduction in E2 cell cycle length is frequently associated with cell migration defect and gastrulation failure. The results suggest that a tissue-specific transcriptional activation is required to coordinate fate differentiation, division timing, and cell migration to ensure proper development. PMID:27056332

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

PubMed Central

Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D.H.; Jo, Brian; Gao, Chuan; McDowell, Ian C.; Engelhardt, Barbara E.

2017-01-01

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. PMID:29021288

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

PubMed

Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

2016-08-01

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

PubMed Central

Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

2015-01-01

Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

Tissue-Specific Transcriptomics in the Field Cricket Teleogryllus oceanicus

PubMed Central

Bailey, Nathan W.; Veltsos, Paris; Tan, Yew-Foon; Millar, A. Harvey; Ritchie, Michael G.; Simmons, Leigh W.

2013-01-01

Field crickets (family Gryllidae) frequently are used in studies of behavioral genetics, sexual selection, and sexual conflict, but there have been no studies of transcriptomic differences among different tissue types. We evaluated transcriptome variation among testis, accessory gland, and the remaining whole-body preparations from males of the field cricket, Teleogryllus oceanicus. Non-normalized cDNA libraries from each tissue were sequenced on the Roche 454 platform, and a master assembly was constructed using testis, accessory gland, and whole-body preparations. A total of 940,200 reads were assembled into 41,962 contigs, to which 36,856 singletons (reads not assembled into a contig) were added to provide a total of 78,818 sequences used in annotation analysis. A total of 59,072 sequences (75%) were unique to one of the three tissues. Testis tissue had the greatest proportion of tissue-specific sequences (62.6%), followed by general body (56.43%) and accessory gland tissue (44.16%). We tested the hypothesis that tissues expressing gene products expected to evolve rapidly as a result of sexual selection—testis and accessory gland—would yield a smaller proportion of BLASTx matches to homologous genes in the model organism Drosophila melanogaster compared with whole-body tissue. Uniquely expressed sequences in both testis and accessory gland showed a significantly lower rate of matching to annotated D. melanogaster genes compared with those from general body tissue. These results correspond with empirical evidence that genes expressed in testis and accessory gland tissue are rapidly evolving targets of selection. PMID:23390599

Tissue-specific transcriptomics in the field cricket Teleogryllus oceanicus.

PubMed

Bailey, Nathan W; Veltsos, Paris; Tan, Yew-Foon; Millar, A Harvey; Ritchie, Michael G; Simmons, Leigh W

2013-02-01

Field crickets (family Gryllidae) frequently are used in studies of behavioral genetics, sexual selection, and sexual conflict, but there have been no studies of transcriptomic differences among different tissue types. We evaluated transcriptome variation among testis, accessory gland, and the remaining whole-body preparations from males of the field cricket, Teleogryllus oceanicus. Non-normalized cDNA libraries from each tissue were sequenced on the Roche 454 platform, and a master assembly was constructed using testis, accessory gland, and whole-body preparations. A total of 940,200 reads were assembled into 41,962 contigs, to which 36,856 singletons (reads not assembled into a contig) were added to provide a total of 78,818 sequences used in annotation analysis. A total of 59,072 sequences (75%) were unique to one of the three tissues. Testis tissue had the greatest proportion of tissue-specific sequences (62.6%), followed by general body (56.43%) and accessory gland tissue (44.16%). We tested the hypothesis that tissues expressing gene products expected to evolve rapidly as a result of sexual selection--testis and accessory gland--would yield a smaller proportion of BLASTx matches to homologous genes in the model organism Drosophila melanogaster compared with whole-body tissue. Uniquely expressed sequences in both testis and accessory gland showed a significantly lower rate of matching to annotated D. melanogaster genes compared with those from general body tissue. These results correspond with empirical evidence that genes expressed in testis and accessory gland tissue are rapidly evolving targets of selection.

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

PubMed

Mei, Hao; Li, Lianna; Liu, Shijian; Jiang, Fan; Griswold, Michael; Mosley, Thomas

2017-01-21

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

Analysis of RNA-Seq datasets reveals enrichment of tissue-specific splice variants for nuclear envelope proteins.

PubMed

Capitanchik, Charlotte; Dixon, Charles; Swanson, Selene K; Florens, Laurence; Kerr, Alastair R W; Schirmer, Eric C

2018-06-18

Nuclear envelopathies/laminopathies yield tissue-specific pathologies, yet arise from mutation of ubiquitously-expressed genes. One possible explanation of this tissue specificity is that tissue-specific partners become disrupted from larger complexes, but a little investigated alternate hypothesis is that the mutated proteins themselves have tissue-specific splice variants. Here, we analyze RNA-Seq datasets to identify muscle-specific splice variants of nuclear envelope genes that could be relevant to the study of laminopathies, particularly muscular dystrophies, that are not currently annotated in sequence databases. Notably, we found novel isoforms or tissue-specificity of isoforms for: Lap2, linked to cardiomyopathy; Nesprin 2, linked to Emery-Dreifuss muscular dystrophy and Lmo7, a regulator of the emerin gene that is linked to Emery-Dreifuss muscular dystrophy. Interestingly, the muscle-specific exon in Lmo7 is rich in serine phosphorylation motifs, suggesting an important regulatory function. Evidence for muscle-specific splice variants in non-nuclear envelope proteins linked to other muscular dystrophies was also found. Tissue-specific variants were also indicated for several nucleoporins including Nup54, Nup133, Nup153 and Nup358/RanBP2. We confirmed expression of novel Lmo7 and RanBP2 variants with RT-PCR and found that specific knockdown of the Lmo7 variant caused a reduction in myogenic index during mouse C2C12 myogenesis. Global analysis revealed an enrichment of tissue-specific splice variants for nuclear envelope proteins in general compared to the rest of the genome, suggesting that splice variants contribute to regulating its tissue-specific functions.

Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

PubMed

Kolokitha, Olga-Elpis

2007-10-01

The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

PubMed

Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

2007-06-18

Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

2013-11-21

The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys.more » 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.« less

Bioprinting for Neural Tissue Engineering.

PubMed

Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

2018-01-01

Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

PubMed

Nielsen, Anne; Hansen, Mikkel Bo; Tietze, Anna; Mouridsen, Kim

2018-06-01

Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume. Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNN deep ) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNN deep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNN Tmax ), a shallow CNN based on a combination of 9 different biomarkers (CNN shallow ), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10 -6 mm 2 /s (ADC thres ). To assess whether CNN deep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNN deep, -rtpa to access a treatment effect. The networks' performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast. CNN deep yields significantly better performance in predicting final outcome (AUC=0.88±0.12) than generalized linear model (AUC=0.78±0.12; P =0.005), CNN Tmax (AUC=0.72±0.14; P <0.003), and ADC thres (AUC=0.66±0.13; P <0.0001) and a substantially better performance than CNN shallow (AUC=0.85±0.11; P =0.063). Measured by contrast, CNN deep improves the predictions significantly, showing superiority to all other methods ( P ≤0.003). CNN deep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different ( P =0.048). The considerable prediction

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

PubMed

Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H; Jo, Brian; Gao, Chuan; McDowell, Ian C; Engelhardt, Barbara E; Battle, Alexis

2017-11-01

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press.

ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

NASA Astrophysics Data System (ADS)

Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

2016-05-01

At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

NASA Astrophysics Data System (ADS)

Unger, Jakob; Sun, Tianchen; Chen, Yi-Ling; Phipps, Jennifer E.; Bold, Richard J.; Darrow, Morgan A.; Ma, Kwan-Liu; Marcu, Laura

2018-01-01

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

PubMed

Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

2017-01-01

Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

PubMed Central

Li, Zhen; Zhang, Renyu

2017-01-01

Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

Obtaining Accurate Probabilities Using Classifier Calibration

ERIC Educational Resources Information Center

Pakdaman Naeini, Mahdi

2016-01-01

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

Validity of a Manual Soft Tissue Profile Prediction Method Following Mandibular Setback Osteotomy

PubMed Central

Kolokitha, Olga-Elpis

2007-01-01

Objectives The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. Methods To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Results Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Conclusions Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication. PMID:19212468

Tissue-Specific Effects of Loss of Estrogen during Menopause and Aging.

PubMed

Wend, Korinna; Wend, Peter; Krum, Susan A

2012-01-01

The roles of estrogens have been best studied in the breast, breast cancers, and in the female reproductive tract. However, estrogens have important functions in almost every tissue in the body. Recent clinical trials such as the Women's Health Initiative have highlighted both the importance of estrogens and how little we know about the molecular mechanism of estrogens in these other tissues. In this review, we illustrate the diverse functions of estrogens in the bone, adipose tissue, skin, hair, brain, skeletal muscle and cardiovascular system, and how the loss of estrogens during aging affects these tissues. Early transcriptional targets of estrogen are reviewed in each tissue. We also describe the tissue-specific effects of selective estrogen receptor modulators (SERMs) used for the treatment of breast cancers and postmenopausal symptoms.

A Systematic Approach to Predicting Spring Force for Sagittal Craniosynostosis Surgery.

PubMed

Zhang, Guangming; Tan, Hua; Qian, Xiaohua; Zhang, Jian; Li, King; David, Lisa R; Zhou, Xiaobo

2016-05-01

Spring-assisted surgery (SAS) can effectively treat scaphocephaly by reshaping crania with the appropriate spring force. However, it is difficult to accurately estimate spring force without considering biomechanical properties of tissues. This study presents and validates a reliable system to accurately predict the spring force for sagittal craniosynostosis surgery. The authors randomly chose 23 patients who underwent SAS and had been followed for at least 2 years. An elastic model was designed to characterize the biomechanical behavior of calvarial bone tissue for each individual. After simulating the contact force on accurate position of the skull strip with the springs, the finite element method was applied to calculating the stress of each tissue node based on the elastic model. A support vector regression approach was then used to model the relationships between biomechanical properties generated from spring force, bone thickness, and the change of cephalic index after surgery. Therefore, for a new patient, the optimal spring force can be predicted based on the learned model with virtual spring simulation and dynamic programming approach prior to SAS. Leave-one-out cross-validation was implemented to assess the accuracy of our prediction. As a result, the mean prediction accuracy of this model was 93.35%, demonstrating the great potential of this model as a useful adjunct for preoperative planning tool.

Biochemistry of Short-Chain Alkanes (Tissue-Specific Biosynthesis of n-Heptane in Pinus jeffreyi).

PubMed Central

Savage, T. J.; Hamilton, B. S.; Croteau, R.

1996-01-01

Short-chain (C7-C11) alkanes accumulate as the volatile component of oleoresin (pitch) in several pine species native to western North America. To establish the tissue most amenable for use in detailed studies of short-chain alkane biosynthesis, we examined the tissue specificity of alkane accumulation and biosynthesis in Pinus jeffreyi Grev. & Balf. Short-chain alkane accumulation was highly tissue specific in both 2-year-old saplings and mature trees; heart-wood xylem accumulated alkanes up to 7.1 mg g-1 dry weight, whereas needles and other young green tissue contained oleoresin with monoterpenoid, rather than paraffinic, volatiles. These tissue-specific differences in oleoresin composition appear to be a result of tissue-specific rates of alkane and monoterpene biosynthesis; incubation of xylem tissue with [14C]sucrose resulted in accumulation of radiolabel in alkanes but not monoterpenes, whereas incubation of foliar tissue with 14CO2 resulted in the accumulation of radiolabel in monoterpenes but not alkanes. Furthermore, incubation of xylem sections with [14C]acetate resulted in incorporation of radiolabel into alkanes at rates up to 1.7 nmol h-1 g-1 fresh weight, a rate that exceeds most biosynthetic rates reported with other plant systems for the incorporation of this basic precursor into natural products. This suggests that P. jeffreyi may provide a suitable model for elucidating the enzymology and molecular biology of short-chain alkane biosynthesis. PMID:12226177

Analysis of tissue specific progenitor cell differentiation using FT-IR

NASA Astrophysics Data System (ADS)

Ishii, Katsunori; Kimura, Akinori; Kushibiki, Toshihiro; Awazu, Kunio

2007-07-01

Tissue specific progenitor cells and its differentiations have got a lot of attentions in regenerative medicine. The process of differentiations, the formation of tissues, has become better understood by the study using a lot of cell types progressively. These studies of cells and tissue dynamics at molecular levels are carried out through various approaches like histochemical methods, application of molecular biology and immunology. However, in case of using regenerative sources (cells, tissues and biomaterials etc.) clinically, they are measured and quality-controlled by non-contact and non-destructive methods from the view point of safety. Or the analysis with small quantities of materials could be possible if the quantities of materials are acceptable. A non-contact and non-destructive quality control method has been required. Recently, the use of Fourier Transform Infrared spectroscopy (FT-IR) has been used to monitor biochemical changes in cells, and has gained considerable importance. The changes in the cells and tissues, which are subtle and often not obvious in the histpathological studies, are shown to be well resolved using FT-IR. Moreover, although most techniques designed to detect one or a few changes, FT-IR is possible to identify the changes in the levels of various cellular biochemicals simultaneously under in vivo and in vitro conditions. The objective of this study is to establish the infrared spectroscopy of tissue specific progenitor cell differentiations as a quality control of cell sources for regenerative medicine. In the present study, as a basic study, we examine the adipose differentiation kinetics of preadipose cells (3T3-L1) and the osteoblast differentiation kinetics of mesenchymal stem cells (Kusa-A1) to analyze the infrared absorption spectra.

Fluorescence confocal mosaicing microscopy of basal cell carcinomas ex vivo: demonstration of rapid surgical pathology with high sensitivity and specificity

NASA Astrophysics Data System (ADS)

Gareau, Daniel S.; Karen, Julie K.; Dusza, Stephen W.; Tudisco, Marie; Nehal, Kishwer S.; Rajadhyaksha, Milind

2009-02-01

Mohs surgery, for the precise removal of basal cell carcinomas (BCCs), consists of a series of excisions guided by the surgeon's examination of the frozen histology of the previous excision. The histology reveals atypical nuclear morphology, identifying cancer. The preparation of frozen histology is accurate but labor-intensive and slow. Nuclear pathology can be achieved by staining with acridine orange (1 mM, 20 s) BCCs in Mohs surgical skin excisions within 5-9 minutes, compared to 20-45 for frozen histology. For clinical utility, images must have high contrast and high resolution. We report tumor contrast of 10-100 fold over the background dermis and submicron (diffraction limited) resolution over a cm field of view. BCCs were detected with an overall sensitivity of 96.6%, specificity of 89.2%, positive predictive value of 93.0% and negative predictive value of 94.7%. The technique was therefore accurate for normal tissue as well as tumor. We conclude that fluorescence confocal mosaicing serves as a sensitive and rapid pathological tool. Beyond Mohs surgery, this technology may be extended to suit other pathological needs with the development of new contrast agents. The technique reported here accurately detects all subtypes of BCC in skin excisions, including the large nodular, small micronodular, and tiny sclerodermaform tumors. However, this technique may be applicable to imaging tissue that is larger, more irregular and of various mechanical compliances with further engineering of the tissue mounting and staging mechanisms.

Prediction and measurement of thermally induced cambial tissue necrosis in tree stems

Treesearch

Joshua L. Jones; Brent W. Webb; Bret W. Butler; Matthew B. Dickinson; Daniel Jimenez; James Reardon; Anthony S. Bova

2006-01-01

A model for fire-induced heating in tree stems is linked to a recently reported model for tissue necrosis. The combined model produces cambial tissue necrosis predictions in a tree stem as a function of heating rate, heating time, tree species, and stem diameter. Model accuracy is evaluated by comparison with experimental measurements in two hardwood and two softwood...

Exposure to air pollution interacts with obesogenic nutrition to induce tissue-specific response patterns.

PubMed

Pardo, Michal; Kuperman, Yael; Levin, Liron; Rudich, Assaf; Haim, Yulia; Schauer, James J; Chen, Alon; Rudich, Yinon

2018-04-20

Obesity and exposure to particular matter (PM) have become two leading global threats to public health. However, the exact mechanisms and tissue-specificity of their health effects are largely unknown. Here we investigate whether a metabolic challenge (early nutritional obesity) synergistically interacts with an environmental challenge (PM exposure) to alter genes representing key response pathways, in a tissue-specific manner. Mice subjected to 7 weeks obesogenic nutrition were exposed every other day during the final week and a half to aqueous extracts of PM collected in the city of London (UK). The expression of 61 selected genes representing key response pathways were investigated in lung, liver, white and brown adipose tissues. Principal component analysis (PCA) revealed distinct patterns of expression changes between the 4 tissues, particularly in the lungs and the liver. Surprisingly, the lung responded to the nutrition challenge. The response of these organs to the PM challenge displayed opposite patterns for some key genes, in particular, those related to the Nrf2 pathway. While the contribution to the variance in gene expression changes in mice exposed to the combined challenge were largely similar among the tissues in PCA1, PCA2 exhibited predominant contribution of inflammatory and oxidative stress responses to the variance in the lungs, and a greater contribution of autophagy genes and MAP kinases in adipose tissues. Possible involvement of alterations in DNA methylation was demonstrated by cell-type-specific responses to a methylation inhibitor. Correspondingly, the DNA methyltransferase Dnmt3a2 increased in the lungs but decreased in the liver, demonstrating potential tissue-differential synergism between nutritional and PM exposure. The results suggest that urban PM, containing dissolved metals, interacts with obesogenic nutrition to regulate diverse response pathways including inflammation and oxidative stress, in a tissue-specific manner. Tissue

Temporal, Diagnostic, and Tissue-Specific Regulation of NRG3 Isoform Expression in Human Brain Development and Affective Disorders.

PubMed

Paterson, Clare; Wang, Yanhong; Hyde, Thomas M; Weinberger, Daniel R; Kleinman, Joel E; Law, Amanda J

2017-03-01

Genes implicated in schizophrenia are enriched in networks differentially regulated during human CNS development. Neuregulin 3 (NRG3), a brain-enriched neurotrophin, undergoes alternative splicing and is implicated in several neurological disorders with developmental origins. Isoform-specific increases in NRG3 are observed in schizophrenia and associated with rs10748842, a NRG3 risk polymorphism, suggesting NRG3 transcriptional dysregulation as a molecular mechanism of risk. The authors quantitatively mapped the temporal trajectories of NRG3 isoforms (classes I-IV) in the neocortex throughout the human lifespan, examined whether tissue-specific regulation of NRG3 occurs in humans, and determined if abnormalities in NRG3 transcriptomics occur in mood disorders and are genetically determined. NRG3 isoform classes I-IV were quantified using quantitative real-time polymerase chain reaction in human postmortem dorsolateral prefrontal cortex from 286 nonpsychiatric control individuals, from gestational week 14 to 85 years old, and individuals diagnosed with either bipolar disorder (N=34) or major depressive disorder (N=69). Tissue-specific mapping was investigated in several human tissues. rs10748842 was genotyped in individuals with mood disorders, and association with NRG3 isoform expression examined. NRG3 classes displayed individually specific expression trajectories across human neocortical development and aging; classes I, II, and IV were significantly associated with developmental stage. NRG3 class I was increased in bipolar and major depressive disorder, consistent with observations in schizophrenia. NRG3 class II was increased in bipolar disorder, and class III was increased in major depression. The rs10748842 risk genotype predicted elevated class II and III expression, consistent with previous reports in the brain, with tissue-specific analyses suggesting that classes II and III are brain-specific isoforms of NRG3. Mapping the temporal expression of genes

Temporal, Diagnostic, and Tissue-Specific Regulation of NRG3 Isoform Expression in Human Brain Development and Affective Disorders

PubMed Central

Paterson, Clare; Wang, Yanhong; Hyde, Thomas M.; Weinberger, Daniel R.; Kleinman, Joel E.; Law, Amanda J.

2018-01-01

Objective Genes implicated in schizophrenia are enriched in networks differentially regulated during human CNS development. Neuregulin 3 (NRG3), a brain-enriched neurotrophin, undergoes alternative splicing and is implicated in several neurological disorders with developmental origins. Isoform-specific increases in NRG3 are observed in schizophrenia and associated with rs10748842, a NRG3 risk polymorphism, suggesting NRG3 transcriptional dysregulation as a molecular mechanism of risk. The authors quantitatively mapped the temporal trajectories of NRG3 isoforms (classes I–IV) in the neocortex throughout the human lifespan, examined whether tissue-specific regulation of NRG3 occurs in humans, and determined if abnormalities in NRG3 transcriptomics occur in mood disorders and are genetically determined. Method NRG3 isoform classes I–IV were quantified using quantitative real-time polymerase chain reaction in human postmortem dorsolateral prefrontal cortex from 286 nonpsychiatric control individuals, from gestational week 14 to 85 years old, and individuals diagnosed with either bipolar disorder (N=34) or major depressive disorder (N=69). Tissue-specific mapping was investigated in several human tissues. rs10748842 was genotyped in individuals with mood disorders, and association with NRG3 isoform expression examined. Results NRG3 classes displayed individually specific expression trajectories across human neocortical development and aging; classes I, II, and IV were significantly associated with developmental stage. NRG3 class I was increased in bipolar and major depressive disorder, consistent with observations in schizophrenia. NRG3 class II was increased in bipolar disorder, and class III was increased in major depression. The rs10748842 risk genotype predicted elevated class II and III expression, consistent with previous reports in the brain, with tissue-specific analyses suggesting that classes II and III are brain-specific isoforms of NRG3. Conclusions

Tissue-specific DNA methylation is conserved across human, mouse, and rat, and driven by primary sequence conservation.

PubMed

Zhou, Jia; Sears, Renee L; Xing, Xiaoyun; Zhang, Bo; Li, Daofeng; Rockweiler, Nicole B; Jang, Hyo Sik; Choudhary, Mayank N K; Lee, Hyung Joo; Lowdon, Rebecca F; Arand, Jason; Tabers, Brianne; Gu, C Charles; Cicero, Theodore J; Wang, Ting

2017-09-12

Uncovering mechanisms of epigenome evolution is an essential step towards understanding the evolution of different cellular phenotypes. While studies have confirmed DNA methylation as a conserved epigenetic mechanism in mammalian development, little is known about the conservation of tissue-specific genome-wide DNA methylation patterns. Using a comparative epigenomics approach, we identified and compared the tissue-specific DNA methylation patterns of rat against those of mouse and human across three shared tissue types. We confirmed that tissue-specific differentially methylated regions are strongly associated with tissue-specific regulatory elements. Comparisons between species revealed that at a minimum 11-37% of tissue-specific DNA methylation patterns are conserved, a phenomenon that we define as epigenetic conservation. Conserved DNA methylation is accompanied by conservation of other epigenetic marks including histone modifications. Although a significant amount of locus-specific methylation is epigenetically conserved, the majority of tissue-specific DNA methylation is not conserved across the species and tissue types that we investigated. Examination of the genetic underpinning of epigenetic conservation suggests that primary sequence conservation is a driving force behind epigenetic conservation. In contrast, evolutionary dynamics of tissue-specific DNA methylation are best explained by the maintenance or turnover of binding sites for important transcription factors. Our study extends the limited literature of comparative epigenomics and suggests a new paradigm for epigenetic conservation without genetic conservation through analysis of transcription factor binding sites.

Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging.

PubMed

Anbeek, Petronella; Vincken, Koen L; Groenendaal, Floris; Koeman, Annemieke; van Osch, Matthias J P; van der Grond, Jeroen

2008-02-01

A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.

Regulatory mechanisms for specification and patterning of plant vascular tissues.

PubMed

Caño-Delgado, Ana; Lee, Ji-Young; Demura, Taku

2010-01-01

Plant vascular tissues, the conduits of water, nutrients, and small molecules, play important roles in plant growth and development. Vascular tissues have allowed plants to successfully adapt to various environmental conditions since they evolved 450 Mya. The majority of plant biomass, an important source of renewable energy, comes from the xylem of the vascular tissues. Efforts have been made to identify the underlying mechanisms of cell specification and patterning of plant vascular tissues and their proliferation. The formation of the plant vascular system is a complex process that integrates signaling and gene regulation at transcriptional and posttranscriptional levels. Recently, a wealth of molecular genetic studies and the advent of cell biology and genomic tools have enabled important progress toward understanding its underlying mechanisms. Here, we provide a comprehensive review of the cell and developmental processes of plant vascular tissue and resources recently available for studying them that will enable the discovery of new ways to develop sustainable energy using plant biomass.

Urinary Squamous Epithelial Cells Do Not Accurately Predict Urine Culture Contamination, but May Predict Urinalysis Performance in Predicting Bacteriuria.

PubMed

Mohr, Nicholas M; Harland, Karisa K; Crabb, Victoria; Mutnick, Rachel; Baumgartner, David; Spinosi, Stephanie; Haarstad, Michael; Ahmed, Azeemuddin; Schweizer, Marin; Faine, Brett

2016-03-01

The presence of squamous epithelial cells (SECs) has been advocated to identify urinary contamination despite a paucity of evidence supporting this practice. We sought to determine the value of using quantitative SECs as a predictor of urinalysis contamination. Retrospective cross-sectional study of adults (≥18 years old) presenting to a tertiary academic medical center who had urinalysis with microscopy and urine culture performed. Patients with missing or implausible demographic data were excluded (2.5% of total sample). The primary analysis aimed to determine an SEC threshold that predicted urine culture contamination using receiver operating characteristics (ROC) curve analysis. The a priori secondary analysis explored how demographic variables (age, sex, body mass index) may modify the SEC test performance and whether SECs impacted traditional urinalysis indicators of bacteriuria. A total of 19,328 records were included. ROC curve analysis demonstrated that SEC count was a poor predictor of urine culture contamination (area under the ROC curve = 0.680, 95% confidence interval [CI] = 0.671 to 0.689). In secondary analysis, the positive likelihood ratio (LR+) of predicting bacteriuria via urinalysis among noncontaminated specimens was 4.98 (95% CI = 4.59 to 5.40) in the absence of SECs, but the LR+ fell to 2.35 (95% CI = 2.17 to 2.54) for samples with more than 8 SECs/low-powered field (lpf). In an independent validation cohort, urinalysis samples with fewer than 8 SECs/lpf predicted bacteriuria better (sensitivity = 75%, specificity = 84%) than samples with more than 8 SECs/lpf (sensitivity = 86%, specificity = 70%; diagnostic odds ratio = 17.5 [14.9 to 20.7] vs. 8.7 [7.3 to 10.5]). Squamous epithelial cells are a poor predictor of urine culture contamination, but may predict poor predictive performance of traditional urinalysis measures. © 2016 by the Society for Academic Emergency Medicine.

Method for accurate quantitation of background tissue optical properties in the presence of emission from a strong fluorescence marker

NASA Astrophysics Data System (ADS)

Bravo, Jaime; Davis, Scott C.; Roberts, David W.; Paulsen, Keith D.; Kanick, Stephen C.

2015-03-01

Quantification of targeted fluorescence markers during neurosurgery has the potential to improve and standardize surgical distinction between normal and cancerous tissues. However, quantitative analysis of marker fluorescence is complicated by tissue background absorption and scattering properties. Correction algorithms that transform raw fluorescence intensity into quantitative units, independent of absorption and scattering, require a paired measurement of localized white light reflectance to provide estimates of the optical properties. This study focuses on the unique problem of developing a spectral analysis algorithm to extract tissue absorption and scattering properties from white light spectra that contain contributions from both elastically scattered photons and fluorescence emission from a strong fluorophore (i.e. fluorescein). A fiber-optic reflectance device was used to perform measurements in a small set of optical phantoms, constructed with Intralipid (1% lipid), whole blood (1% volume fraction) and fluorescein (0.16-10 μg/mL). Results show that the novel spectral analysis algorithm yields accurate estimates of tissue parameters independent of fluorescein concentration, with relative errors of blood volume fraction, blood oxygenation fraction (BOF), and the reduced scattering coefficient (at 521 nm) of <7%, <1%, and <22%, respectively. These data represent a first step towards quantification of fluorescein in tissue in vivo.

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis.

PubMed

Liang, Liang; Liu, Minliang; Martin, Caitlin; Sun, Wei

2018-01-01

Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis. © 2018 The Author(s).

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

NASA Astrophysics Data System (ADS)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

PubMed Central

Xue, Yi; Skrynnikov, Nikolai R

2014-01-01

Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

PubMed Central

Garcia Lopez, Sebastian; Kim, Philip M.

2014-01-01

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints

PubMed Central

Suciu, Maria C.; Telenius, Jelena

2017-01-01

In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome. PMID:28904015

Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

PubMed

Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

2011-12-01

Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy. Copyright © 2011 Wiley-Liss, Inc.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain.

PubMed

Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J; Simonson, Thomas

2017-01-01

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or "PB/LIE" free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α 2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo . The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain

PubMed Central

Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J.; Simonson, Thomas

2017-01-01

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or “PB/LIE” free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo. The overall performance of the model should allow its use in the design of new PDZ ligands in the future. PMID:29018806

Increased lipoprotein lipase activity in non-small cell lung cancer tissue predicts shorter patient survival.

PubMed

Trost, Zoran; Sok, Miha; Marc, Janja; Cerne, Darko

2009-07-01

Cumulative evidence suggests the involvement of lipoprotein lipase (LPL) in tumor progression. We tested the hypothesis that increased LPL activity in resectable non-small cell lung cancer (NSCLC) tissue and the increased LPL gene expression in the surrounding non-cancer lung tissue found in our previous study are predictors of patient survival. Forty two consecutive patients with resected NSCLC were enrolled in the study. Paired samples of lung cancer tissue and adjacent non-cancer lung tissue were collected from resected specimens for baseline LPL activity and gene expression estimation. During a 4-year follow-up, 21 patients died due to tumor progression. One patient died due to a non-cancer reason and was not included in Cox regression analysis. High LPL activity in cancer tissue (relative to the adjacent non-cancer lung tissue) predicted shorter survival, independently of standard prognostic factors (p=0.003). High gene expression in the non-cancer lung tissue surrounding the tumor had no predictive value. Our study further underlines the involvement of cancer tissue LPL activity in tumor progression.

Estimating energy expenditure in vascular surgery patients: Are predictive equations accurate enough?

PubMed

Suen, J; Thomas, J M; Delaney, C L; Spark, J I; Miller, M D

2016-12-01

Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris-Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland-Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris-Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2  = 0.005, p = 0.54) and Harris-Benedict equations (R 2  = 0.045, p = 0.06) but was present in the Miller equation (R 2  = 0.210, p < 0.01) even after logarithmic transformation (R 2  = 0.213, p < 0.01). Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris-Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

2008-10-20

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

Concurrent musculoskeletal dynamics and finite element analysis predicts altered gait patterns to reduce foot tissue loading.

PubMed

Halloran, Jason P; Ackermann, Marko; Erdemir, Ahmet; van den Bogert, Antonie J

2010-10-19

Current computational methods for simulating locomotion have primarily used muscle-driven multibody dynamics, in which neuromuscular control is optimized. Such simulations generally represent joints and soft tissue as simple kinematic or elastic elements for computational efficiency. These assumptions limit application in studies such as ligament injury or osteoarthritis, where local tissue loading must be predicted. Conversely, tissue can be simulated using the finite element method with assumed or measured boundary conditions, but this does not represent the effects of whole body dynamics and neuromuscular control. Coupling the two domains would overcome these limitations and allow prediction of movement strategies guided by tissue stresses. Here we demonstrate this concept in a gait simulation where a musculoskeletal model is coupled to a finite element representation of the foot. Predictive simulations incorporated peak plantar tissue deformation into the objective of the movement optimization, as well as terms to track normative gait data and minimize fatigue. Two optimizations were performed, first without the strain minimization term and second with the term. Convergence to realistic gait patterns was achieved with the second optimization realizing a 44% reduction in peak tissue strain energy density. The study demonstrated that it is possible to alter computationally predicted neuromuscular control to minimize tissue strain while including desired kinematic and muscular behavior. Future work should include experimental validation before application of the methodology to patient care. Copyright © 2010 Elsevier Ltd. All rights reserved.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

PubMed

Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J; Frey, Juerg E; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H

2014-08-28

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction

Fusarium oxysporum Triggers Tissue-Specific Transcriptional Reprogramming in Arabidopsis thaliana

PubMed Central

Lyons, Rebecca; Stiller, Jiri; Powell, Jonathan; Rusu, Anca; Manners, John M.; Kazan, Kemal

2015-01-01

Some of the most devastating agricultural diseases are caused by root-infecting pathogens, yet the majority of studies on these interactions to date have focused on the host responses of aerial tissues rather than those belowground. Fusarium oxysporum is a root-infecting pathogen that causes wilt disease on several plant species including Arabidopsis thaliana. To investigate and compare transcriptional changes triggered by F. oxysporum in different Arabidopsis tissues, we infected soil-grown plants with F. oxysporum and subjected root and leaf tissue harvested at early and late timepoints to RNA-seq analyses. At least half of the genes induced or repressed by F. oxysporum showed tissue-specific regulation. Regulators of auxin and ABA signalling, mannose binding lectins and peroxidases showed strong differential expression in root tissue. We demonstrate that ARF2 and PRX33, two genes regulated in the roots, promote susceptibility to F. oxysporum. In the leaves, defensins and genes associated with the response to auxin, cold and senescence were strongly regulated while jasmonate biosynthesis and signalling genes were induced throughout the plant. PMID:25849296

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

PubMed

Jones, David T; Singh, Tanya; Kosciolek, Tomasz; Tetchner, Stuart

2015-04-01

Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Lung cancer signature biomarkers: tissue specific semantic similarity based clustering of digital differential display (DDD) data.

PubMed

Srivastava, Mousami; Khurana, Pankaj; Sugadev, Ragumani

2012-11-02

The tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used 'Gene Ontology semantic similarity score' to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability. Subsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1-4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4). Expression analysis reveals that hypoxia induced lung cancer related biomarkers (panel 3), HIF and its modulating proteins (TGM2, CSNK1A1, CTNNA1, NAMPT/Visfatin, TNFRSF1A, ETS1, SRC-1, FN1, APLP2, DMBT1/SAG, AIB1 and AZIN1) are significantly down regulated

Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

PubMed

Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

2017-09-01

Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

Melanoma metastases in regional lymph nodes are accurately detected by proton magnetic resonance spectroscopy of fine-needle aspirate biopsy samples.

PubMed

Stretch, Jonathan R; Somorjai, Ray; Bourne, Roger; Hsiao, Edward; Scolyer, Richard A; Dolenko, Brion; Thompson, John F; Mountford, Carolyn E; Lean, Cynthia L

2005-11-01

Nonsurgical assessment of sentinel nodes (SNs) would offer advantages over surgical SN excision by reducing morbidity and costs. Proton magnetic resonance spectroscopy (MRS) of fine-needle aspirate biopsy (FNAB) specimens identifies melanoma lymph node metastases. This study was undertaken to determine the accuracy of the MRS method and thereby establish a basis for the future development of a nonsurgical technique for assessing SNs. FNAB samples were obtained from 118 biopsy specimens from 77 patients during SN biopsy and regional lymphadenectomy. The specimens were histologically evaluated and correlated with MRS data. Histopathologic analysis established that 56 specimens contained metastatic melanoma and that 62 specimens were benign. A linear discriminant analysis-based classifier was developed for benign tissues and metastases. The presence of metastatic melanoma in lymph nodes was predicted with a sensitivity of 92.9%, a specificity of 90.3%, and an accuracy of 91.5% in a primary data set. In a second data set that used FNAB samples separate from the original tissue samples, melanoma metastases were predicted with a sensitivity of 87.5%, a specificity of 90.3%, and an accuracy of 89.1%, thus supporting the reproducibility of the method. Proton MRS of FNAB samples may provide a robust and accurate diagnosis of metastatic disease in the regional lymph nodes of melanoma patients. These data indicate the potential for SN staging of melanoma without surgical biopsy and histopathological evaluation.

Accurate prediction of cation-π interaction energy using substituent effects.

PubMed

Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

2012-06-14

(M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

Diurnal Changes in Volume and Specific Tissue Weight of Crassulacean Acid Metabolism Plants 1

PubMed Central

Chen, Sheng-Shu; Black, Clanton C.

1983-01-01

The diurnal variations in volume and in specific weight were determined for green stems and leaves of Crassulacen acid metabolism (CAM) plants. Volume changes were measured by a water displacement method. Diurnal variations occurred in the volume of green CAM tissues. Their volume increased early in the light period reaching a maximum about mid-day, then the volume decreased to a minimum near midnight. The maximum volume increase each day was about 2.7% of the total volume. Control leaves of C3 and C4 plants exhibited reverse diurnal volume changes of 0.2 to 0.4%. The hypothesis is presented and supported that green CAM tissues should exhibit a diurnal increase in volume due to the increase of internal gas pressure from CO2 and O2 when their stomata are closed. Conversely, the volume should decrease when the gas pressure is decreased. The second hypothesis presented and supported was that the specific weight (milligrams of dry weight per square centimeter of green surface area) of green CAM tissues should increase at night due to the net fixation of CO2. Green CAM tissues increased their specific weight at night in contrast to control C3 and C4 leaves which decreased their specific weight at night. With Kalanchoë daigremontiana leaves, the calculated increase in specific leaf weight at night based on estimates of carbohydrate available for net CO2 fixation was near 6% and the measured increase in specific leaf weight was 6%. Diurnal measurements of CAM tissue water content were neither coincident nor reciprocal with their diurnal patterns of either volume or specific weight changes. PMID:16662833

Literature-based condition-specific miRNA-mRNA target prediction.

PubMed

Oh, Minsik; Rhee, Sungmin; Moon, Ji Hwan; Chae, Heejoon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2017-01-01

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary

Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.

PubMed

Schwessinger, Ron; Suciu, Maria C; McGowan, Simon J; Telenius, Jelena; Taylor, Stephen; Higgs, Doug R; Hughes, Jim R

2017-10-01

In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k -mer-based analysis of DNase footprints to determine any k -mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome. © 2017 Schwessinger et al.; Published by Cold Spring Harbor Laboratory Press.

Explicit formula of finite difference method to estimate human peripheral tissue temperatures during exposure to severe cold stress.

PubMed

Khanday, M A; Hussain, Fida

2015-02-01

During cold exposure, peripheral tissues undergo vasoconstriction to minimize heat loss to preserve the maintenance of a normal core temperature. However, vasoconstricted tissues exposed to cold temperatures are susceptible to freezing and frostbite-related tissue damage. Therefore, it is imperative to establish a mathematical model for the estimation of tissue necrosis due to cold stress. To this end, an explicit formula of finite difference method has been used to obtain the solution of Pennes' bio-heat equation with appropriate boundary conditions to estimate the temperature profiles of dermal and subdermal layers when exposed to severe cold temperatures. The discrete values of nodal temperature were calculated at the interfaces of skin and subcutaneous tissues with respect to the atmospheric temperatures of 25 °C, 20 °C, 15 °C, 5 °C, -5 °C and -10 °C. The results obtained were used to identify the scenarios under which various degrees of frostbite occur on the surface of skin as well as the dermal and subdermal areas. The explicit formula of finite difference method proposed in this model provides more accurate predictions as compared to other numerical methods. This model of predicting tissue temperatures provides researchers with a more accurate prediction of peripheral tissue temperature and, hence, the susceptibility to frostbite during severe cold exposure. Copyright © 2014 Elsevier Ltd. All rights reserved.

Specification of embryonic stem cell-derived tissues into eye fields by Wnt signaling using rostral diencephalic tissue-inducing culture.

PubMed

Sakakura, Eriko; Eiraku, Mototsugu; Takata, Nozomu

2016-08-01

The eyes are subdivided from the rostral diencephalon in early development. How the neuroectoderm regulates this subdivision, however, is largely unknown. Taking advantage of embryonic stem cell (ESC) culture using a Rax reporter line to monitor rostral diencephalon formation, we found that ESC-derived tissues at day 7 grown in Glasgow Minimum Expression Media (GMEM) containing knockout serum replacement (KSR) exhibited higher levels of expression of axin2, a Wnt target gene, than those grown in chemically defined medium (CDM). Surprisingly, Wnt agonist facilitated eye field-like tissue specification in CDM. In contrast, the addition of Wnt antagonist diminished eye field tissue formation in GMEM+KSR. Furthermore, the morphological formation of the eye tissue anlage, including the optic vesicle, was accompanied by Wnt signaling activation. Additionally, using CDM culture, we developed an efficient method for generating Rax+/Chx10+ retinal progenitors, which could become fully stratified retina. Here we provide a new avenue for exploring the mechanisms of eye field specification in vitro. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Transcriptomics reveals tissue/organ-specific differences in gene expression in the starfish Patiria pectinifera.

PubMed

Kim, Chan-Hee; Go, Hye-Jin; Oh, Hye Young; Jo, Yong Hun; Elphick, Maurice R; Park, Nam Gyu

2018-02-01

Starfish (Phylum Echinodermata) are of interest from an evolutionary perspective because as deuterostomian invertebrates they occupy an "intermediate" phylogenetic position with respect to chordates (e.g. vertebrates) and protostomian invertebrates (e.g. Drosophila). Furthermore, starfish are model organisms for research on fertilization, embryonic development, innate immunity and tissue regeneration. However, large-scale molecular data for starfish tissues/organs are limited. To provide a comprehensive genetic resource for the starfish Patiria pectinifera, we report de novo transcriptome assemblies and global gene expression analysis for six P. pectinifera tissues/organs - body wall (BW), coelomic epithelium (CE), tube feet (TF), stomach (SM), pyloric caeca (PC) and gonad (GN). A total of 408 million high-quality reads obtained from six cDNA libraries were assembled de novo using Trinity, resulting in a total of 549,598 contigs with a mean length of 835 nucleotides (nt), an N50 of 1473nt, and GC ratio of 42.5%. A total of 126,136 contigs (22.9%) were obtained as predicted open reading frames (ORFs) by TransDecoder, of which 102,187 were annotated with NCBI non-redundant (NR) hits, and 51,075 and 10,963 were annotated with Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) using the Blast2GO program, respectively. Gene expression analysis revealed that tissues/organs are grouped into three clusters: BW/CE/TF, SM/PC, and GN, which likely reflect functional relationships. 2408, 8560, 2687, 1727, 3321, and 2667 specifically expressed genes were identified for BW, GN, PC, CE, SM and TF, respectively, using the ROKU method. This study provides a valuable transcriptome resource and novel molecular insights into the functional biology of different tissues/organs in starfish as a model organism. Copyright © 2017 Elsevier B.V. All rights reserved.

Shortened acquisition protocols for the quantitative assessment of the 2-tissue-compartment model using dynamic PET/CT 18F-FDG studies.

PubMed

Strauss, Ludwig G; Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2011-03-01

(18)F-FDG kinetics are quantified by a 2-tissue-compartment model. The routine use of dynamic PET is limited because of this modality's 1-h acquisition time. We evaluated shortened acquisition protocols up to 0-30 min regarding the accuracy for data analysis with the 2-tissue-compartment model. Full dynamic series for 0-60 min were analyzed using a 2-tissue-compartment model. The time-activity curves and the resulting parameters for the model were stored in a database. Shortened acquisition data were generated from the database using the following time intervals: 0-10, 0-16, 0-20, 0-25, and 0-30 min. Furthermore, the impact of adding a 60-min uptake value to the dynamic series was evaluated. The datasets were analyzed using dedicated software to predict the results of the full dynamic series. The software is based on a modified support vector machines (SVM) algorithm and predicts the compartment parameters of the full dynamic series. The SVM-based software provides user-independent results and was accurate at predicting the compartment parameters of the full dynamic series. If a squared correlation coefficient of 0.8 (corresponding to 80% explained variance of the data) was used as a limit, a shortened acquisition of 0-16 min was accurate at predicting the 60-min 2-tissue-compartment parameters. If a limit of 0.9 (90% explained variance) was used, a dynamic series of at least 0-20 min together with the 60-min uptake values is required. Shortened acquisition protocols can be used to predict the parameters of the 2-tissue-compartment model. Either a dynamic PET series of 0-16 min or a combination of a dynamic PET/CT series of 0-20 min and a 60-min uptake value is accurate for analysis with a 2-tissue-compartment model.

Extensive variation between tissues in allele specific expression in an outbred mammal.

PubMed

Chamberlain, Amanda J; Vander Jagt, Christy J; Hayes, Benjamin J; Khansefid, Majid; Marett, Leah C; Millen, Catriona A; Nguyen, Thuy T T; Goddard, Michael E

2015-11-23

Allele specific gene expression (ASE), with the paternal allele more expressed than the maternal allele or vice versa, appears to be a common phenomenon in humans and mice. In other species the extent of ASE is unknown, and even in humans and mice there are several outstanding questions. These include; to what extent is ASE tissue specific? how often does the direction of allele expression imbalance reverse between tissues? how often is only one of the two alleles expressed? is there a genome wide bias towards expression of the paternal or maternal allele; and finally do genes that are nearby on a chromosome share the same direction of ASE? Here we use gene expression data (RNASeq) from 18 tissues from a single cow to investigate each of these questions in turn, and then validate some of these findings in two tissues from 20 cows. Between 40 and 100 million sequence reads were generated per tissue across three replicate samples for each of the eighteen tissues from the single cow (the discovery dataset). A bovine gene expression atlas was created (the first from RNASeq data), and differentially expressed genes in each tissue were identified. To analyse ASE, we had access to unambiguously phased genotypes for all heterozygous variants in the cow's whole genome sequence, where these variants were homozygous in the whole genome sequence of her sire, and as a result we were able to map reads to parental genomes, to determine SNP and genes showing ASE in each tissue. In total 25,251 heterozygous SNP within 7985 genes were tested for ASE in at least one tissue. ASE was pervasive, 89 % of genes tested had significant ASE in at least one tissue. This large proportion of genes displaying ASE was confirmed in the two tissues in a validation dataset. For individual tissues the proportion of genes showing significant ASE varied from as low as 8-16 % of those tested in thymus to as high as 71-82 % of those tested in lung. There were a number of cases where the direction of

Hypoxia as a target for tissue specific gene therapy.

PubMed

Rhim, Taiyoun; Lee, Dong Yun; Lee, Minhyung

2013-12-10

Hypoxia is a hallmark of various ischemic diseases such as ischemic heart disease, ischemic limb, ischemic stroke, and solid tumors. Gene therapies for these diseases have been developed with various therapeutic genes including growth factors, anti-apoptotic genes, and toxins. However, non-specific expression of these therapeutic genes may induce dangerous side effects in the normal tissues. To avoid the side effects, gene expression should be tightly regulated in an oxygen concentration dependent manner. The hypoxia inducible promoters and enhancers have been evaluated as a transcriptional regulation tool for hypoxia inducible gene therapy. The hypoxia inducible UTRs were also used in gene therapy for spinal cord injury as a translational regulation strategy. In addition to transcriptional and translational regulations, post-translational regulation strategies have been developed using the HIF-1α ODD domain. Hypoxia inducible transcriptional, translational, and post-translational regulations are useful for tissue specific gene therapy of ischemic diseases. In this review, hypoxia inducible gene expression systems are discussed and their applications are introduced. Copyright © 2013 Elsevier B.V. All rights reserved.

Tissue-specific insulin signaling mediates female sexual attractiveness

PubMed Central

Arbuthnott, Devin; Rundle, Howard D.; Promislow, Daniel E. L.; Pletcher, Scott D.

2017-01-01

Individuals choose their mates so as to maximize reproductive success, and one important component of this choice is assessment of traits reflecting mate quality. Little is known about why specific traits are used for mate quality assessment nor about how they reflect it. We have previously shown that global manipulation of insulin signaling, a nutrient-sensing pathway governing investment in survival versus reproduction, affects female sexual attractiveness in the fruit fly, Drosophila melanogaster. Here we demonstrate that these effects on attractiveness derive from insulin signaling in the fat body and ovarian follicle cells, whose signals are integrated by pheromone-producing cells called oenocytes. Functional ovaries were required for global insulin signaling effects on attractiveness, and manipulations of insulin signaling specifically in late follicle cells recapitulated effects of global manipulations. Interestingly, modulation of insulin signaling in the fat body produced opposite effects on attractiveness, suggesting a competitive relationship with the ovary. Furthermore, all investigated tissue-specific insulin signaling manipulations that changed attractiveness also changed fecundity in the corresponding direction, pointing to insulin pathway activity as a reliable link between fecundity and attractiveness cues. The cues themselves, cuticular hydrocarbons, responded distinctly to fat body and follicle cell manipulations, indicating independent readouts of the pathway activity from these two tissues. Thus, here we describe a system in which female attractiveness results from an apparent connection between attractiveness cues and an organismal state of high fecundity, both of which are created by lowered insulin signaling in the fat body and increased insulin signaling in late follicle cells. PMID:28817572

Tissue-specific insulin signaling mediates female sexual attractiveness.

PubMed

Fedina, Tatyana Y; Arbuthnott, Devin; Rundle, Howard D; Promislow, Daniel E L; Pletcher, Scott D

2017-08-01

Individuals choose their mates so as to maximize reproductive success, and one important component of this choice is assessment of traits reflecting mate quality. Little is known about why specific traits are used for mate quality assessment nor about how they reflect it. We have previously shown that global manipulation of insulin signaling, a nutrient-sensing pathway governing investment in survival versus reproduction, affects female sexual attractiveness in the fruit fly, Drosophila melanogaster. Here we demonstrate that these effects on attractiveness derive from insulin signaling in the fat body and ovarian follicle cells, whose signals are integrated by pheromone-producing cells called oenocytes. Functional ovaries were required for global insulin signaling effects on attractiveness, and manipulations of insulin signaling specifically in late follicle cells recapitulated effects of global manipulations. Interestingly, modulation of insulin signaling in the fat body produced opposite effects on attractiveness, suggesting a competitive relationship with the ovary. Furthermore, all investigated tissue-specific insulin signaling manipulations that changed attractiveness also changed fecundity in the corresponding direction, pointing to insulin pathway activity as a reliable link between fecundity and attractiveness cues. The cues themselves, cuticular hydrocarbons, responded distinctly to fat body and follicle cell manipulations, indicating independent readouts of the pathway activity from these two tissues. Thus, here we describe a system in which female attractiveness results from an apparent connection between attractiveness cues and an organismal state of high fecundity, both of which are created by lowered insulin signaling in the fat body and increased insulin signaling in late follicle cells.

Cell- and Tissue-Specific Transcriptome Analyses of Medicago truncatula Root Nodules

PubMed Central

Limpens, Erik; Moling, Sjef; Hooiveld, Guido; Pereira, Patrícia A.; Bisseling, Ton; Becker, Jörg D.; Küster, Helge

2013-01-01

Legumes have the unique ability to host nitrogen-fixing Rhizobium bacteria as symbiosomes inside root nodule cells. To get insight into this key process, which forms the heart of the endosymbiosis, we isolated specific cells/tissues at different stages of symbiosome formation from nodules of the model legume Medicago truncatula using laser-capture microdissection. Next, we determined their associated expression profiles using Affymetrix Medicago GeneChips. Cells were collected from the nodule infection zone divided into a distal (where symbiosome formation and division occur) and proximal region (where symbiosomes are mainly differentiating), as well as infected cells from the fixation zone containing mature nitrogen fixing symbiosomes. As non-infected cells/tissue we included nodule meristem cells and uninfected cells from the fixation zone. Here, we present a comprehensive gene expression map of an indeterminate Medicago nodule and selected genes that show specific enriched expression in the different cells or tissues. Validation of the obtained expression profiles, by comparison to published gene expression profiles and experimental verification, indicates that the data can be used as digital “in situ”. This digital “in situ” offers a genome-wide insight into genes specifically associated with subsequent stages of symbiosome and nodule cell development, and can serve to guide future functional studies. PMID:23734198

Drosophila CLOCK target gene characterization: implications for circadian tissue-specific gene expression

PubMed Central

Abruzzi, Katharine Compton; Rodriguez, Joseph; Menet, Jerome S.; Desrochers, Jennifer; Zadina, Abigail; Luo, Weifei; Tkachev, Sasha; Rosbash, Michael

2011-01-01

CLOCK (CLK) is a master transcriptional regulator of the circadian clock in Drosophila. To identify CLK direct target genes and address circadian transcriptional regulation in Drosophila, we performed chromatin immunoprecipitation (ChIP) tiling array assays (ChIP–chip) with a number of circadian proteins. CLK binding cycles on at least 800 sites with maximal binding in the early night. The CLK partner protein CYCLE (CYC) is on most of these sites. The CLK/CYC heterodimer is joined 4–6 h later by the transcriptional repressor PERIOD (PER), indicating that the majority of CLK targets are regulated similarly to core circadian genes. About 30% of target genes also show cycling RNA polymerase II (Pol II) binding. Many of these generate cycling RNAs despite not being documented in prior RNA cycling studies. This is due in part to different RNA isoforms and to fly head tissue heterogeneity. CLK has specific targets in different tissues, implying that important CLK partner proteins and/or mechanisms contribute to gene-specific and tissue-specific regulation. PMID:22085964

Engineering a functional three-dimensional human cardiac tissue model for drug toxicity screening.

PubMed

Lu, Hong Fang; Leong, Meng Fatt; Lim, Tze Chiun; Chua, Ying Ping; Lim, Jia Kai; Du, Chan; Wan, Andrew C A

2017-05-11

Cardiotoxicity is one of the major reasons for clinical drug attrition. In vitro tissue models that can provide efficient and accurate drug toxicity screening are highly desired for preclinical drug development and personalized therapy. Here, we report the fabrication and characterization of a human cardiac tissue model for high throughput drug toxicity studies. Cardiac tissues were fabricated via cellular self-assembly of human transgene-free induced pluripotent stem cells-derived cardiomyocytes in pre-fabricated polydimethylsiloxane molds. The formed tissue constructs expressed cardiomyocyte-specific proteins, exhibited robust production of extracellular matrix components such as laminin, collagen and fibronectin, aligned sarcomeric organization, and stable spontaneous contractions for up to 2 months. Functional characterization revealed that the cardiac cells cultured in 3D tissues exhibited higher contraction speed and rate, and displayed a significantly different drug response compared to cells cultured in age-matched 2D monolayer. A panel of clinically relevant compounds including antibiotic, antidiabetic and anticancer drugs were tested in this study. Compared to conventional viability assays, our functional contractility-based assays were more sensitive in predicting drug-induced cardiotoxic effects, demonstrating good concordance with clinical observations. Thus, our 3D cardiac tissue model shows great potential to be used for early safety evaluation in drug development and drug efficiency testing for personalized therapy.

Extensive tissue-specific transcriptomic plasticity in maize primary roots upon water deficit.

PubMed

Opitz, Nina; Marcon, Caroline; Paschold, Anja; Malik, Waqas Ahmed; Lithio, Andrew; Brandt, Ronny; Piepho, Hans-Peter; Nettleton, Dan; Hochholdinger, Frank

2016-02-01

Water deficit is the most important environmental constraint severely limiting global crop growth and productivity. This study investigated early transcriptome changes in maize (Zea mays L.) primary root tissues in response to moderate water deficit conditions by RNA-Sequencing. Differential gene expression analyses revealed a high degree of plasticity of the water deficit response. The activity status of genes (active/inactive) was determined by a Bayesian hierarchical model. In total, 70% of expressed genes were constitutively active in all tissues. In contrast, <3% (50 genes) of water deficit-responsive genes (1915) were consistently regulated in all tissues, while >75% (1501 genes) were specifically regulated in a single root tissue. Water deficit-responsive genes were most numerous in the cortex of the mature root zone and in the elongation zone. The most prominent functional categories among differentially expressed genes in all tissues were 'transcriptional regulation' and 'hormone metabolism', indicating global reprogramming of cellular metabolism as an adaptation to water deficit. Additionally, the most significant transcriptomic changes in the root tip were associated with cell wall reorganization, leading to continued root growth despite water deficit conditions. This study provides insight into tissue-specific water deficit responses and will be a resource for future genetic analyses and breeding strategies to develop more drought-tolerant maize cultivars. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis.

PubMed

Taroni, Jaclyn N; Greene, Casey S; Martyanov, Viktor; Wood, Tammara A; Christmann, Romy B; Farber, Harrison W; Lafyatis, Robert A; Denton, Christopher P; Hinchcliff, Monique E; Pioli, Patricia A; Mahoney, J Matthew; Whitfield, Michael L

2017-03-23

Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology. We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast the skin and lung microenvironments and to assess the functional role of the inflammatory and fibrotic genes in each organ. Lastly, we tested the expression of macrophage activation state-associated gene sets for enrichment in skin and lung using a Wilcoxon rank sum test. We identified a common pathogenic gene expression signature-an immune-fibrotic axis-indicative of pro-fibrotic macrophages (MØs) in multiple tissues (skin, lung, esophagus, and peripheral blood mononuclear cells) affected by SSc. While the co-expression of these genes is common to all tissues, the functional consequences of this upregulation differ by organ. We used this disease-associated signature to query tissue-specific functional genomic networks to identify common and tissue-specific pathologies of SSc and related conditions. In contrast to skin, in the lung-specific functional network we identify a distinct lung-resident MØ signature associated with lipid stimulation and alternative activation. In keeping with our network results, we find distinct MØ alternative activation transcriptional programs in SSc-associated PF lung and in the skin of patients with an "inflammatory" SSc gene expression signature. Our results suggest that the innate immune

Tissue-specific mutation accumulation in human adult stem cells during life

NASA Astrophysics Data System (ADS)

Blokzijl, Francis; de Ligt, Joep; Jager, Myrthe; Sasselli, Valentina; Roerink, Sophie; Sasaki, Nobuo; Huch, Meritxell; Boymans, Sander; Kuijk, Ewart; Prins, Pjotr; Nijman, Isaac J.; Martincorena, Inigo; Mokry, Michal; Wiegerinck, Caroline L.; Middendorp, Sabine; Sato, Toshiro; Schwank, Gerald; Nieuwenhuis, Edward E. S.; Verstegen, Monique M. A.; van der Laan, Luc J. W.; de Jonge, Jeroen; Ijzermans, Jan N. M.; Vries, Robert G.; van de Wetering, Marc; Stratton, Michael R.; Clevers, Hans; Cuppen, Edwin; van Boxtel, Ruben

2016-10-01

The gradual accumulation of genetic mutations in human adult stem cells (ASCs) during life is associated with various age-related diseases, including cancer. Extreme variation in cancer risk across tissues was recently proposed to depend on the lifetime number of ASC divisions, owing to unavoidable random mutations that arise during DNA replication. However, the rates and patterns of mutations in normal ASCs remain unknown. Here we determine genome-wide mutation patterns in ASCs of the small intestine, colon and liver of human donors with ages ranging from 3 to 87 years by sequencing clonal organoid cultures derived from primary multipotent cells. Our results show that mutations accumulate steadily over time in all of the assessed tissue types, at a rate of approximately 40 novel mutations per year, despite the large variation in cancer incidence among these tissues. Liver ASCs, however, have different mutation spectra compared to those of the colon and small intestine. Mutational signature analysis reveals that this difference can be attributed to spontaneous deamination of methylated cytosine residues in the colon and small intestine, probably reflecting their high ASC division rate. In liver, a signature with an as-yet-unknown underlying mechanism is predominant. Mutation spectra of driver genes in cancer show high similarity to the tissue-specific ASC mutation spectra, suggesting that intrinsic mutational processes in ASCs can initiate tumorigenesis. Notably, the inter-individual variation in mutation rate and spectra are low, suggesting tissue-specific activity of common mutational processes throughout life.

Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system

PubMed Central

Zhang, Jialing; Rector, John; Lin, John Q.; Young, Jonathan H.; Sans, Marta; Katta, Nitesh; Giese, Noah; Yu, Wendong; Nagi, Chandandeep; Suliburk, James; Liu, Jinsong; Bensussan, Alena; DeHoog, Rachel J.; Garza, Kyana Y.; Ludolph, Benjamin; Sorace, Anna G.; Syed, Anum; Zahedivash, Aydin; Milner, Thomas E.; Eberlin, Livia S.

2018-01-01

Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis. PMID:28878011

Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyar, M. Darby; McCanta, Molly; Breves, Elly

2016-03-01

Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyar, M. Darby; McCanta, Molly; Breves, Elly

2016-03-01

Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe 3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yieldmore » accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

Poliovirus intrahost evolution is required to overcome tissue-specific innate immune responses.

PubMed

Xiao, Yinghong; Dolan, Patrick Timothy; Goldstein, Elizabeth Faul; Li, Min; Farkov, Mikhail; Brodsky, Leonid; Andino, Raul

2017-08-29

RNA viruses, such as poliovirus, have a great evolutionary capacity, allowing them to quickly adapt and overcome challenges encountered during infection. Here we show that poliovirus infection in immune-competent mice requires adaptation to tissue-specific innate immune microenvironments. The ability of the virus to establish robust infection and virulence correlates with its evolutionary capacity. We further identify a region in the multi-functional poliovirus protein 2B as a hotspot for the accumulation of minor alleles that facilitate a more effective suppression of the interferon response. We propose that population genetic dynamics enables poliovirus spread between tissues through optimization of the genetic composition of low frequency variants, which together cooperate to circumvent tissue-specific challenges. Thus, intrahost virus evolution determines pathogenesis, allowing a dynamic regulation of viral functions required to overcome barriers to infection.RNA viruses, such as polioviruses, have a great evolutionary capacity and can adapt quickly during infection. Here, the authors show that poliovirus infection in mice requires adaptation to innate immune microenvironments encountered in different tissues.

Acute Hypercortisolemia Exerts Depot-Specific Effects on Abdominal and Femoral Adipose Tissue Function

PubMed Central

O’Reilly, Michael W.; Bujalska, Iwona J.; Tomlinson, Jeremy W.; Arlt, Wiebke

2017-01-01

Context: Glucocorticoids have pleiotropic metabolic functions, and acute glucocorticoid excess affects fatty acid metabolism, increasing systemic lipolysis. Whether glucocorticoids exert adipose tissue depot-specific effects remains unclear. Objective: To provide an in vivo assessment of femoral and abdominal adipose tissue responses to acute glucocorticoid administration. Design and Outcome Measures: Nine healthy male volunteers were studied on two occasions, after a hydrocortisone infusion (0.2 mg/kg/min for 14 hours) and a saline infusion, respectively, given in randomized double-blind order. The subjects were studied in the fasting state and after a 75-g glucose drink with an in vivo assessment of femoral adipose tissue blood flow (ATBF) using radioactive xenon washout and of lipolysis and glucose uptake using the arteriovenous difference technique. In a separate study (same infusion design), eight additional healthy male subjects underwent assessment of fasting abdominal ATBF and lipolysis only. Lipolysis was assessed as the net release of nonesterified fatty acids (NEFAs) from femoral and abdominal subcutaneous adipose tissue. Results: Acute hypercortisolemia significantly increased basal and postprandial ATBF in femoral adipose tissue, but the femoral net NEFA release did not change. In abdominal adipose tissue, hypercortisolemia induced substantial increases in basal ATBF and NEFA release. Conclusions: Acute hypercortisolemia induces differential lipolysis and ATBF responses in abdominal and femoral adipose tissue, suggesting depot-specific glucocorticoid effects. Abdominal, but not femoral, adipose tissue contributes to the hypercortisolemia-induced systemic NEFA increase, with likely contributions from other adipose tissue sources and intravascular triglyceride hydrolysis. PMID:28323916

The Relation of Codon Bias to Tissue-Specific Gene Expression in Arabidopsis thaliana

PubMed Central

Camiolo, Salvatore; Farina, Lorenzo; Porceddu, Andrea

2012-01-01

The codon composition of coding sequences plays an important role in the regulation of gene expression. Herein, we report systematic differences in the usage of synonymous codons among Arabidopsis thaliana genes that are expressed specifically in distinct tissues. Although we observed that both regionally and transcriptionally associated mutational biases were associated significantly with codon bias, they could not explain the observed differences fully. Similarly, given that transcript abundances did not account for the differences in codon usage, it is unlikely that selection for translational efficiency can account exclusively for the observed codon bias. Thus, we considered the possible evolution of codon bias as an adaptive response to the different abundances of tRNAs in different tissues. Our analysis demonstrated that in some cases, codon usage in genes that were expressed in a broad range of tissues was influenced primarily by the tissue in which the gene was expressed maximally. On the basis of this finding we propose that genes that are expressed in certain tissues might show a tissue-specific compositional signature in relation to codon usage. These findings might have implications for the design of transgenes in relation to optimizing their expression. PMID:22865738

Histology-specific therapy for advanced soft tissue sarcoma and benign connective tissue tumors.

PubMed

Silk, Ann W; Schuetze, Scott M

2012-09-01

Molecularly targeted agents have shown activity in soft tissue sarcoma (STS) and benign connective tissue tumors over the past ten years, but response rates differ by histologic subtype. The field of molecularly targeted agents in sarcoma is increasingly complex. Often, clinicians must rely on phase II data or even case series due to the rarity of these diseases. In subtypes with a clear role of specific factors in the pathophysiology of disease, such as giant cell tumor of the bone and diffuse-type tenosynovial giant cell tumor, it is reasonable to treat with newer targeted therapies, when available, in place of chemotherapy when systemic treatment is needed to control disease. In diseases without documented implication of a pathway in disease pathogenesis (e.g. soft tissue sarcoma and vascular endothelial growth factor), clear benefit from drug treatment should be established in randomized phase III trials before implementation into routine clinical practice. Histologic subtype will continue to emerge as a critical factor in treatment selection as we learn more about the molecular drivers of tumor growth and survival in different subtypes. Many of the drugs that have been recently developed affect tumor growth more than survival, therefore progression-free survival may be a more clinically relevant intermediate endpoint than objective response rate using Response Evaluation Criteria In Solid Tumors (RECIST) in early phase sarcoma trials. Because of the rarity of disease and increasing need for multidisciplinary management, patients with connective tissue tumors should be evaluated at a center with expertise in these diseases. Participation in clinical trials, when available, is highly encouraged.

Accurate interatomic force fields via machine learning with covariant kernels

NASA Astrophysics Data System (ADS)

Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

2017-06-01

We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

Thermal and molecular investigation of laser tissue welding

NASA Astrophysics Data System (ADS)

Small, Ward, IV

Despite the growing number of successful animal and human trials, the exact mechanisms of laser tissue welding remain unknown. Furthermore, the effects of laser heating on tissue on the molecular scale are not fully understood. To address these issues, a multi-front attack on both extrinsic (solder/patch mediated) and intrinsic (laser only) tissue welding was launched using two-color infrared thermometry, computer modeling, weld strength assessment, biochemical assays, and vibrational spectroscopy. The coupling of experimentally measured surface temperatures with the predictive numerical simulations provided insight into the sub surface dynamics of the laser tissue welding process. Quantification of the acute strength of the welds following the welding procedure enabled comparison among trials during an experiment, with previous experiments, and with other studies in the literature. The acute weld integrity also provided an indication of the probability of long-term success. Molecular effects induced in the tissue by laser irradiation were investigated by measuring the concentrations of specific collagen covalent crosslinks and measuring the infrared absorption spectra before and after the laser exposure. This investigation yielded results pertaining to both the methods and mechanisms of laser tissue welding. The combination of two-color infrared thermometry to obtain accurate surface temperatures free from emissivity bias and computer modeling illustrated the importance of including evaporation in the simulations, which effectively serves as an inherent cooling mechanism during laser irradiation. Moreover, the hydration state predicted by the model was useful in assessing the role of electrostatic versus covalent bonding in the fusion. These tools also helped elicit differences between dye- enhanced liquid solders and solid-matrix patches in laser-assisted tissue welding, demonstrating the significance of repeatable energy delivery. Surprisingly, covalent bonds

Identification of candidate biomarkers with cancer-specific glycosylation in the tissue and serum of endometrioid ovarian cancer patients by glycoproteomic analysis

PubMed Central

Abbott, Karen L.; Lim, Jae-Min; Wells, Lance; Benigno, Benedict B.; McDonald, John F.; Pierce, Michael

2016-01-01

Epithelial ovarian cancer is diagnosed less than 25% of the time when the cancer is confined to the ovary, leading to 5-year survival rates of less than 30%. Therefore, there is an urgent need for early diagnostics for ovarian cancer. Our study using glycotranscriptome comparative analysis of endometrioid ovarian cancer tissue and normal ovarian tissue led to the identification of distinct differences in the transcripts of a restricted set of glycosyltransferases involved in N-linked glycosylation. Utilizing lectins that bind to glycan structures predicted to show changes, we observed differences in lectin-bound glycoproteins consistent with some of the transcript differences. In this study, we have extended our observations by the use of selected lectins to perform a targeted glycoproteomic analysis of ovarian cancer and normal ovarian tissues. Our results have identified several glycoproteins that display tumor-specific glycosylation changes. We have verified these glycosylation changes on glycoproteins from tissue using immunoprecipitation followed by lectin blot detection. The glycoproteins that were verified were then analyzed further using existing microarray data obtained from benign ovarian adenomas, borderline ovarian adenocarcinomas, and malignant ovarian adenocarcinomas. The verified glycoproteins found to be expressed above control levels in the microarray data sets were then screened for tumor-specific glycan modifications in serum from ovarian cancer patients. Results obtained from two of these glycoprotein markers, periostin and thrombospondin, have confirmed that tumor-specific glycan changes can be used to distinguish ovarian cancer patient serum from normal serum. PMID:19953551

Accurate indel prediction using paired-end short reads

PubMed Central

2013-01-01

Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

Characteristics of microRNAs enriched in specific cell types and primary tissue types in solid organs.

PubMed

Kriegel, Alison J; Liu, Yong; Liu, Pengyuan; Baker, Maria Angeles; Hodges, Matthew R; Hua, Xing; Liang, Mingyu

2013-12-01

Knowledge of miRNA expression and function in specific cell types in solid organs is limited because of difficulty in obtaining appropriate specimens. We used laser capture microdissection to obtain nine tissue regions from rats, including the nucleus of the solitary tract, hypoglossal motor nucleus, ventral respiratory column/pre-Bötzinger complex, and midline raphe nucleus from the brain stem, myocardium and coronary artery from the heart, and glomerulus, proximal convoluted tubule, and medullary thick ascending limb from the kidney. Each tissue region consists of or is enriched for a specific cell type. Differential patterns of miRNA expression obtained by deep sequencing of minute amounts of laser-captured cells were highly consistent with data obtained from real-time PCR analysis. miRNA expression patterns correctly clustered the specimens by tissue regions and then by primary tissue types (neural, muscular, or epithelial). The aggregate difference in miRNA profiles between tissue regions that contained the same primary tissue type was as large as one-half of the aggregate difference between primary tissue types. miRNAs differentially expressed between primary tissue types are more likely to be abundant miRNAs, while miRNAs differentially expressed between tissue regions containing the same primary tissue type were distributed evenly across the abundance spectrum. The tissue type-enriched miRNAs were more likely to target genes enriched for specific functional categories compared with either cell type-enriched miRNAs or randomly selected miRNAs. These data indicate that the role of miRNAs in determining characteristics of primary tissue types may be different than their role in regulating cell type-specific functions in solid organs.

Tissue-specific Regulation of Porcine Prolactin Receptor Expression by Estrogen, Progesterone and Prolactin

USDA-ARS?s Scientific Manuscript database

Prolactin (PRL) acts through its receptor (PRLR) via both endocrine and local paracrine/autocrine pathways to regulate biological processes including reproduction and lactation. We analyzed the tissue and stage of gestation-specific regulation of PRL and PRLR expression in various tissues of pigs. ...

Can plantar soft tissue mechanics enhance prognosis of diabetic foot ulcer?

PubMed

Naemi, R; Chatzistergos, P; Suresh, S; Sundar, L; Chockalingam, N; Ramachandran, A

2017-04-01

To investigate if the assessment of the mechanical properties of plantar soft tissue can increase the accuracy of predicting Diabetic Foot Ulceration (DFU). 40 patients with diabetic neuropathy and no DFU were recruited. Commonly assessed clinical parameters along with plantar soft tissue stiffness and thickness were measured at baseline using ultrasound elastography technique. 7 patients developed foot ulceration during a 12months follow-up. Logistic regression was used to identify parameters that contribute to predicting the DFU incidence. The effect of using parameters related to the mechanical behaviour of plantar soft tissue on the specificity, sensitivity, prediction strength and accuracy of the predicting models for DFU was assessed. Patients with higher plantar soft tissue thickness and lower stiffness at the 1st Metatarsal head area showed an increased risk of DFU. Adding plantar soft tissue stiffness and thickness to the model improved its specificity (by 3%), sensitivity (by 14%), prediction accuracy (by 5%) and prognosis strength (by 1%). The model containing all predictors was able to effectively (χ 2 (8, N=40)=17.55, P<0.05) distinguish between the patients with and without DFU incidence. The mechanical properties of plantar soft tissue can be used to improve the predictability of DFU in moderate/high risk patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Glucose predictability, blood capillary permeability, and glucose utilization rate in subcutaneous, skeletal muscle, and visceral fat tissues.

PubMed

Koutny, Tomas

2013-11-01

This study suggests an approach for the comparison and evaluation of particular compartments with modest experimental setup costs. A glucose level prediction model was used to evaluate the compartment's glucose transport rate across the blood capillary membrane and the glucose utilization rate by the cells. The glucose levels of the blood, subcutaneous tissue, skeletal muscle tissue, and visceral fat were obtained in experiments conducted on hereditary hypertriglyceridemic rats. After the blood glucose level had undergone a rapid change, the experimenter attempted to reach a steady blood glucose level by manually correcting the glucose infusion rate and maintaining a constant insulin infusion rate. The interstitial fluid glucose levels of subcutaneous tissue, skeletal muscle tissue, and visceral fat were evaluated to determine the reaction delay compared with the change in the blood glucose level, the interstitial fluid glucose level predictability, the blood capillary permeability, the effect of the concentration gradient, and the glucose utilization rate. Based on these data, the glucose transport rate across the capillary membrane and the utilization rate in a particular tissue were determined. The rates obtained were successfully verified against positron emission tomography experiments. The subcutaneous tissue exhibits the lowest and the most predictable glucose utilization rate, whereas the skeletal muscle tissue has the greatest glucose utilization rate. In contrast, the visceral fat is the least predictable and has the shortest reaction delay compared with the change in the blood glucose level. The reaction delays obtained for the subcutaneous tissue and skeletal muscle tissue were found to be approximately equal using a metric based on the time required to reach half of the increase in the interstitial fluid glucose level. © 2013 Published by Elsevier Ltd.

Tissue-Specific Transcriptome Profiling of Plutella Xylostella Third Instar Larval Midgut

PubMed Central

Xie, Wen; Lei, Yanyuan; Fu, Wei; Yang, Zhongxia; Zhu, Xun; Guo, Zhaojiang; Wu, Qingjun; Wang, Shaoli; Xu, Baoyun; Zhou, Xuguo; Zhang, Youjun

2012-01-01

The larval midgut of diamondback moth, Plutella xylostella, is a dynamic tissue that interfaces with a diverse array of physiological and toxicological processes, including nutrient digestion and allocation, xenobiotic detoxification, innate and adaptive immune response, and pathogen defense. Despite its enormous agricultural importance, the genomic resources for P. xylostella are surprisingly scarce. In this study, a Bt resistant P. xylostella strain was subjected to the in-depth transcriptome analysis to identify genes and gene networks putatively involved in various physiological and toxicological processes in the P. xylostella larval midgut. Using Illumina deep sequencing, we obtained roughly 40 million reads containing approximately 3.6 gigabases of sequence data. De novo assembly generated 63,312 ESTs with an average read length of 416bp, and approximately half of the P. xylostella sequences (45.4%, 28,768) showed similarity to the non-redundant database in GenBank with a cut-off E-value below 10-5. Among them, 11,092 unigenes were assigned to one or multiple GO terms and 16,732 unigenes were assigned to 226 specific pathways. In-depth analysis indentified genes putatively involved in insecticide resistance, nutrient digestion, and innate immune defense. Besides conventional detoxification enzymes and insecticide targets, novel genes, including 28 chymotrypsins and 53 ABC transporters, have been uncovered in the P. xylostella larval midgut transcriptome; which are potentially linked to the Bt toxicity and resistance. Furthermore, an unexpectedly high number of ESTs, including 46 serpins and 7 lysozymes, were predicted to be involved in the immune defense. As the first tissue-specific transcriptome analysis of P. xylostella, this study sheds light on the molecular understanding of insecticide resistance, especially Bt resistance in an agriculturally important insect pest, and lays the foundation for future functional genomics research. In addition, current

Tissue-specific transcriptome profiling of Plutella xylostella third instar larval midgut.

PubMed

Xie, Wen; Lei, Yanyuan; Fu, Wei; Yang, Zhongxia; Zhu, Xun; Guo, Zhaojiang; Wu, Qingjun; Wang, Shaoli; Xu, Baoyun; Zhou, Xuguo; Zhang, Youjun

2012-01-01

The larval midgut of diamondback moth, Plutella xylostella, is a dynamic tissue that interfaces with a diverse array of physiological and toxicological processes, including nutrient digestion and allocation, xenobiotic detoxification, innate and adaptive immune response, and pathogen defense. Despite its enormous agricultural importance, the genomic resources for P. xylostella are surprisingly scarce. In this study, a Bt resistant P. xylostella strain was subjected to the in-depth transcriptome analysis to identify genes and gene networks putatively involved in various physiological and toxicological processes in the P. xylostella larval midgut. Using Illumina deep sequencing, we obtained roughly 40 million reads containing approximately 3.6 gigabases of sequence data. De novo assembly generated 63,312 ESTs with an average read length of 416 bp, and approximately half of the P. xylostella sequences (45.4%, 28,768) showed similarity to the non-redundant database in GenBank with a cut-off E-value below 10(-5). Among them, 11,092 unigenes were assigned to one or multiple GO terms and 16,732 unigenes were assigned to 226 specific pathways. In-depth analysis identified genes putatively involved in insecticide resistance, nutrient digestion, and innate immune defense. Besides conventional detoxification enzymes and insecticide targets, novel genes, including 28 chymotrypsins and 53 ABC transporters, have been uncovered in the P. xylostella larval midgut transcriptome; which are potentially linked to the Bt toxicity and resistance. Furthermore, an unexpectedly high number of ESTs, including 46 serpins and 7 lysozymes, were predicted to be involved in the immune defense.As the first tissue-specific transcriptome analysis of P. xylostella, this study sheds light on the molecular understanding of insecticide resistance, especially Bt resistance in an agriculturally important insect pest, and lays the foundation for future functional genomics research. In addition, current

Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues

NASA Astrophysics Data System (ADS)

Möhler, Christian; Russ, Tom; Wohlfahrt, Patrick; Elter, Alina; Runz, Armin; Richter, Christian; Greilich, Steffen

2018-01-01

An experimental setup for consecutive measurement of ion and x-ray absorption in tissue or other materials is introduced. With this setup using a 3D-printed sample container, the reference stopping-power ratio (SPR) of materials can be measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine tissue samples were prepared for measurement, comprising five samples each of 13 tissue types representing about 80% of the total body mass (three different muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone). Using a standard stoichiometric calibration for single-energy CT (SECT) as well as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all tissues and then compared to the measured reference. With the SECT approach, the SPRs of all tissues were predicted with a mean error of (-0.84  ±  0.12)% and a mean absolute error of (1.27  ±  0.12)%. In contrast, the DECT-based SPR predictions were overall consistent with the measured reference with a mean error of (-0.02  ±  0.15)% and a mean absolute error of (0.10  ±  0.15)%. Thus, in this study, the potential of DECT to decrease range uncertainty could be confirmed in biological tissue.

Association of 5-hydroxymethylation and 5-methylation of DNA cytosine with tissue-specific gene expression

PubMed Central

Ponnaluri, V. K. Chaithanya; Ehrlich, Kenneth C.; Zhang, Guoqiang; Lacey, Michelle; Johnston, Douglas; Pradhan, Sriharsa; Ehrlich, Melanie

2017-01-01

ABSTRACT Differentially methylated or hydroxymethylated regions (DMRs) in mammalian DNA are often associated with tissue-specific gene expression but the functional relationships are still being unraveled. To elucidate these relationships, we studied 16 human genes containing myogenic DMRs by analyzing profiles of their epigenetics and transcription and quantitatively assaying 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) at specific sites in these genes in skeletal muscle (SkM), myoblasts, heart, brain, and diverse other samples. Although most human promoters have little or no methylation regardless of expression, more than half of the genes that we chose to study—owing to their myogenic DMRs—overlapped tissue-specific alternative or cryptic promoters displaying corresponding tissue-specific differences in histone modifications. The 5mC levels in myoblast DMRs were significantly associated with 5hmC levels in SkM at the same site. Hypermethylated myogenic DMRs within CDH15, a muscle- and cerebellum-specific cell adhesion gene, and PITX3, a homeobox gene, were used for transfection in reporter gene constructs. These intragenic DMRs had bidirectional tissue-specific promoter activity that was silenced by in vivo-like methylation. The CDH15 DMR, which was previously associated with an imprinted maternal germline DMR in mice, had especially strong promoter activity in myogenic host cells. These findings are consistent with the controversial hypothesis that intragenic DNA methylation can facilitate transcription and is not just a passive consequence of it. Our results support varied roles for tissue-specific 5mC- or 5hmC-enrichment in suppressing inappropriate gene expression from cryptic or alternative promoters and in increasing the plasticity of gene expression required for development and rapid responses to tissue stress or damage. PMID:27911668

Notch signalling coordinates tissue growth and wing fate specification in Drosophila.

PubMed

Rafel, Neus; Milán, Marco

2008-12-01

During the development of a given organ, tissue growth and fate specification are simultaneously controlled by the activity of a discrete number of signalling molecules. Here, we report that these two processes are extraordinarily coordinated in the Drosophila wing primordium, which extensively proliferates during larval development to give rise to the dorsal thoracic body wall and the adult wing. The developmental decision between wing and body wall is defined by the opposing activities of two secreted signalling molecules, Wingless and the EGF receptor ligand Vein. Notch signalling is involved in the determination of a variety of cell fates, including growth and cell survival. We present evidence that growth of the wing primordium mediated by the activity of Notch is required for wing fate specification. Our data indicate that tissue size modulates the activity range of the signalling molecules Wingless and Vein. These results highlight a crucial role of Notch in linking proliferation and fate specification in the developing wing primordium.

Combining Evidence of Preferential Gene-Tissue Relationships from Multiple Sources

PubMed Central

Guo, Jing; Hammar, Mårten; Öberg, Lisa; Padmanabhuni, Shanmukha S.; Bjäreland, Marcus; Dalevi, Daniel

2013-01-01

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity. PMID:23950964

Prediction of radiation-induced normal tissue complications in radiotherapy using functional image data

NASA Astrophysics Data System (ADS)

Nioutsikou, Elena; Partridge, Mike; Bedford, James L.; Webb, Steve

2005-03-01

The aim of this study has been to explicitly include the functional heterogeneity of an organ as a factor that contributes to the probability of complication of normal tissues following radiotherapy. Situations for which the inclusion of this information can be advantageous to the design of treatment plans are then investigated. A Java program has been implemented for this purpose. This makes use of a voxelated model of a patient, which is based on registered anatomical and functional data in order to enable functional voxel weighting. Using this model, the functional dose-volume histogram (fDVH) and the functional normal tissue complication probability (fNTCP) are then introduced as extensions to the conventional dose-volume histogram (DVH) and normal tissue complication probability (NTCP). In the presence of functional heterogeneity, these tools are physically more meaningful for plan evaluation than the traditional indices, as they incorporate additional information and are anticipated to show a better correlation with outcome. New parameters mf, nf and TD50f are required to replace the m, n and TD50 parameters. A range of plausible values was investigated, awaiting fitting of these new parameters to patient outcomes where functional data have been measured. As an example, the model is applied to two lung datasets utilizing accurately registered computed tomography (CT) and single photon emission computed tomography (SPECT) perfusion scans. Assuming a linear perfusion-function relationship, the biological index mean perfusion weighted lung dose (MPWLD) has been extracted from integration over outlined regions of interest. In agreement with the MPWLD ranking, the fNTCP predictions reveal that incorporation of functional imaging in radiotherapy treatment planning is most beneficial for organs with a large volume effect and large focal areas of dysfunction. There is, however, no additional advantage in cases presenting with homogeneous function. Although presented

Sensitivity, Specificity, PPV, and NPV for Predictive Biomarkers

PubMed Central

2015-01-01

Molecularly targeted cancer drugs are often developed with companion diagnostics that attempt to identify which patients will have better outcome on the new drug than the control regimen. Such predictive biomarkers are playing an increasingly important role in precision oncology. For diagnostic tests, sensitivity, specificity, positive predictive value, and negative predictive are usually used as performance measures. This paper discusses these indices for predictive biomarkers, provides methods for their calculation with survival or response endpoints, and describes assumptions involved in their use. PMID:26109105

Tissue-Specific and Cation/Anion-Specific DNA Methylation Variations Occurred in C. virgata in Response to Salinity Stress

PubMed Central

Gao, Xiang; Cao, Donghui; Liu, Jie; Wang, Xiaoping; Geng, Shujuan; Liu, Bao; Shi, Decheng

2013-01-01

Salinity is a widespread environmental problem limiting productivity and growth of plants. Halophytes which can adapt and resist certain salt stress have various mechanisms to defend the higher salinity and alkalinity, and epigenetic mechanisms especially DNA methylation may play important roles in plant adaptability and plasticity. In this study, we aimed to investigate the different influences of various single salts (NaCl, Na2SO4, NaHCO3, Na2CO3) and their mixed salts on halophyte Chloris. virgata from the DNA methylation prospective, and discover the underlying relationships between specific DNA methylation variations and specific cations/anions through the methylation-sensitive amplification polymorphism analysis. The results showed that the effects on DNA methylation variations of single salts were ranked as follows: Na2CO3> NaHCO3> Na2SO4> NaCl, and their mixed salts exerted tissue-specific effects on C. virgata seedlings. Eight types of DNA methylation variations were detected and defined in C. virgata according to the specific cations/anions existed in stressful solutions; in addition, mix-specific and higher pH-specific bands were the main type in leaves and roots independently. These findings suggested that mixed salts were not the simple combination of single salts. Furthermore, not only single salts but also mixed salts showed tissue-specific and cations/anions-specific DNA methylation variations. PMID:24223802

Tissue-specific and cation/anion-specific DNA methylation variations occurred in C. virgata in response to salinity stress.

PubMed

Gao, Xiang; Cao, Donghui; Liu, Jie; Wang, Xiaoping; Geng, Shujuan; Liu, Bao; Shi, Decheng

2013-01-01

Salinity is a widespread environmental problem limiting productivity and growth of plants. Halophytes which can adapt and resist certain salt stress have various mechanisms to defend the higher salinity and alkalinity, and epigenetic mechanisms especially DNA methylation may play important roles in plant adaptability and plasticity. In this study, we aimed to investigate the different influences of various single salts (NaCl, Na2SO4, NaHCO3, Na2CO3) and their mixed salts on halophyte Chloris. virgata from the DNA methylation prospective, and discover the underlying relationships between specific DNA methylation variations and specific cations/anions through the methylation-sensitive amplification polymorphism analysis. The results showed that the effects on DNA methylation variations of single salts were ranked as follows: Na2CO3> NaHCO3> Na2SO4> NaCl, and their mixed salts exerted tissue-specific effects on C. virgata seedlings. Eight types of DNA methylation variations were detected and defined in C. virgata according to the specific cations/anions existed in stressful solutions; in addition, mix-specific and higher pH-specific bands were the main type in leaves and roots independently. These findings suggested that mixed salts were not the simple combination of single salts. Furthermore, not only single salts but also mixed salts showed tissue-specific and cations/anions-specific DNA methylation variations.

Computational Models Predict Larger Muscle Tissue Strains at Faster Sprinting Speeds

PubMed Central

Fiorentino, Niccolo M; Rehorn, Michael R; Chumanov, Elizabeth S; Thelen, Darryl G; Blemker, Silvia S

2014-01-01

Introduction: Proximal biceps femoris musculotendon strain injury has been well established as a common injury among athletes participating in sports that require sprinting near or at maximum speed; however, little is known about the mechanisms that make this muscle tissue more susceptible to injury at faster speeds. Purpose: Quantify localized tissue strain during sprinting at a range of speeds. Methods: Biceps femoris long head (BFlh) musculotendon dimensions of 14 athletes were measured on magnetic resonance (MR) images and used to generate a finite element computational model. The model was first validated through comparison with previous dynamic MR experiments. After validation, muscle activation and muscle-tendon unit length change were derived from forward dynamic simulations of sprinting at 70%, 85% and 100% maximum speed and used as input to the computational model simulations. Simulations ran from mid-swing to foot contact. Results: The model predictions of local muscle tissue strain magnitude compared favorably with in vivo tissue strain measurements determined from dynamic MR experiments of the BFlh. For simulations of sprinting, local fiber strain was non-uniform at all speeds, with the highest muscle tissue strain where injury is often observed (proximal myotendinous junction). At faster sprinting speeds, increases were observed in fiber strain non-uniformity and peak local fiber strain (0.56, 0.67 and 0.72, for sprinting at 70%, 85% and 100% maximum speed). A histogram of local fiber strains showed that more of the BFlh reached larger local fiber strains at faster speeds. Conclusions: At faster sprinting speeds, peak local fiber strain, fiber strain non-uniformity and the amount of muscle undergoing larger strains are predicted to increase, likely contributing to the BFlh muscle’s higher injury susceptibility at faster speeds. PMID:24145724

A General Map of Iron Metabolism and Tissue-specific Subnetworks

PubMed Central

Hower, Valerie; Mendes, Pedro; Torti, Frank M.; Laubenbacher, Reinhard; Akman, Steven; Shulaev, Vladmir; Torti, Suzy V.

2009-01-01

Iron is required for survival of mammalian cells. Recently, understanding of iron metabolism and trafficking has increased dramatically, revealing a complex, interacting network largely unknown just a few years ago. This provides an excellent model for systems biology development and analysis. The first step in such an analysis is the construction of a structural network of iron metabolism, which we present here. This network was created using CellDesigner version 3.5.2 and includes reactions occurring in mammalian cells of numerous tissue types. The iron metabolic network contains 151 chemical species and 107 reactions and transport steps. Starting from this general model, we construct iron networks for specific tissues and cells that are fundamental to maintaining body iron homeostasis. We include subnetworks for cells of the intestine and liver, tissues important in iron uptake and storage, respectively; as well as the reticulocyte and macrophage, key cells in iron utilization and recycling. The addition of kinetic information to our structural network will permit the simulation of iron metabolism in different tissues as well as in health and disease. PMID:19381358

Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

PubMed

Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

2017-04-04

Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

Microvascular remodelling in preeclampsia: quantifying capillary rarefaction accurately and independently predicts preeclampsia.

PubMed

Antonios, Tarek F T; Nama, Vivek; Wang, Duolao; Manyonda, Isaac T

2013-09-01

Preeclampsia is a major cause of maternal and neonatal mortality and morbidity. The incidence of preeclampsia seems to be rising because of increased prevalence of predisposing disorders, such as essential hypertension, diabetes, and obesity, and there is increasing evidence to suggest widespread microcirculatory abnormalities before the onset of preeclampsia. We hypothesized that quantifying capillary rarefaction could be helpful in the clinical prediction of preeclampsia. We measured skin capillary density according to a well-validated protocol at 5 consecutive predetermined visits in 322 consecutive white women, of whom 16 subjects developed preeclampsia. We found that structural capillary rarefaction at 20-24 weeks of gestation yielded a sensitivity of 0.87 with a specificity of 0.50 at the cutoff of 2 capillaries/field with the area under the curve of the receiver operating characteristic value of 0.70, whereas capillary rarefaction at 27-32 weeks of gestation yielded a sensitivity of 0.75 and a higher specificity of 0.77 at the cutoff of 8 capillaries/field with area under the curve of the receiver operating characteristic value of 0.82. Combining capillary rarefaction with uterine artery Doppler pulsatility index increased the sensitivity and specificity of the prediction. Multivariable analysis shows that the odds of preeclampsia are increased in women with previous history of preeclampsia or chronic hypertension and in those with increased uterine artery Doppler pulsatility index, but the most powerful and independent predictor of preeclampsia was capillary rarefaction at 27-32 weeks. Quantifying structural rarefaction of skin capillaries in pregnancy is a potentially useful clinical marker for the prediction of preeclampsia.

Indication for Computed Tomography Scan in Shoulder Instability: Sensitivity and Specificity of Standard Radiographs to Predict Bone Defects After Traumatic Anterior Glenohumeral Instability.

PubMed

Delage Royle, Audrey; Balg, Frédéric; Bouliane, Martin J; Canet-Silvestri, Fanny; Garant-Saine, Laurianne; Sheps, David M; Lapner, Peter; Rouleau, Dominique M

2017-10-01

Quantifying glenohumeral bone loss is key in preoperative surgical planning for a successful Bankart repair. Simple radiographs can accurately measure bone defects in cases of recurrent shoulder instability. Cohort study (diagnosis); Level of evidence, 2. A true anteroposterior (AP) view, alone and in combination with an axillary view, was used to evaluate the diagnostic properties of radiographs compared with computed tomography (CT) scan, the current gold standard, to predict significant bone defects in 70 patients. Sensitivity, specificity, and positive and negative predictive values were evaluated and compared. Detection of glenoid bone loss on plain film radiographs, with and without axillary view, had a sensitivity of 86% for both views and a specificity of 73% and 64% with and without the axillary view, respectively. For detection of humeral bone loss, the sensitivity was 8% and 17% and the specificity was 98% and 91% with and without the axillary view, respectively. Regular radiographs would have missed 1 instance of significant bone loss on the glenoid side and 20 on the humeral side. Interobserver reliabilities were moderate for glenoid detection (κ = 0.473-0.503) and poor for the humeral side (κ = 0.278-0.336). Regular radiographs showed suboptimal sensitivity, specificity, and reliability. Therefore, CT scan should be considered in the treatment algorithm for accurate quantification of bone loss to prevent high rates of recurrent instability.

Category-Specific Neural Oscillations Predict Recall Organization During Memory Search

PubMed Central

Morton, Neal W.; Kahana, Michael J.; Rosenberg, Emily A.; Baltuch, Gordon H.; Litt, Brian; Sharan, Ashwini D.; Sperling, Michael R.; Polyn, Sean M.

2013-01-01

Retrieved-context models of human memory propose that as material is studied, retrieval cues are constructed that allow one to target particular aspects of past experience. We examined the neural predictions of these models by using electrocorticographic/depth recordings and scalp electroencephalography (EEG) to characterize category-specific oscillatory activity, while participants studied and recalled items from distinct, neurally discriminable categories. During study, these category-specific patterns predict whether a studied item will be recalled. In the scalp EEG experiment, category-specific activity during study also predicts whether a given item will be recalled adjacent to other same-category items, consistent with the proposal that a category-specific retrieval cue is used to guide memory search. Retrieved-context models suggest that integrative neural circuitry is involved in the construction and maintenance of the retrieval cue. Consistent with this hypothesis, we observe category-specific patterns that rise in strength as multiple same-category items are studied sequentially, and find that individual differences in this category-specific neural integration during study predict the degree to which a participant will use category information to organize memory search. Finally, we track the deployment of this retrieval cue during memory search: Category-specific patterns are stronger when participants organize their responses according to the category of the studied material. PMID:22875859

Shedding light on the variability of optical skin properties: finding a path towards more accurate prediction of light propagation in human cutaneous compartments

PubMed Central

Mignon, C.; Tobin, D. J.; Zeitouny, M.; Uzunbajakava, N. E.

2018-01-01

Finding a path towards a more accurate prediction of light propagation in human skin remains an aspiration of biomedical scientists working on cutaneous applications both for diagnostic and therapeutic reasons. The objective of this study was to investigate variability of the optical properties of human skin compartments reported in literature, to explore the underlying rational of this variability and to propose a dataset of values, to better represent an in vivo case and recommend a solution towards a more accurate prediction of light propagation through cutaneous compartments. To achieve this, we undertook a novel, logical yet simple approach. We first reviewed scientific articles published between 1981 and 2013 that reported on skin optical properties, to reveal the spread in the reported quantitative values. We found variations of up to 100-fold. Then we extracted the most trust-worthy datasets guided by a rule that the spectral properties should reflect the specific biochemical composition of each of the skin layers. This resulted in the narrowing of the spread in the calculated photon densities to 6-fold. We conclude with a recommendation to use the identified most robust datasets when estimating light propagation in human skin using Monte Carlo simulations. Alternatively, otherwise follow our proposed strategy to screen any new datasets to determine their biological relevance. PMID:29552418

A composite score combining waist circumference and body mass index more accurately predicts body fat percentage in 6- to 13-year-old children.

PubMed

Aeberli, I; Gut-Knabenhans, M; Kusche-Ammann, R S; Molinari, L; Zimmermann, M B

2013-02-01

Body mass index (BMI) and waist circumference (WC) are widely used to predict % body fat (BF) and classify degrees of pediatric adiposity. However, both measures have limitations. The aim of this study was to evaluate whether a combination of WC and BMI would more accurately predict %BF than either alone. In a nationally representative sample of 2,303 6- to 13-year-old Swiss children, weight, height, and WC were measured, and %BF was determined from multiple skinfold thicknesses. Regression and receiver operating characteristic (ROC) curves were used to evaluate the combination of WC and BMI in predicting %BF against WC or BMI alone. An optimized composite score (CS) was generated. A quadratic polynomial combination of WC and BMI led to a better prediction of %BF (r (2) = 0.68) compared with the two measures alone (r (2) = 0.58-0.62). The areas under the ROC curve for the CS [0.6 * WC-SDS + 0.4 * BMI-SDS] ranged from 0.962 ± 0.0053 (overweight girls) to 0.982 ± 0.0046 (obese boys) and were somewhat greater than the AUCs for either BMI or WC alone. At a given specificity, the sensitivity of the prediction of overweight and obesity based on the CS was higher than that based on either WC or BMI alone, although the improvement was small. Both BMI and WC are good predictors of %BF in primary school children. However, a composite score incorporating both measures increased sensitivity at a constant specificity as compared to the individual measures. It may therefore be a useful tool for clinical and epidemiological studies of pediatric adiposity.

Molecular Signatures of Tissue-Specific Microvascular Endothelial Cell Heterogeneity in Organ Maintenance and Regeneration

PubMed Central

Nolan, Daniel J.; Ginsberg, Michael; Israely, Edo; Palikuqi, Brisa; Poulos, Michael G.; James, Daylon; Ding, Bi-Sen; Schachterle, William; Liu, Ying; Rosenwaks, Zev; Butler, Jason M.; Xiang, Jenny; Rafii, Arash; Shido, Koji; Rabbany, Sina Y.; Elemento, Olivier; Rafii, Shahin

2013-01-01

SUMMARY Microvascular endothelial cells (ECs) within different tissues are endowed with distinct but as yet unrecognized structural, phenotypic, and functional attributes. We devised EC purification, cultivation, profiling, and transplantation models that establish tissue-specific molecular libraries of ECs devoid of lymphatic ECs or parenchymal cells. These libraries identify attributes that confer ECs with their organotypic features. We show that clusters of transcription factors, angiocrine growth factors, adhesion molecules, and chemokines are expressed in unique combinations by ECs of each organ. Furthermore, ECs respond distinctly in tissue regeneration models, hepatectomy, and myeloablation. To test the data set, we developed a transplantation model that employs generic ECs differentiated from embryonic stem cells. Transplanted generic ECs engraft into regenerating tissues and acquire features of organotypic ECs. Collectively, we demonstrate the utility of informational databases of ECs toward uncovering the extravascular and intrinsic signals that define EC heterogeneity. These factors could be exploited therapeutically to engineer tissue-specific ECs for regeneration. PMID:23871589

Tissue polarimetry: concepts, challenges, applications, and outlook.

PubMed

Ghosh, Nirmalya; Vitkin, I Alex

2011-11-01

Polarimetry has a long and successful history in various forms of clear media. Driven by their biomedical potential, the use of the polarimetric approaches for biological tissue assessment has also recently received considerable attention. Specifically, polarization can be used as an effective tool to discriminate against multiply scattered light (acting as a gating mechanism) in order to enhance contrast and to improve tissue imaging resolution. Moreover, the intrinsic tissue polarimetry characteristics contain a wealth of morphological and functional information of potential biomedical importance. However, in a complex random medium-like tissue, numerous complexities due to multiple scattering and simultaneous occurrences of many scattering and polarization events present formidable challenges both in terms of accurate measurements and in terms of analysis of the tissue polarimetry signal. In order to realize the potential of the polarimetric approaches for tissue imaging and characterization/diagnosis, a number of researchers are thus pursuing innovative solutions to these challenges. In this review paper, we summarize these and other issues pertinent to the polarized light methodologies in tissues. Specifically, we discuss polarized light basics, Stokes-Muller formalism, methods of polarization measurements, polarized light modeling in turbid media, applications to tissue imaging, inverse analysis for polarimetric results quantification, applications to quantitative tissue assessment, etc.

Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

ERIC Educational Resources Information Center

Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

2012-01-01

One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

On Acoustic Source Specification for Rotor-Stator Interaction Noise Prediction

NASA Technical Reports Server (NTRS)

Nark, Douglas M.; Envia, Edmane; Burley, Caesy L.

2010-01-01

This paper describes the use of measured source data to assess the effects of acoustic source specification on rotor-stator interaction noise predictions. Specifically, the acoustic propagation and radiation portions of a recently developed coupled computational approach are used to predict tonal rotor-stator interaction noise from a benchmark configuration. In addition to the use of full measured data, randomization of source mode relative phases is also considered for specification of the acoustic source within the computational approach. Comparisons with sideline noise measurements are performed to investigate the effects of various source descriptions on both inlet and exhaust predictions. The inclusion of additional modal source content is shown to have a much greater influence on the inlet results. Reasonable agreement between predicted and measured levels is achieved for the inlet, as well as the exhaust when shear layer effects are taken into account. For the number of trials considered, phase randomized predictions follow statistical distributions similar to those found in previous statistical source investigations. The shape of the predicted directivity pattern relative to measurements also improved with phase randomization, having predicted levels generally within one standard deviation of the measured levels.

Prediction of Surface and pH-Specific Binding of Peptides to Metal and Oxide Nanoparticles

NASA Astrophysics Data System (ADS)

Heinz, Hendrik; Lin, Tzu-Jen; Emami, Fateme Sadat; Ramezani-Dakhel, Hadi; Naik, Rajesh; Knecht, Marc; Perry, Carole C.; Huang, Yu

2015-03-01

The mechanism of specific peptide adsorption onto metallic and oxidic nanostructures has been elucidated in atomic resolution using novel force fields and surface models in comparison to measurements. As an example, variations in peptide adsorption on Pd and Pt nanoparticles depending on shape, size, and location of peptides on specific bounding facets are explained. Accurate computational predictions of reaction rates in C-C coupling reactions using particle models derived from HE-XRD and PDF data illustrate the utility of computational methods for the rational design of new catalysts. On oxidic nanoparticles such as silica and apatites, it is revealed how changes in pH lead to similarity scores of attracted peptides lower than 20%, supported by appropriate model surfaces and data from adsorption isotherms. The results demonstrate how new computational methods can support the design of nanoparticle carriers for drug release and the understanding of calcification mechanisms in the human body.

New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

PubMed

Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

2013-10-07

An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

PubMed

Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

2018-04-01

Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the