CDK5 inhibition promotes osteoblastic differentiation of MSCs and blocks the migration of osteosarcoma MG-63 cells

CDK5 belongs to the cyclin-dependent kinase family.CDK5 is multifunctional and plays an important role in neural differentiation.However,the role of CDK5 in osteoblastic differentiation remains unclear.The present study investigated functions and molecular mechanism of CDK5 in osteoblastic differentiation.It was found that,CDK5 inhibition promoted the expression of Runx2,ALP,OCN and OPN of MSCs and the mineralization of MC-3T3E1 cells and MSCs.CDK5 inhibition enhanced the development of F-actin,nuclear localization ofβ-catenin and YAP,as well as the expression of RMRP RNA.When F-actin was suppressed by Blebbistatin,the nuclear localization of YAP andβ-catenin,and expression of RMRP RNA as well as Runx2 and ALP were decreased.These indicate Seliciclib promotes osteoblastic differentiation mainly by F-actin.Moreover,Seliciclib also suppressed the migration of MG-63,suggesting a potential application for Seliciclib in bone defect repair and inhibition of the migration of osteosarcoma cells after osteosarcoma surgical resection.

Lower Bounds of Decay Rates for Solution to the Single-Layer Quasi-Geostrophic Model

In this paper, we study the long-time behavior of solutions of the single-layer quasi-geostrophic model arising from geophysical fluid dynamics. We obtain the lower bound of the decay estimate of the solution. Utilizing the Fourier splitting method, under suitable assumptions on the initial data, for any multi-index α, we show that the solution Ψ satisfies .

Cephalopods-inspired Repairable MWCNTs/PDMS Conductive Elastomers for Sensitive Strain Sensor

Flexible electronic devices based on reversible bonds for self-curing capabilities arouses extensive interest.However,most of the composite conductive elastomers have the problems of poor mechanical properties and slow recovery of mechanical properties during multiple stretching,which hinder their stability in continuous operation.In this study,hyperbranched-MWCNTs/hyperbranched-PDMS self-healable conductive elastomers inspired by cephalopods were successfully developed.The prepared conductive elastomer exhibited good self-healing ability(91%)at room temperature excited by multiple reversible interactions.The prepared elastomer showed outstanding mechanical properties and anti-fatigue ability,so that it can cope with more arduous tasks.Moreover,the elastomer was sensitive to the change of stress states and can be used as a stable strain sensor.Therefore,the self-repairing conductive elastomer has potential practicability in the fields of human-computer interaction,motion monitoring,soft robot and so on.

Joule-heated carbonized melamine sponge for high-speed absorption of viscous oil spills

Introducing heating function to oil sorbents opens up a new pathway to the fast cleanup of viscous crude oil spills in situ.The oil sorption speed increases with the rise of the temperature,thus oil sorbents with high heating temperature are desirable.Besides,the oil sorbents also need to be produced environment-friendly.Here we present carbonized melamine-formaldehyde sponges(CMSs)that exhibited superior heating performance and the CMSs could be massively fabricated through a non-polluting pyrolysis process.The conductive CMSs could be heated over 300℃with a low applied voltage of 6.9 V and keep above 250℃for 30 min in the air without obvious damage.Such high heating performance enabled heating up the oil spills with a high rate of 2.65℃·s^(-1) and 14%improvement of oil sorption coefficient compared with the state-of-the-art value.We demonstrated that one joule-heated CMS could continuously and selectively collect viscous oil spills(9,010 mPa·s)690 times its own weight in one hour.The CMSs will be a highly competitive sorbent material for the fast remediation of future crude oil spills.

Highly stretchable,soft and sticky PDMS elastomer by solvothermal polymerization process

Siloxane rubber shows attractive properties of high stability,elasticity and transparency.Besides,the regulation of its properties renders it widely used in many application fields.However,most of the reported performance improvement methods of siloxane rubber focus on the change of chemical composition of siloxane rubber,including the design of molecular chain and the introduction of other compounds,etc.Such a strategy is still faced with many limitations in practical application.In this work,on the premise of not changing the chemical composition of siloxane rubber,we propose a facile solvothermal polymerization process to change the structure of cross-linking networks,so as to obtain the siloxane rubber with controllable mechanical properties.Compared to the normal curing method,we realized polydimethylsiloxane elastomer(PDMS)with maximum elongation of more than 3,000%(>10 times of normally cured one)and tensile modulus lower than 0.15 MPa(<1/10 of normally cured one).In addition to superior stretchability,it gains extra high softness,stickiness and sensitive response to organic solvents.Based on our solvothermal cured PDMS,its applications in oil collection and organic solvent sensor have been demonstrated.It is expected that this method can be readily utilized widely and shows great application potentials.

Scene-adaptive crowd counting method based on meta learning with dual-input network DMNet

Crowd counting is recently becoming a hot research topic, which aims to count the number of the people in different crowded scenes. Existing methods are mainly based on training-testing pattern and rely on large data training, which fails to accurately count the crowd in real-world scenes because of the limitation of model’s generalization capability. To alleviate this issue, a scene-adaptive crowd counting method based on meta-learning with Dual-illumination Merging Network (DMNet) is proposed in this paper. The proposed method based on learning-to-learn and few-shot learning is able to adapt different scenes which only contain a few labeled images. To generate high quality density map and count the crowd in low-lighting scene, the DMNet is proposed, which contains Multi-scale Feature Extraction module and Element-wise Fusion Module. The Multi-scale Feature Extraction module is used to extract the image feature by multi-scale convolutions, which helps to improve network accuracy. The Element-wise Fusion module fuses the low-lighting feature and illumination-enhanced feature, which supplements the missing illumination in low-lighting environments. Experimental results on benchmarks, WorldExpo’10, DISCO, USCD, and Mall, show that the proposed method outperforms the existing state-of-the-art methods in accuracy and gets satisfied results.