Improving consensus contact prediction via server correlation reduction

<p>Abstract</p> <p>Background</p> <p>Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them.</p> <p>Results</p> <p>In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top <it>L</it>/5 predicted contacts are evaluated where <it>L </it>is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively.</p> <p>Conclusion</p> <p>Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.</p

Effect of MgO and Al2O3 on High-temperature Stability Performance of High-alumina Cement

High-alumina cement has an important position in refractory materials with its good performance at high temperatures, but its disadvantages such as strength inversion and unstable transformation of hydration products have always limited its development. To clarify the working mechanism of high-alumina cement and improve its high temperature resistance, MgO and Al2O3 were added to the high-alumina cement paste. The optimal design method was used to determine the influence of each factor on the high temperature stability of the cement paste. The mix ratio of raw materials was optimized and the strength change patterns of the specimens under the optimal ratio were verified. From a microstructure perspective, the high temperature evolution of the hardened paste of high-alumina cement was explored using X-ray diffraction, scanning electron microscopy, thermogravimetry, and differential scanning calorimetry. The results show that the introduction of refractory powders, especially Al2O3, can significantly improve the volumetric stability of the cement paste at high temperatures. When the water-cement ratio is 0.20, the admixture of MgO is 5 % or 10 %, and Al2O3 is 20 %, the high temperature volume stability of the cement paste is the best. However, its corresponding mechanical strength is weakened to some extent with an increase in calcinating temperature. Moreover, the structure-property evolution process of cementite under high temperature calcinating conditions was verified by microstructural characterization, especially the influence of the powder on the volume and strength of the cement block at high temperatures. The results of this study can serve as a guide for the development of high-alumina cement and its cementing materials, as well as for the improvement of their properties

Regulation of RhoA/ROCK1 signaling pathway by miR 26b in sepsis induced acute lung injury

Purpose: To investigate the role of miR-26b in the regulation of RhoA/ ROCK1 signaling pathway in acute lung injury (ALI) caused by sepsis. Methods: Thirty male rats were randomized into sham group (SG), cecal ligation and puncture (CLP) group (CG) and miR-26b mimic group (MG). Hematoxylin and eosin (H &amp; E) staining assay was performed to determine the pathological characteristics of rat lung tissues in each group, while enzyme-linked immunosorbent assay (ELISA) was conducted to determine TNF-α and IL-1β levels. The miR-26b expression was evaluated by quantitative reverse transcription polymerase chain reaction (qRT-PCR), while RhoA and Rock1 protein levels were assessed using western blotting. Results: The CG had significant lung injury in comparison with the SG. There were significant elevation in TNF-α and IL-1β levels (p &lt; 0.05). RhoA and ROCK1 levels in lung tissue were noticeably elevated in CG (p &lt; 0.05). After treatment, lung injury in MG was reduced in contrast to CG. The MG showed statistically significant decrease (p &lt; 0.05) in the levels of TNF-α and IL-1β, while the lung tissue mRNA expression and the RhoA and ROCK1 expression levels were significantly reduced in MG (p &lt; 0.05). Conclusion: The MiR-26b mimics plays an important role in the treatment of ALI induced by sepsis in rats by regulating RhoA/ROCK1 signaling pathway. Thus, the findings of this study provide a theoretical basis for clinical studies on the use of miR-26b in the therapy of sepsis

InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems

In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems. The prevailing approach is to pretrain a powerful task-agnostic model on a large unlabeled corpus but may struggle to transfer knowledge to downstream tasks. In this study, we propose InstructMol, a semi-supervised learning algorithm, to take better advantage of unlabeled examples. It introduces an instructor model to provide the confidence ratios as the measurement of pseudo-labels' reliability. These confidence scores then guide the target model to pay distinct attention to different data points, avoiding the over-reliance on labeled data and the negative influence of incorrect pseudo-annotations. Comprehensive experiments show that InstructBio substantially improves the generalization ability of molecular models, in not only molecular property predictions but also activity cliff estimations, demonstrating the superiority of the proposed method. Furthermore, our evidence indicates that InstructBio can be equipped with cutting-edge pretraining methods and used to establish large-scale and task-specific pseudo-labeled molecular datasets, which reduces the predictive errors and shortens the training process. Our work provides strong evidence that semi-supervised learning can be a promising tool to overcome the data scarcity limitation and advance molecular representation learning

Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach

Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t-tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment

Creating Ferromagnetic Insulating La0.9Ba0.1MnO3 Thin Films by Tuning Lateral Coherence Length

In this work, heteroepitaxial vertically aligned nanocomposite (VAN) La0.9Ba0.1MnO3 (LBMO)-CeO2 films are engineered to produce ferromagnetic insulating (FMI) films. From combined X-ray photoelectron spectroscopy, X-ray diffraction and electron microscopy, the elimination of the insulator-metal (I-M) transition is shown to result from the creation of very small lateral coherence lengths (with the corresponding lateral size ~ 3 nm (~ 7 u.c.)) in the LBMO matrix, achieved by engineering a high density of CeO2 nanocolumns in the matrix. The small lateral coherence length leads to a shift in the valence band maximum and reduction of the double exchange (DE) coupling. There is no "dead layer" effect at the smallest lateral coherence length achieved of ~ 3 nm. The FMI behaviour obtained by lateral dimensional tuning is independent of substrate interactions, thus intrinsic to the film itself and hence not related to film thickness. The unique properties of VAN films give the possibility for multilayer spintronic devices that can be made without interface degradation effects between the layers.Royal Academy of Engineering - CIET1819_24, the Leverhulme Trust grant RPG-2015-017, EPSRC grants EP/N004272/1, EP/T012218/1, and EP/M000524/1, the Isaac Newton Trust in Cambridge (minute 16.24(p) and RG96474). EU grant H2020-MSCA-IF-2016 (745886)-MuStMAM

Real-time scheduling of renewable power systems through planning-based reinforcement learning

The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the future scheduling system to make real-time scheduling decisions aligning with ultra-short-term forecasts. Restricted by the computation speed, traditional optimization-based methods can not solve this problem. Recent developments in reinforcement learning (RL) have demonstrated the potential to solve this challenge. However, the existing RL methods are inadequate in terms of constraint complexity, algorithm performance, and environment fidelity. We are the first to propose a systematic solution based on the state-of-the-art reinforcement learning algorithm and the real power grid environment. The proposed approach enables planning and finer time resolution adjustments of power generators, including unit commitment and economic dispatch, thus increasing the grid's ability to admit more renewable energy. The well-trained scheduling agent significantly reduces renewable curtailment and load shedding, which are issues arising from traditional scheduling's reliance on inaccurate day-ahead forecasts. High-frequency control decisions exploit the existing units' flexibility, reducing the power grid's dependence on hardware transformations and saving investment and operating costs, as demonstrated in experimental results. This research exhibits the potential of reinforcement learning in promoting low-carbon and intelligent power systems and represents a solid step toward sustainable electricity generation.Comment: 12 pages, 7 figure