Feasibility of Combined UASB-MBR System in Treating PTA Wastewater and Polyimide Membrane for Biogas Purification

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

<p>Abstract</p> <p>Background</p> <p>Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.</p> <p>Results</p> <p>This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors.</p> <p>Conclusions</p> <p>Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information.</p

Real value prediction of protein solvent accessibility using enhanced PSSM features

<p>Abstract</p> <p>Background</p> <p>Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).</p> <p>Results</p> <p>This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.</p> <p>Conclusion</p> <p>Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.</p

Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission

Sequential labeling is a task predicting labels for each token in a sequence, such as Named Entity Recognition (NER). NER tasks aim to extract entities and predict their labels given a text, which is important in information extraction. Although previous works have shown great progress in improving NER performance, uncertainty estimation on NER (UE-NER) is still underexplored but essential. This work focuses on UE-NER, which aims to estimate uncertainty scores for the NER predictions. Previous uncertainty estimation models often overlook two unique characteristics of NER: the connection between entities (i.e., one entity embedding is learned based on the other ones) and wrong span cases in the entity extraction subtask. Therefore, we propose a Sequential Labeling Posterior Network (SLPN) to estimate uncertainty scores for the extracted entities, considering uncertainty transmitted from other tokens. Moreover, we have defined an evaluation strategy to address the specificity of wrong-span cases. Our SLPN has achieved significant improvements on two datasets, such as a 5.54-point improvement in AUPR on the MIT-Restaurant dataset.Comment: 11 pages, 2 figure

BIOMECHANICAL EFFECT OF GROUP EXERCISE PROGRAM USING STABILITY BALL ON THE COUNTER MOVEMENT JUMP

The purpose of this study was to evaluate the training effect of three-month group exercise program using stability ball. Ten female subjects were recruited to join this group exercise class for three months. The biomechanical parameters of counter-movement jump were collected before and after class. The average of maximal jumping height and the take-off velocity have significantly increased after three months. The group exercise program accoring American College of Sport Medicine trainig guideline which was designed in this study was helpful to increase the jump height of counter movement jump and to improve the biomechanical parameters of the landing

Integrin-mediated membrane blebbing is dependent on the NHE1 and NCX1 activities.

Integrin-mediated signal transduction and membrane blebbing have been well studied to modulate cell adhesion, spreading and migration^1-6^. However, the relationship between membrane blebbing and integrin signaling has not been explored. Here we show that integrin-ligand interaction induces membrane blebbing and membrane permeability change. We found that sodium-proton exchanger 1 (NHE1) and sodium-calcium exchanger 1 (NCX1) are located in the membrane blebbing sites and inhibition of NHE1 disrupts membrane blebbing and decreases membrane permeability change. However, inhibition of NCX1 enhances cell blebbing to cause cell swelling which is correlated with an intracellular sodium accumulation induced by NHE17. These data suggest that sodium influx induced by NHE1 is a driving force for membrane blebbing growth, while sodium efflux induced by NCX1 in a reverse mode causes membrane blebbing retraction. Together, these data reveal a novel function of NHE1 and NCX1 in membrane permeability change and blebbing and provide the link for integrin signaling and membrane blebbing

Exosomal miRNA 16-5p/29a-3p from pancreatic cancer induce adipose atrophy by inhibiting adipogenesis and promoting lipolysis.

Over 80% of the patients with pancreatic ductal adenocarcinoma (PDAC) have cachexia/wasting syndrome. Cachexia is associated with reduced survival, decreased quality of life, and higher metastasis rates. Here, we demonstrate that fat loss is the earliest feature of PDAC-exosome-induced cachexia. MicroRNA sequencing of exosomal components from normal and cancer-derived exosomes revealed enrichment of miR-16-5p, miR-21-5p, miR-29a-3p, and miR-125b-5p in serum exosomes of mice harboring PDAC and patients with PDAC. Further, miR-16-5p and miR-29a-3p inhibited adipogenesis through decreasing Erlin2 and Cmpk1 expression which downregulates C/EBPβ and PPARγ. Synergistically, miR-29a-3p promotes lipolysis through increasing ATGL expression by suppressing MCT1 expression. Furthermore, PDAC-exosomes deprived of miR-16-5p and miR-29a-3p fail to induce fat loss. Hence, miR-16-5p and miR-29a-3p exosomal miRs are essential for PDAC-induced fat loss. Thus, we unravel that PDAC induces adipose atrophy via exosomal miRs. This knowledge may provide new diagnostic and therapeutic strategies for PDAC-induced cachexia