Consensus Features Research Articles

XAuth: Efficient Privacy-Preserving Cross-Domain Authentication

It is well known that each Public Key Infrastructure (PKI) system forms a closed security domain and only recognizes certificates in its own domain (such as medical systems, financial systems, and 5G networks). When users need to access services in other domains, their identities often cannot be recognized or PKI systems require extremely complex operations to authenticate the users’ identities. This is the cross-domain authentication problem. The distributed consensus feature of blockchain provides a technical approach to solve this problem. However, there are some unresolved problems in existing blockchain-based schemes. On one hand, due to the low throughput of blockchain systems, the response speed may be insufferable when the number of cross-domain authentication requirements becomes enormous. On the other hand, these schemes insufficiently consider the privacy risk in the cross-domain scenario. In this article, we propose an efficient privacy-preserving cross-domain authentication scheme called XAuth that is integrated naturally with the existing PKI and Certificate Transparency (CT) systems. Specifically, we design a lightweight correctness verification protocol based on Multiple Merkle Hash Tree for rapid response. To protect users’ privacy, we present an anonymous authentication protocol for cross-domain authentication. The security analysis and experimental results demonstrate that XAuth is secure and efficient.

Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis.

(1) Background: MALDI-TOF mass spectrometry (MS) is the gold standard for microbial fingerprinting, however, for phylogenetically closely related species, the resolution power drops down to the genus level. In this study, we analyzed MALDI-TOF spectra from 44 strains of B. melitensis, B. suis and B. abortus to identify the optimal classification method within popular supervised and unsupervised machine learning (ML) algorithms. (2) Methods: A consensus feature selection strategy was applied to pinpoint from among the 500 MS features those that yielded the best ML model and that may play a role in species differentiation. Unsupervised k-means and hierarchical agglomerative clustering were evaluated using the silhouette coefficient, while the supervised classifiers Random Forest, Support Vector Machine, Neural Network, and Multinomial Logistic Regression were explored in a fine-tuning manner using nested k-fold cross validation (CV) with a feature reduction step between the two CV loops. (3) Results: Sixteen differentially expressed peaks were identified and used to feed ML classifiers. Unsupervised and optimized supervised models displayed excellent predictive performances with 100% accuracy. The suitability of the consensus feature selection strategy for learning system accuracy was shown. (4) Conclusion: A meaningful ML approach is here introduced, to enhance Brucella spp. classification using MALDI-TOF MS data.

Deep-GHBP: Improving prediction of Growth Hormone-binding proteins using deep learning model

Growth hormone-binding proteins (GHBPs) are carrier proteins that interact with other growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant roles in various biological activities including cell growth, granular cellular mechanism, and therapeutic approaches. Considering these crucial functions, an accurate prediction of GHBPs is indispensable. In this connection, wet-laboratory and machine learning (ML) models have been developed. However, these methods have a limited amount of performance, including less informative features or inefficient learning models. This study presents an innovative approach by employing a Position Specific Scoring Matrix (PSSM), Composition Transition Distribution (CTD), Geary, Moran, and Pseudo Amino Acid Composition (PseAAC). The important features of these were selected by a novel Multi-Model Consensus (MMC) feature selection algorithm. The most appropriate feature set was then provided to four classifiers: Deep Neural Network (DNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The DNN on the optimal feature set achieved the highest accuracies of 88.09% and 83.33% on the training and testing datasets, respectively. The achieved results were also superior to existing predictors for the identification of GHBPs. Thus, GHBP-Pred will be significantly helpful for large scale prediction of GHBPs with high precision.

Infrared cavity ring-down spectroscopy for detecting non-small cell lung cancer in exhaled breath

Early diagnosis of lung cancer greatly improves the likelihood of survival and remission, but limitations in existing technologies like low-dose computed tomography have prevented the implementation of widespread screening programs. Breath-based solutions that seek disease biomarkers in exhaled volatile organic compound (VOC) profiles show promise as affordable, accessible and non-invasive alternatives to traditional imaging. In this pilot work, we present a lung cancer detection framework using cavity ring-down spectroscopy (CRDS), an effective and practical laser absorption spectroscopy technique that has the ability to advance breath screening into clinical reality. The main aims of this work were to (1) test the utility of infrared CRDS breath profiles for discriminating non-small cell lung cancer (NSCLC) patients from controls, (2) compare models with VOCs as predictors to those with patterns from the CRDS spectra (breathprints) as predictors, and (3) present a robust approach for identifying relevant disease biomarkers. First, based on a proposed learning curve technique that estimated the limits of a model’s performance at multiple sample sizes (10–158), the CRDS-based models developed in this work were found to achieve classification performance comparable or superior to like mass spectroscopy and sensor-based systems. Second, using 158 collected samples (62 NSCLC subjects and 96 controls), the accuracy range for the VOC-based model was 65.19%–85.44% (51.61%–66.13% sensitivity and 73.96%–97.92% specificity), depending on the employed cross-validation technique. The model based on breathprint predictors generally performed better, with accuracy ranging from 71.52%–86.08% (58.06%–82.26% sensitivity and 80.21%–88.54% specificity). Lastly, using a protocol based on consensus feature selection, three VOCs (isopropanol, dimethyl sulfide, and butyric acid) and two breathprint features (from a local binary pattern transformation of the spectra) were identified as possible NSCLC biomarkers. This research demonstrates the potential of infrared CRDS breath profiles and the developed early-stage classification techniques for lung cancer biomarker detection and screening.

Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.

To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

Consensus Feature Network for Scene Parsing

Scene parsing is challenging as it aims to assign one of the semantic categories to each pixel in scene images. Thus, pixel-level features are desired for scene parsing. However, classification networks are dominated by the discriminative portion, so directly applying classification networks to scene parsing will result in inconsistent parsing predictions within one instance and among instances of the same category. To address this problem, we propose two transform units to learn pixel-level consensus features. One is an Instance Consensus Transform (ICT) unit to learn the instance-level consensus features by aggregating features within the same instance. The other is a Category Consensus Transform (CCT) unit to pursue category-level consensus features through keeping the consensus of features among instances of the same category in scene images. The proposed ICT and CCT units are lightweight, data-driven and end-to-end trainable. The features learned by the two units are more coherent in both instance-level and category-level. Furthermore, we present the Consensus Feature Network (CFNet) based on the proposed ICT and CCT units, and demonstrate the effectiveness of each component in our method by performing extensive ablation experiments. Finally, our proposed CFNet achieves competitive performance on four datasets, including Cityscapes, Pascal Context, CamVid, and COCO Stuff.

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

PurposeTo evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT.Materials and methodsOne hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled.CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann–Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves.ResultsUnfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001).ConclusionsPreliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.

Brain network excitatory/inhibitory imbalance is a biomarker for drug‐naive Rolandic epilepsy: A radiomics strategy

Seizure occurs when the balance between excitatory and inhibitory (E/I) inputs to neurons is perturbed, resulting in abnormal electrical activity. This study investigated whether an existing E/I imbalance in neural networks is a useful diagnostic biomarker for Rolandic epilepsy by a resting-state dynamic causal modeling-based support vector machine (rs-DCM-SVM) algorithm. This multicenter study enrolled a discovery cohort (76 children with Rolandic epilepsy and 76 normal controls [NCs]) and a replication cohort (59 children with Rolandic epilepsy and 60NCs). Spatial independent component analysis was used to seven canonical neural networks, and a total of 25 regions of interest were selected from these networks. The rs-DCM-SVM classifier was used for individual classification, consensus feature selection, and feature ranking. The rs-DCM-SVM classifier showed that the E/I imbalance in brain networks is a useful neuroimaging biomarker for Rolandic epilepsy, with an accuracy of 88.2% and 81.5% and an area under curve of .92 and .83 in the discovery and the replication cohorts, respectively. Consensus brain regions with the highest contributions to the classification were located within the epilepsy-related networks, indicating that this classifier was suitable. Consensus functional connection pairs with the highest contributions to the classification were associated with an excitation network loop and an inhibition network loop. The excitation loop mediated the integration of advanced cognitive networks (subcortex, dorsal attention, default mode, executive control, and salience networks), whereas the inhibition loop was involved in the segregation of sensorimotor and language networks. The two loops showed functional segregation. Brain E/I imbalance has potential to serve as a biomarker for individual classification in children with Rolandic epilepsy, and might be an important mechanism for causing seizures and cognitive impairment in children with Rolandic epilepsy.

A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach.

BackgroundPulmonary arterial hypertension (PAH) is a rare but life shortening disease, the diagnosis of which is often delayed, and requires an invasive right heart catheterisation. Identifying diagnostic biomarkers may improve screening to identify patients at risk of PAH earlier and provide new insights into disease pathogenesis. MicroRNAs are small, non-coding molecules of RNA, previously shown to be dysregulated in PAH, and contribute to the disease process in animal models.MethodsPlasma from 64 treatment naïve patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. Following quality control and normalisation, the cohort was split into training and validation sets. Four separate machine learning feature selection methods were applied to the training set, along with a univariate analysis.Findings20 microRNAs were identified as putative biomarkers by consensus feature selection from all four methods. Two microRNAs (miR-636 and miR-187-5p) were selected by all methods and used to predict PAH diagnosis with high accuracy. Integrating microRNA expression profiles with their associated target mRNA revealed 61 differentially expressed genes verified in two independent, publicly available PAH lung tissue data sets. Two of seven potentially novel gene targets were validated as differentially expressed in vitro in human pulmonary artery smooth muscle cells.InterpretationThis consensus of multiple machine learning approaches identified two miRNAs that were able to distinguish PAH from both disease and healthy controls. These circulating miRNA, and their target genes may provide insight into PAH pathogenesis and reveal novel regulators of disease and putative drug targets.

Suppressed Macrophage Responses in Metastatic Merkel Cell Carcinoma: Mining of Core Expression Features by Meta-Analytic Machine Learning (MAML)

Background: Merkel cell carcinoma (MCC) is an exceedingly lethal cutaneous neoplasm with highly controversial origin and a stiffly rising incidence. Immunotherapies, including checkpoint blockade (CPB), have shown some benefit in advanced-stage or chemotherapy-resistant MCC, but the performance has been at best erratic. The aim of the study was to establish the transcriptomic profiling of advanced MCC, as well as tumor infiltrating immune cell profiles using sophisticated the meta-analytic machine learning (MAML) method. Methods: Using an ML algorithm the authors trained our model to select consensus features that may be used to differentiate metastatic MCC from primary lesions. We then proceeded to validate and fine-tune the model’s performance with an assortment of probabilistic tools. Findings: We successfully extracted 98 core gene features for metastatic lesions with a superb accuracy, as shown by the AUROC of 0·905 (95% CI, 0·848-0.962) and AUPRC of 0·919 on leave-one-out cross validation. More significantly, our algorithm also identified core pathways with non-zero pathway dysregulation coefficients. The performance metrics for pathway analysis proved to be comparable to those for genes, with the AUROC of 0·888 (95% CI, 0·825, 0·950) and AUPRC of 0·842. Intriguingly, a significant reduction of monocyte-macrophage subpopulation was observed on cell enrichment-based immune profiling, a finding we substantiated through a deconvolution-based method. Interpretation: We have built a predictive model to find a reliable set of gene features in metastatic MCC, and successfully demonstrated both up- and down-regulated genes and pathways with non-zero coefficients, which provide a framework for new prospective targets in checkpoint blockade therapy. The excellent performance of the model points to the value and potential of MAML, an eclectic mixture of MA and predictive ML modelling. Funding Statement: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1062023). Declaration of Interests: The authors declare no conflicts of interest regarding the contents of this article.

Effects of pH on an IDP conformational ensemble explored by molecular dynamics simulation

The conformational ensemble of intrinsically disordered proteins, such as α-synuclein, are responsible for their function and malfunction. Misfolding of α-synuclein can lead to neurodegenerative diseases, and the ability to study their conformations and those of other intrinsically disordered proteins under varying physiological conditions can be crucial to understanding and preventing pathologies. In contrast to well-folded peptides, a consensus feature of IDPs is their low hydropathy and high charge, which makes their conformations sensitive to pH perturbation. We examine a prominent member of this subset of IDPs, α-synuclein, using a divide-and-conquer scheme that provides enhanced sampling of IDP structural ensembles. We constructed conformational ensembles of α-synuclein under neutral (pH ~ 7) and low (pH ~ 3) pH conditions and compared our results with available information obtained from smFRET, SAXS, and NMR studies. Specifically, α-synuclein has been found to in a more compact state at low pH conditions and the structural changes observed are consistent with those from experiments. We also characterize the conformational and dynamic differences between these ensembles and discussed the implication on promoting pathogenic fibril formation. We find that under low pH conditions, neutralization of negatively charged residues leads to compaction of the C-terminal portion of α-synuclein while internal reorganization allows α-synuclein to maintain its overall end-to-end distance. We also observe different levels of intra-protein interaction between three regions of α-synuclein at varying pH and a shift towards more hydrophilic interactions with decreasing pH.

Consensus and majority vote feature selection methods and a detection technique for web phishing

Phishing is one of the most frequently occurring forms of cybercrime that Internet users face and represents a violation of cybersecurity principles. Phishing is a fraudulent attack that is performed over the Internet with the purpose of obtaining and using without authorization the sensitive information of Internet users, such as usernames, passwords, credit card details, and bank account information. Some widely used phishing attempts involve using email spoofing or instant messaging, aiming to convince a victim to visit the spoofed websites, which will result in obtaining the victim’s information. In this work, we identify and analyze the most important features needed to detect the spoofed websites in virtue of two new feature selection techniques. The first proposed feature selection technique uses underlying feature selection methods that vote on each feature, and if such methods agree on a specific feature, that feature is selected. The second feature selection technique also uses underlying feature selection methods that vote on each feature, and if the majority vote on a specific feature, the feature is selected. We also propose a phishing detection technique based on both AdaBoost and LightGBM ensemble methods to detect the spoofed websites. The proposed method achieves a very high accuracy compared to that of the existing methods.

A working human abnormal operation recognition approach based on the deep multi-instance sorting model

Modeling abnormal behavior detection in the intelligent video monitoring system for recognition or detection of special event has attracted significant research interest in recent years. In order to achieve more effective recognition of abnormal behavior detection of the intelligent video surveillance system, this paper proposes a working human abnormal operation recognition approach based on deep multi-instance sorting model. Firstly, the uncut long video is sparse to obtain normal and abnormal behavior video segments, and the RGB and optical flow features in the segment are extracted by the deep convolution network. Video feature vectors are obtained by the consensus function and feature extractor. Then, multi-instance sorting learning is used to assign abnormality scores between 0 and 1 to feature vectors. When the abnormal values of abnormal packets are higher than those of normal packets, the abnormality score is returned and the high abnormality score is determined as an abnormal behavior. The experiments on the open THUMOS14 data set and our own XAGCWD data set using CUDA GPU accelerated computing to demonstrate that our approach improves the recognition accuracy about 10.8% and high accuracy of abnormal detection. The main purpose of this research is to apply the model proposed in this paper to the intelligent workshop behavior monitoring system to effectively realize the safety management of workshop personnel.

Consensus features nested cross-validation.

Feature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets.We develop consensus nested cross-validation (cnCV) that combines the idea of feature stability from differential privacy with nCV. Feature selection is applied in each inner fold and the consensus of top features across folds is used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative cooling (pEC). We also compare these methods using real RNA-seq data from a study of major depressive disorder.The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification. Code available at https://github.com/insilico/cncv. Supplementary data are available at Bioinformatics online.

Abstract B20: KrasG12D effector dependencies in the maintenance of pancreatic ductal adenocarcinoma

Abstract More than 90% of pancreatic ductal adenocarcinoma (PDAC) tumors in humans feature activating mutations in the proto-oncogene KRAS. In mice, KrasG12D mutation alone is sufficient for PanIN formation; when coupled with mutational inactivation of TP53 (Tp53R172H), PanIN formation is accelerated and these neoplastic lesions progress to invasive and metastatic disease at high frequency. Several groups have demonstrated that inactivation of mutant Kras in established PanIN and PDAC leads to redifferentiation and apoptosis of the tumor, respectively. More recently, several combinatorial genetic and pharmacologic strategies have been employed that highlight RAF kinases as key oncoeffectors for KRAS addiction, but that RAF inhibition alone demonstrates little efficiency against KRAS mutant tumors. Additionally, previous studies suggest a requirement for both C-raf and EGFR blockade or the combination of RAF-MEK or RAF-PI3K inhibitors to inhibit to tumor growth. However, a systematic evaluation of dependencies of Kras effectors in tumor maintenance has not been explicitly performed. To investigate effector dependencies in the KrasG12D setting, we have developed an organoid platform to generate isogenic lines bearing doxycycline-inducible small hairpin (sh)RNAs coupled to GFP inserted into mouse pancreatic tumor organoids derived from KPC mice. These shRNAs are directed against each of the major Kras effector molecules, including B-raf, C-raf, Pi3k, RalA, RalB, and Rac1, with Kras shRNAs and Renilla shRNA used as controls. We identified 2 effective shRNAs per target, with strong and durable inhibition of each protein observed after doxycycline treatment. To investigate the hierarchy of Kras effectors, we employed a competition assay of the shRNA organoids, and observed the drop-out of Kras, C-raf, and Rac1 shRNA-bearing tumor organoid lines. Cell number and volumetric analyses confirmed strong dependencies on Kras, C-raf, and Rac1. Chromatin accessibility and transcriptomic analyses of Kras- , C-raf- , and Rac1-depleted organoids revealed consensus features of abrogated tumor maintenance and key molecular nodes in tumor maintenance. Together, we have employed a mouse organoid platform to create a rigorous map of the requirements for the key effectors of mutant Kras, and to dissect the biologic mechanisms shared by effectors disruptive of the mutant Kras epigenetic and transcriptional landscape. In turn, we anticipate these findings can inform the subsequent development of novel therapies to address these vulnerabilities. Citation Format: Adrien Grimont, Zhen Zhao, Steven D. Leach, Rohit Chandwani. KrasG12D effector dependencies in the maintenance of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr B20.

Building Human-Machine Trust via Interpretability

Developing human-machine trust is a prerequisite for adoption of machine learning systems in decision critical settings (e.g healthcare and governance). Users develop appropriate trust in these systems when they understand how the systems make their decisions. Interpretability not only helps users understand what a system learns but also helps users contest that system to align with their intuition. We propose an algorithm, AVA: Aggregate Valuation of Antecedents, that generates a consensus feature attribution, retrieving local explanations and capturing global patterns learned by a model. Our empirical results show that AVA rivals current benchmarks.

A method to reduce imbalance for site-level randomized stepped wedge implementation trial designs

BackgroundControlled implementation trials often randomize the intervention at the site level, enrolling relatively few sites (e.g., 6–20) compared to trials that randomize by subject. Trials with few sites carry a substantial risk of an imbalance between intervened (cases) and non-intervened (control) sites in important site characteristics, thereby threatening the internal validity of the primary comparison. A stepped wedge design (SWD) staggers the intervention at sites over a sequence of times or time waves until all sites eventually receive the intervention. We propose a new randomization method, sequential balance, to control time trend in site allocation by minimizing sequential imbalance across multiple characteristics. We illustrate the new method by applying it to a SWD implementation trial.MethodsThe trial investigated the impact of blended internal-external facilitation on the establishment of evidence-based teams in general mental health clinics in nine US Department of Veterans Affairs medical centers. Prior to randomization to start time, an expert panel of implementation researchers and health system program leaders identified by consensus a series of eight facility-level characteristics judged relevant to the success of implementation. We characterized each of the nine sites according to these consensus features. Using a weighted sum of these characteristics, we calculated imbalance scores for each of 1680 possible site assignments to identify the most sequentially balanced assignment schemes.ResultsFrom 1680 possible site assignments, we identified 34 assignments with minimal imbalance scores, and then randomly selected one assignment by which to randomize start time. Initially, the mean imbalance score was 3.10, but restricted to the 34 assignments, it declined to 0.99.ConclusionsSequential balancing of site characteristics across groups of sites in the time waves of a SWD strengthens the internal validity of study conclusions by minimizing potential confounding.Trial registrationRegistered at ClinicalTrials.gov as clinical trials # NCT02543840; entered 9/4/2015.

Biclustering as Strategy for Improving Feature Selection in Consensus QSAR Modeling

Feature selection applied to QSAR (Quantitative Structure-Activity Relationship) modeling is a challenging combinatorial optimization problem due to the high dimensionality of the chemical space associated with molecules and the complexity of the physicochemical properties usually studied in Cheminformatics. This derives commonly in classification models with a large number of variables, decreasing the generalization and interpretability of these classifiers. In this paper, a novel strategy based on biclustering analysis is proposed for addressing this problem. The new method is applied as a post-processing step for feature selection outputs generated by consensus feature selection methods. The approach was evaluated using datasets oriented to ready biodegradation prediction of chemical compounds. These preliminary results show that biclustering can help to identify features with low class-discrimination power, which it is useful for reducing the complexity of QSAR models without losing prediction accuracy.

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework.

Conformational ensembles of biopolymers, whether proteins or chromosomes, can be described using contact matrices. Principal component analysis (PCA) on the contact data has been used to interrogate both protein and chromosome structures and/or dynamics. However, as these fields have developed separately, variants of PCA have emerged. Previously, a variant we hereby term Implicit-PCA (I-PCA) has been applied to chromosome contact matrices and revealed the spatial segregation of active and inactive chromatin. Separately, Explicit-PCA (E-PCA) has previously been applied to proteins and characterized their correlated structure fluctuations. Here, we swapped analysis methods (I-PCA and E-PCA), applying each to a different biopolymer type (chromosome or protein) than the one for which they were initially developed. We find that applying E-PCA to chromosome distance matrices derived from microscopy data can reveal the dominant motion (concerted fluctuation) of these chromosomes. Further, by applying E-PCA to Hi-C data across the human blood cell lineage, we isolated the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we applied I-PCA to simulation snapshots of proteins, the major component reported the consensus features of the structure, making this a promising approach for future analysis of semi-structured proteins.

Chloroplast DNA Dynamics: Copy Number, Quality Control and Degradation.

Endosymbiotically originated chloroplast DNA (cpDNA) encodes part of the genetic information needed to fulfill chloroplast function, including fundamental processes such as photosynthesis. In the last two decades, advances in genome analysis led to the identification of a considerable number of cpDNA sequences from various species. While these data provided the consensus features of cpDNA organization and chloroplast evolution in plants, how cpDNA is maintained through development and is inherited remains to be fully understood. In particular, the fact that cpDNA exists as multiple copies despite its limited genetic capacity raises the important question of how copy number is maintained or whether cpDNA is subjected to quantitative fluctuation or even developmental degradation. For example, cpDNA is abundant in leaves, where it forms punctate structures called nucleoids, which seemingly alter their morphologies and numbers depending on the developmental status of the chloroplast. In this review, we summarize our current understanding of 'cpDNA dynamics', focusing on the changes in DNA abundance. A special focus is given to the cpDNA degradation mechanism, which appears to be mediated by Defective in Pollen organelle DNA degradation 1 (DPD1), a recently discovered organelle exonuclease. The physiological significance of cpDNA degradation in flowering plants is also discussed.