Systematic design of tetra-petals auxetic structures with stiffness constraint

This paper focuses on a systematic isogeometric design approach for the optimal petal form and size characterization of tetra-petals auxetics, considering both plane stress and plane strain conditions. The underlying deformation mechanism of a tetra-petals auxetic is analyzed numerically with respect to several key parameters. Design optimizations are performed systematically to give bounding graphs for the minimum Poisson's ratio achievable with different stiffness constraints. Tunable design studies with targeted effective Poisson's ratio, shear modulus and stiffness are demonstrated. Potential application for functionally graded lattice structures is presented. Numerical and experimental verifications are provided to verify the designs. The out-of-plane buckling phenomenon in tension for thin auxetics with re-entrant features is illustrated experimentally to draw caution to results obtained using plane stress formulations for designing such structures.Ministry of Education - Singapore (MOE) Tier 2 Grant R302000139112 National Natural Science Foundation of China (11702036

Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

<p>Abstract</p> <p>Background</p> <p>Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks.</p> <p>Results</p> <p>In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes.</p> <p>We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells.</p> <p>Conclusions</p> <p>Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the underlying mechanisms in breast cancer cells or other human diseases.</p

AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides

Antimicrobial peptides are present ubiquitously in intra- and extra-biological environments and display considerable antibacterial and antifungal activities. Clinically, it has shown good antibacterial effect in the treatment of diabetic foot and its complications. However, the discovery and screening of antimicrobial peptides primarily rely on wet lab experiments, which are inefficient. This study endeavors to create a precise and efficient method of predicting antimicrobial peptides by incorporating novel machine learning technologies. We proposed a deep learning strategy named AMP-EBiLSTM to accurately predict them, and compared its performance with ensemble learning and baseline models. We utilized Binary Profile Feature (BPF) and Pseudo Amino Acid Composition (PSEAAC) for effective local sequence capture and amino acid information extraction, respectively, in deep learning and ensemble learning. Each model was cross-validated and externally tested independently. The results demonstrate that the Enhanced Bi-directional Long Short-Term Memory (EBiLSTM) deep learning model outperformed others with an accuracy of 92.39% and AUC value of 0.9771 on the test set. On the other hand, the ensemble learning models demonstrated cost-effectiveness in terms of training time on a T4 server equipped with 16 GB of GPU memory and 8 vCPUs, with training durations varying from 0 to 30 s. Therefore, the strategy we propose is expected to predict antimicrobial peptides more accurately in the future

Isogeometric shape optimization of smoothed petal auxetic structures via computational periodic homogenization

An important feature that drives the auxetic behaviour of the star-shaped auxetic structures is the hinge-functional connection at the vertex connections. This feature poses a great challenge for manufacturing and may lead to significant stress concentrations. To overcome these problems, we introduced smoothed petal-shaped auxetic structures, where the hinges are replaced by smoothed connections. To accommodate the curved features of the petal-shaped auxetics, a parametrisation modelling scheme using multiple NURBS patches is proposed. Next, an integrated shape design frame work using isogeometric analysis is adopted to improve the structural performance. To ensure a minimum thickness for each member, a geometry sizing constraint is imposed via piece-wise bounding polynomials. This geometry sizing constraint, in the context of isogeometric shape optimization, is particularly interesting due to the non-interpolatory nature of NURBS basis. The effective Poisson ratio is used directly as the objective function, and an adjoint sensitivity analysis is carried out. The optimized designs – smoothed petal auxetic structures – are shown to achieve low negative Poisson’s ratios, while the difficulties of manufacturing the hinges are avoided. For the case with six petals, an in-plane isotropy is achieved.Singapore MOE Tier 2 Grant R30200013911

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Principles of transparency in emissions trading schemes: The Chinese experience

Free to read on publisher website Transparency is fundamental to environmental governance. It promotes public trust, goodwill, and credibility in environmental decision making. It also ensures that monitoring and enforcement of emissions reduction targets are efficient and effective. As the impacts of climate change increase, it is urgent that scholars and policy makers develop and test criteria for transparency in both the calculation of emissions reductions and the public reporting of emissions. This article highlights basic principles of transparency that should inform such criteria and that may be applied on a transnational basis. We also examine China’s recently implemented pilot emissions trading schemes and find that the approach in China does not yet comply with our suggested principles. Nevertheless, the positive direction of environmental governance in this region is encouraging

Insight into the nonlinear effect of COVID-19 on well-being in China: Commuting, a vital ingredient

BACKGROUND: COVID-19 had a devastating impact on people's work, travel, and well-being worldwide. As one of the first countries to be affected by the virus and develop relatively well-executed pandemic control, China has witnessed a significant shift in people's well-being and habits, related to both commuting and social interaction. In this context, what factors and the extent to which they contribute to well-being are worth exploring. METHODS: Through a questionnaire survey within mainland China, 688 valid sheets were collected, capturing various aspects of individuals' life, including travel, and social status. Focusing on commuting and other factors, a Gradient Boosting Decision Tree (GBDT) model was developed based on 300 sheets reporting working trips, to analyze the effects on well-being. Two indicators, i.e., the Relative Importance (RI) and Partial Dependency Plot (PDP), were used to quantify and visualize the effects of the explanatory factors and the synergy among them. RESULTS: Commuting characteristics are the most critical ingredients, followed by social interactions to explain subjective well-being. Commuting stress poses the most substantial effect. Less stressful commuting trips can solidly improve overall well-being. Better life satisfaction is linked with shorter confinement periods and increased restriction levels. Meanwhile, the switch from in-person to online social interactions had less impact on young people's life satisfaction. Older people were unsatisfied with this change, which had a significant negative impact on their life satisfaction. CONCLUSIONS: From the synergy of commuting stress and commuting time on well-being, the effect of commuting time on well-being is mediated by commuting stress in the case of China. Even if one is satisfied with online communication, the extent of enhancement on well-being is minimal, for it still cannot replace face-to-face interaction. The findings can be beneficial in improving the overall well-being of society during the pandemic and after the virus has been eradicated

miRNA-223 is a potential diagnostic and prognostic marker for osteosarcoma

Background: MicroRNA-223 (miR-223) has been shown to be a potential diagnostic and prognostic marker for several cancers. In addition, miR-223 has been reported to suppress osteosarcoma cell proliferation in vitro. However, the clinical value of miR-223 is still unknown. Methods: We detected the expression of miR-223 expression in the serum of osteosarcoma patients and in osteosarcoma cancer cells using RT-PCR. We compared the serum expression of miR-223 with the clinicopathological characteristics and survival of osteosarcoma patients. Finally, we explored the role of miR-223 on the invasion of osteosarcoma cancer cells using cell migration and invasion assays. Results: We observed that the expression of miR-223 was significantly decreased in the serum of osteosarcoma patients and osteosarcoma cancer cells compared to healthy controls (P<0.01). Moreover, a receiver operating characteristic (ROC) curve analysis indicated that serum miR-223 is a potential diagnostic marker of osteosarcoma with an area under the ROC curve (AUC) of 0.956. Importantly, the patients with a lower expression of miR-223 tended to have distant metastasis (P<0.001) and a more advanced clinical stage (P<0.001). In addition, the survival time of patients with low miR-223 expression was significantly shorter compared to patients with high miR-223 expression (P<0.001). Furthermore, we found that miR-223 could inhibit the migration and invasion of osteosarcoma cells. Conclusions: miR-223 might be related to the metastasis of osteosarcoma and could be used as a potential diagnostic and prognostic biomarker in osteosarcoma

Correction: mTOR Inhibition Attenuates Dextran Sulfate Sodium-Induced Colitis by Suppressing T Cell Proliferation and Balancing TH1/TH17/Treg Profile.

[This corrects the article DOI: 10.1371/journal.pone.0154564.]