COL12A1 rs970547 Polymorphism Does Not Alter Susceptibility to Anterior Cruciate Ligament Rupture: A Meta-Analysis

Objective: Currently available evidence regarding the association between collagen type XII α1 (COL12A1) polymorphism and risk of anterior cruciate ligament rupture (ACLR) remains elusive. The aim of our present study was to assess the association between COL12A1 rs970547 polymorphism and ACLR risk.Methods: Five online databases, namely, PubMed, EMBASE, ISI Web of Science, CENTRAL, and CNKI, were searched from their inception data up to December 2020 to identify relative observational studies. The methodological quality of each individual study was evaluated using the Newcastle-Ottawa Scale (NOS). The “model-free approach” was employed to estimate the magnitude of effect of COL12A1 rs970547 polymorphism on ACLR, and the association was expressed using odds ratio (OR) and its associated 95% confidence interval (95% CI). Subgroup analysis was performed by ethnicity and sex of included subjects.Results: Eight studies involving 1,477 subjects with ACLR and 100,439 healthy controls were finally included in our study. The methodological quality of included studies was deemed moderate to high based on NOS scores. The “model-free” approach suggested no genotype differences between ACLR and healthy control for the rs970547 polymorphism, but we still used the allele model to present the combined data. Under the random-effect model, there was no significant difference in the frequency of effecting allele between ACLR and control (OR: 0.91, 95% CI 0.77, 1.08; p = 0.28). Stratified analysis by sex and ethnicity also showed no difference in allele frequency.Conclusion: The findings of this current meta-analysis suggested that rs970547 was not associated with ACLR risk in male, female, and the overall population among Asians or Caucasians.

Model-Free Approach for the Configurational Analysis of Marine Natural Products

The NMR-based configurational analysis of complex marine natural products is still not a routine task. Different NMR parameters are used for the assignment of the relative configuration: NOE/ROE, homo- and heteronuclear J couplings as well as anisotropic parameters. The combined distance geometry (DG) and distance bounds driven dynamics (DDD) method allows a model-free approach for the determination of the relative configuration that is invariant to the choice of an initial starting structure and does not rely on comparisons with (DFT) calculated structures. Here, we will discuss the configurational analysis of five complex marine natural products or synthetic derivatives thereof: the cis-palau’amine derivatives 1a and 1b, tetrabromostyloguanidine (1c), plakilactone H (2), and manzamine A (3). The certainty of configurational assignments is evaluated in view of the accuracy of the NOE/ROE data available. These case studies will show the prospective breadth of application of the DG/DDD method.

Model-free estimation of available power using deep learning

In order to assess the level of power reserves during down-regulation, the available power of a wind turbine needs to be estimated. The current practice in available power estimation is heavily dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a single-input model-free approach dynamic estimation of the available power using recurrent neural networks. Accordingly, it combines wind turbine control considerations and modern forecasting methodologies for a model-free, single-input estimation of available power. It enables a robust real-time implementation of dynamic delta control, as well as higher-accuracy provision of the reserves to the system operators. The model-free approach requires only 1 Hz wind speed measurements as input and estimates 1 Hz available power as output. The neural network is trained, tested and validated using the DTU 10 MW reference wind turbine HAWC2 model under realistic atmospheric conditions. The unsteady patterns in the turbulent flow are represented via long short-term memory (LSTM) neurons which are trained during a period of normal operation. The adaptability of the network to changing inflow conditions is ensured via transfer learning, where the last LSTM layer is updated using new measurements. It is seen that the sensitivity of the networks to changing wind speed is much higher than that of turbulence, and the updates are to be implemented solely based on the altering inflow velocity. The validation of the trained LSTM networks on time series with 7, 9 and 11 m s−1 mean wind speeds demonstrates high accuracy (less than 1 % bias) and capability of transfer-learning online. Including highly turbulent inflow cases, the networks have shown to comply with the most recent grid codes, which require the quality of the available power estimations to be evaluated with high accuracy (less than 3.3 % standard deviation of the error around zero bias) at 1 min intervals.

MOVICS: an R package for multi-omics integration and visualization in cancer subtyping

SummaryStratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using a model-free approach for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort.Availability and implementationMOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS.