The physical interactions of growing bacterial cells with each other and with their surroundings significantly affect the structure and dynamics of biofilms. Here a 3D agent-based model is formulated to describe the establishment of simple bacterial colonies expanding by the physical force of their growth. With a single set of parameters, the model captures key dynamical features of colony growth by non-motile, non EPS-producing E. coli cells on hard agar. The model, supported by experiment on colony growth in different types and concentrations of nutrients, suggests that radial colony expansion is not limited by nutrients as commonly believed, but by mechanical forces. Nutrient penetration instead governs vertical colony growth, through thin layers of vertically oriented cells lifting up their ancestors from the bottom. Overall, the model provides a versatile platform to investigate the influences of metabolic and environmental factors on the growth and morphology of bacterial colonies.

In this work, for the first time, Ti(4+)-Fe3O4@polydopamine microspheres were designed and synthesized for efficient and selective enrichment of phosphopeptides in biological samples.

To discover trace phosphorylated proteins or peptides with great biological significance for in-depth phosphoproteome analysis, it is urgent to develop a novel technique for highly selective and effective enrichment of phosphopeptides. In this work, an IMAC (immobilized metal ion affinity chromatography) material with polydopamine coated on the surface of graphene and functionalized with titanium ions (denoted as Ti(4+)-G@PD) was initially designed and synthesized. The newly prepared Ti(4+)-G@PD with enhanced hydrophilicity and biological compatibility was characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and infrared (IR), and its performance for selective and effective enrichment of phosphopeptide was evaluated with both standard peptide mixtures and human serum.

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.