Self-Healing Multiblock Copolypeptide Hydrogels via Polyion Complexation

Diblock, triblock, and pentablock copolypeptides were designed and prepared for formation of polyion complex hydrogels in aqueous media. Increasing the number of block segments was found to allow formation of hydrogels with substantially enhanced stiffness at equivalent concentrations. Use of similar length ionic segments also allowed mixing of different block architectures to fine-tune hydrogel properties. The pentablock hydrogels possess a promising combination of high stiffness, rapid self-healing properties, and cell compatible surface chemistry that makes them promising candidates for applications requiring injectable or printable hydrogel scaffolds

Self-Sorting Microscale Compartmentalized Block Copolypeptide Hydrogels

Multicomponent interpenetrating network hydrogels possessing enhanced mechanical stiffness compared to their individual components were prepared via physical mixing of diblock copolypeptides that assemble by either hydrophobic association or polyion complexation in aqueous media. Optical microscopy analysis of fluorescent-probe-labeled multicomponent hydrogels revealed that the diblock copolypeptide components rapidly and spontaneously self-sort to form distinct hydrogel networks that interpenetrate at micron length scales. These materials represent a class of microscale compartmentalized hydrogels composed of degradable, cell-compatible components, which possess rapid self-healing properties and independently tunable domains for downstream applications in biology and additive manufacturing

BIOMECHANICAL ANALYSIS OF STRIDE FORCE IN THE ELITE BADMINTON PLAYER

INTRODUCTION: In current badminton competition, the movement of the player is becoming faster and faster. This requires not only basic strength for the basic footwork of players, but also more explosive force in lower limbs which is important for improving moving speed of player on the court. In this study, a method has been developed for the measurement of the propulsive force of one leg during a stride of the elite badminton player. This will provide reference for the training of the badminton player

Ability of γδ T cells to modulate the Foxp3 T cell response is dependent on adenosine.

Whether γδ T cells inhibit or enhance the Foxp3 T cell response depends upon their activation status. The critical enhancing effector in the supernatant is adenosine. Activated γδ T cells express adenosine receptors at high levels, which enables them to deprive Foxp3+ T cells of adenosine, and to inhibit their expansion. Meanwhile, cell-free supernatants of γδ T cell cultures enhance Foxp3 T cell expansion. Thus, inhibition and enhancement by γδ T cells of Foxp3 T cell response are a reflection of the balance between adenosine production and absorption by γδ T cells. Non-activated γδ T cells produce adenosine but bind little, and thus enhance the Foxp3 T cell response. Activated γδ T cells express high density of adenosine receptors and have a greatly increased ability to bind adenosine. Extracellular adenosine metabolism and expression of adenosine receptor A2ARs by γδ T cells played a major role in the outcome of γδ and Foxp3 T cell interactions. A better understanding of the functional conversion of γδ T cells could lead to γδ T cell-targeted immunotherapies for related diseases

Graphene-assisted microfiber for optical-power-based temperature sensor

Combined the large evanescent field of microfiber with the high thermal conductivity of graphene, a sensitive all-fiber temperature sensor based on graphene-assisted micro fiber is proposed and experimentally demonstrated. Microfiber can be easily attached with graphene due to the electrostatic 6 force, resulting in an effective interaction between graphene and the evanescent field of microfiber. The change of the ambient temperature has a great influence on the conductivity of graphene, leading to the variation of the effective refractive index of microfiber. Consequently, the optical power transmission will be changed. The temperature sensitivity of 0.1018 dB/°C in the heating process and 0.1052 dB/°C in the cooling process as well as a high resolution of 0.0098 °C is obtained in the experiment. The scheme may have great potential in sensing fields owing to the advantages of high sensitivity, compact size, and low cost

Microfiber-based inline Mach-Zehnder interferometer for dual-parameter measurement

An approach to realizing simultaneous measurement of refractive index (RI) and temperature based on a microfiber-based dual inline Mach-Zehnder interferometer (MZI) is proposed and demonstrated. Due to different interference mechanisms, as one interference between the core mode and the lower order cladding mode in the sensing single-mode fiber and the other interference between the fundamental mode and the high-order mode in the multimode microfiber, the former interferometer achieves RI sensitivity of -23.67 nm/RIU and temperature sensitivity of 81.2 pm/oC, whereas those of the latter are 3820.23 nm/RIU, and -465.7 pm/oC, respectively. The large sensitivity differences can provide a more accurate demodulation of RI and temperature. The sensor is featured with multiparameters measurement, compact structure, high sensitivity, low cost, and easy fabrication

A novel dissolved oxygen prediction model based on enhanced semi-naive Bayes for ocean ranches in northeast China

A challenge of achieving intelligent marine ranching is the prediction of dissolved oxygen (DO). DO directly reflects marine ranching environmental conditions. Through accurate DO predictions, timely human intervention can be made in marine pasture water environments to avoid problems such as reduced yields or marine crop death due to low oxygen concentrations in the water. We use an enhanced semi-naive Bayes model for prediction based on an analysis of DO data from marine pastures in northeastern China from the past three years. Based on the semi-naive Bayes model, this paper takes the possible values of a DO difference series as categories, counts the possible values of the first-order difference series and the difference series of the interval before each possible value, and selects the most probable difference series value at the next moment. The prediction accuracy is optimized by adjusting the attribute length and frequency threshold of the difference sequence. The enhanced semi-naive Bayes model is compared with LSTM, RBF, SVR and other models, and the error function and Willmott’s index of agreement are used to evaluate the prediction accuracy. The experimental results show that the proposed model has high prediction accuracy for DO attributes in marine pastures