An improved method for quantifying total fine root decomposition in plantation forests combining measurements of soil coring and minirhizotrons with a mass balance model.

Accurate measurement of total fine root decomposition (the amount of dead fine roots decomposed per unit soil volume) is essential for constructing a soil carbon budget. However, the ingrowth/soil core-based models are dependent on the assumptions that fine roots in litterbags/intact cores have the same relative decomposition rate as those in intact soils and that fine root growth and death rates remain constant over time, while minirhizotrons cannot quantify the total fine root decomposition. To improve the accuracy of estimates for total fine root decomposition, we propose a new method (balanced hybrid) with two models that integrate measurements of soil coring and minirhizotrons into a mass balance model.

Leptin Expression in Human Epicardial Adipose Tissue Is Associated with Local Coronary Atherosclerosis.

BACKGROUND Leptin is an adipokine related to overweight and cardiovascular diseases. However, the leptin expression level in epicardial adipose tissue (EAT) of humans and its association with coronary atherosclerosis has never been investigated. MATERIAL AND METHODS Patients receiving cardiac surgery were divided into a coronary artery disease group (CAD group) and a non-CAD group (NCAD group).

Pretreatment Attenuates Myocardial Ischemia-Reperfusion Injury via mitoBK.

extracts (EGb) alleviate myocardial ischemia/reperfusion (MI/R) injury. However, the underlying mechanisms have not yet been characterized. This study aimed to investigate whether activation of large-conductance -activated channels at the inner mitochondrial membrane ( of cardiomyocytes is involved in extract-mediated cardioprotection.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates.

Accurate prediction of protein relative solvent accessibility using a balanced model.

Background: Protein relative solvent accessibility provides insight into understanding protein structure and function. Prediction of protein relative solvent accessibility is often the first stage of predicting other protein properties. Recent predictors of relative solvent accessibility discriminate against exposed regions as compared with buried regions, resulting in higher prediction accuracy associated with buried regions relative to exposed regions.

Metabolomics reveals positive acceleration(+Gz)-induced metabolic perturbations and the protective effect of Ginkgo biloba extract in a rat model based on ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry.

Positive acceleration (+Gz) in the head-to-foot direction generated by modern high-performance fighter jets during flight maneuvers is characterized by high G values and a rapid rate of acceleration, and is often long in duration and a repeated occurrence. The acceleration overload far exceeds the pilot's physiological tolerance limits and causes considerable strain on several organ systems. Despite the importance of monitoring pathophysiological alterations related to +Gz exposure, we lack a complete explanation of the pathophysiology of +Gz exposure.

DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

Unlabelled: The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment.

NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data.

SPSSM8: an accurate approach for predicting eight-state secondary structures of proteins.

Protein eight-state secondary structure prediction is challenging, but is necessary to determine protein structure and function. Here, we report the development of a novel approach, SPSSM8, to predict eight-state secondary structures of proteins accurately from sequences based on the structural position-specific scoring matrix (SPSSM). The SPSSM has been successfully utilized to predict three-state secondary structures.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm.

DomHR: accurately identifying domain boundaries in proteins using a hinge region strategy.

Motivation: The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved.

Results: In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy.

Predicting turns in proteins with a unified model.

Motivation: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously.

Shape string: a new feature for prediction of DNA-binding residues.

Protein-DNA interactions are involved in many biological processes essential for gene expression and regulation. To understand the molecular mechanisms of protein-DNA recognition, it is crucial to analyze and identify DNA-binding residues of protein-DNA complexes. Here, we proposed a novel descriptor shape string and another two related features shape string PSSM and shape string pair composition to characterize DNA-binding residues.

Predicting gram-positive bacterial protein subcellular localization based on localization motifs.

The subcellular localization of proteins is closely related to their functions. In this work, we propose a novel approach based on localization motifs to improve the accuracy of predicting subcellular localization of Gram-positive bacterial proteins. Our approach performed well on a five-fold cross validation with an overall success rate of 89.

DSP: a protein shape string and its profile prediction server.

Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string.

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins.

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space.

Identification of the subcellular localization of mycobacterial proteins using localization motifs.

Mycobacterium, the most common disease-causing genus, infects billions of people and is notoriously difficult to treat. Understanding the subcellular localization of mycobacterial proteins can provide essential clues for protein function and drug discovery. In this article, we present a novel approach that focuses on local sequence information to identify localization motifs that are generated by a merging algorithm and are selected based on a binomially distributed model.

A novel structural position-specific scoring matrix for the prediction of protein secondary structures.

Motivation: The precise prediction of protein secondary structure is of key importance for the prediction of 3D structure and biological function. Although the development of many excellent methods over the last few decades has allowed the achievement of prediction accuracies of up to 80%, progress seems to have reached a bottleneck, and further improvements in accuracy have proven difficult.

Results: We propose for the first time a structural position-specific scoring matrix (SPSSM), and establish an unprecedented database of 9 million sequences and their SPSSMs.

Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine model.

Background: The β-turn is a secondary protein structure type that plays an important role in protein configuration and function. Development of accurate prediction methods to identify β-turns in protein sequences is valuable. Several methods for β-turn prediction have been developed; however, the prediction quality is still a challenge and there is substantial room for improvement.

Metabolomic analysis of normal (C57BL/6J, 129S1/SvImJ) mice by gas chromatography-mass spectrometry: detection of strain and gender differences.

Previous studies have shown that the C57 and 129 strains of mice display marked differences in behavioural performance, neuroanatomy, neurochemistry and synaptic plasticity. However, few metabolomic studies of their biofluids have been performed. As part of a series of metabolic phenotyping, the effects of gender and strain upon serum metabolite composition and variation are examined in this study using gas chromatography-mass spectrometry (GC-MS) in normal C57BL/6J and 129S1/SvImJ strains of mice.

Using predicted shape string to enhance the accuracy of γ-turn prediction.

Numerous methods for predicting γ-turns in proteins have been developed. However, the results they generally provided are not very good, with a Matthews correlation coefficient (MCC)≤0.18.

Cancer classification from the gene expression profiles by Discriminant Kernel-PLS.

Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results.

Direct decomposition of three-way arrays using a non-negative approximation.

Non-negative matrix approximation (NNMA) has been used in diverse scientific fields, but it still has some major limitations. In the present study a novel trilinear decomposition method, termed three-way NNMA (TWNNMA), was developed. The method decomposes three-way arrays directly without unfolding and overcomes the restriction of locking zero elements in the deduced multiplicative update rules by adding a positive symmetric matrix.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.

Background: Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.

Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information.

Sequence-based approach for motif prediction is of great interest and remains a challenge. In this work, we develop a local combinational variable approach for sequence-based helix-turn-helix (HTH) motif prediction. First we choose a sequence data set for 88 proteins of 22 amino acids in length to launch an optimized traversal for extracting local combinational segments (LCS) from the data set.