Litter quality and climate regulate the effect of invertebrates on litter decomposition in terrestrial and aquatic ecosystems: A global meta-analysis.

Although the exclusion effects of invertebrate decomposers on litter decomposition have been extensively studied in different experimental contexts, a thorough comparison of the exclusion effects of invertebrate decomposers with different body sizes on litter decomposition and its possible regulatory factors in terrestrial and aquatic ecosystems is still lacking. Here, through a meta-analysis of 1207 pairs of observations from 110 studies in terrestrial ecosystems and 473 pairs of observations from 60 studies in aquatic ecosystems, we found that invertebrate exclusion reduced litter decomposition rates by 36 % globally, 30 % in terrestrial ecosystems, and 44 % in aquatic ecosystems. At the global scale, the exclusion effects of macroinvertebrates and mesoinvertebrates on litter decomposition rates (reduced by 38 % and 36 %, respectively) were greater than those of the combination of macroinvertebrates and mesoinvertebrates (reduced by 30 %). In terrestrial and aquatic ecosystems, the effects of invertebrate exclusion on litter decomposition rates were mainly regulated by climate and initial litter quality, but the effects of invertebrate exclusion with different body sizes were regulated differently by climate, initial litter quality, and abiotic environmental variables. These findings will help us better understand the role of invertebrate decomposers in litter decomposition, especially for invertebrate decomposers with different body sizes, and underscore the need to incorporate invertebrate decomposers with different body sizes into dynamic models of litter decomposition to examine the potential effects and regulatory mechanisms of land-water-atmosphere carbon fluxes.

Fast peak error correction algorithms for proteoform identification using top-down tandem mass spectra.

MOTIVATION: Proteoform identification is an important problem in proteomics. The main task is to find a modified protein that best fits the input spectrum. To overcome the combinatorial explosion of possible proteoforms, the proteoform mass graph and spectrum mass graph are used to represent the protein database and the spectrum, respectively. The problem becomes finding an optimal alignment between the proteoform mass graph and the spectrum mass graph. Peak error correction is an important issue for computing an optimal alignment between the two input mass graphs. RESULTS: We propose a faster algorithm for the error correction alignment of spectrum mass graph and proteoform mass graph problem and produce a program package TopMGFast. The newly designed algorithms require less space and running time so that we are able to compute global optimal alignments for the two input mass graphs in a reasonable time. For the local alignment version, experiments show that the running time of the new algorithm is reduced by 2.5 times. For the global alignment version, experiments show that the maximum mass errors between any pair of matched nodes in the alignments obtained by our method are within a small range as designed, while the alignments produced by the state-of-the-art method, TopMG, have very large maximum mass errors for many cases. The obtained alignment sizes are roughly the same for both TopMG and TopMGFast. Of course, TopMGFast needs more running time than TopMG. Therefore, our new algorithm can obtain more reliable global alignments within a reasonable time. This is the first time that global optimal error correction alignments can be obtained using real datasets. AVAILABILITY AND IMPLEMENTATION: The source code of the algorithm is available at https://github.com/Zeirdo/TopMGFast.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

Assembly of 5H-dibenzo[a,d]cycloheptenes by a formal [5 + 2] annulation of ortho-aryl alkynyl benzyl alcohols with arenes.

A new synthetic methodology for the synthesis of 5H-dibenzo[a,d]cycloheptenes from ortho-aryl alkynyl benzyl alcohols and arenes via a Tf2O-mediated formal [5 + 2] annulation reaction has been achieved. From this transformation, structurally diverse 5H-dibenzo[a,d]cycloheptenes were achieved in moderate to good yields. This transformation probably involves an intermolecular Friedel-Crafts-type alkylation and a subsequent intramolecular 7-endo-dig cyclization in one pot, highlighting the high efficiency, regioselectivity, and step-economy of this protocol.

Proteoform identification based on top-down tandem mass spectra with peak error corrections.

In this paper, we study the problem for finding complex proteoforms from protein databases based on top-down tandem mass spectrum data. The main difficulty to solve the problem is to handle the combinatorial explosion of various alterations on a protein. To overcome the combinatorial explosion of various alterations on a protein, the problem has been formulated as the alignment problem of a proteoform mass graph (PMG) and a spectrum mass graph (SMG). The other important issue is to handle mass errors of peaks in the input spectrum. In previous methods, an error tolerance value is used to handle the mass differences between the matched consecutive nodes/peaks in PMG and SMG. However, such a way to handle mass error can not guarantee that the mass difference between any pairs of nodes in the alignment is approximately the same for both PMG and SMG. It may lead to large error accumulation if positive (or negative) errors occur consecutively for a large number of consecutive matched node pairs. The problem is severe so that some existing software packages include a step to further refine the alignments. In this paper, we propose a new model to handle the mass errors of peaks based on the formulation of the PMG and SMG. Note that the masses of sub-paths on the PMG are theoretical and suppose to be accurate. Our method allows each peak in the input spectrum to have a predefined error range. In the alignment of PMG and SMG, we need to give a correction of the mass for each matched peak within the predefined error range. After the correction, we impose that the mass between any two (not necessarily consecutive) matched nodes in the PMG is identical to that of the corresponding two matched peaks in the SMG. Intuitively, this kind of alignment is more accurate. We design an algorithm to find a maximum number of matched node and peak pairs in the two (PMG and SMG) mass graphs under the new constraint. The obtained alignment can show matched node and peak pairs as well as the corrected positions of peaks. The algorithm works well for moderate size input instances and takes very long time as well as huge size memory for large input size instances. Therefore, we propose an algorithm to do diagonal alignment. The diagonal alignment algorithm can solve large input size instances in reasonable time. Experiments show that our new algorithms can report alignments with much larger number of matched node pairs. The software package and test data sets are available at https://github.com/Zeirdo/TopMGRefine.

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.

Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information.

Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.