Cell softness reveals tumorigenic potential via ITGB8/AKT/glycolysis signaling in a mice model of orthotopic bladder cancer

Background:Bladder cancer,characterized by a high potential of tumor recurrence,has high lifelong monitoring and treatment costs.To date,tumor cells with intrinsic softness have been identified to function as cancer stem cells in several cancer types.Nonetheless,the existence of soft tumor cells in bladder tumors remains elusive.Thus,our study aimed to develop a microbarrier microfluidic chip to efficiently isolate deformable tumor cells from distinct types of bladder cancer cells.Methods:The stiffness of bladder cancer cells was determined by atomic force microscopy(AFM).The modified microfluidic chip was utilized to separate soft cells,and the 3D Matrigel culture system was to maintain the softness of tumor cells.Expression patterns of integrinβ8(ITGB8),protein kinase B(AKT),and mammalian target of rapamycin(mTOR)were determined by Western blotting.Double immunostaining was conducted to examine the interaction between F-actin and tripartite motif containing 59(TRIM59).The stem-cell-like characteristics of soft cells were explored by colony formation assay and in vivo studies upon xenografted tumor models.Results:Using our newly designed microfluidic approach,we identified a small fraction of soft tumor cells in bladder cancer cells.More importantly,the existence of soft tumor cells was confirmed in clinical human bladder cancer specimens,in which the number of soft tumor cells was associated with tumor relapse.Furthermore,we demonstrated that the biomechanical stimuli arising from 3D Matrigel activated the F-actin/ITGB8/TRIM59/AKT/mTOR/glycolysis pathways to enhance the softness and tumorigenic capacity of tumor cells.Simultaneously,we detected a remarkable up-regulation in ITGB8,TRIM59,and phospho-AKT in clinical bladder recurrent tumors compared with their non-recurrent counterparts.Conclusions:The ITGB8/TRIM59/AKT/mTOR/glycolysis axis plays a crucial role in modulating tumor softness and stemness.Meanwhile,the soft tumor cells become more sensitive to chemotherapy after stiffening,that offers new insights for hampering tumor progression and recurrence.

Method for simultaneously and directly measuring all six-DOF motion errors of a rotary axis

We report a method for simultaneously and directly measuring all six-degrees-of-freedom(six-DOF) motion errors of a rotary axis. Such a method combines the principles of laser interferometry and laser collimation measurement. One reference rotary axis and two retro-reflectors are used to achieve simultaneous sensitivity to all six errors in a full-circle measuring range. As no separation models are required, our method is capable of dynamically measuring these errors in real time and conveniently determining the origin of the errors. An automatic measuring device is built. The effectiveness of our method is experimentally demonstrated.

ResLNet:deep residual LSTM network with longer input for action recognition

Action recognition is an important research topic in video analysis that remains very challenging.Effective recognition relies on learning a good representation of both spatial information(for appearance)and temporal information(for motion).These two kinds of information are highly correlated but have quite different properties,leading to unsatisfying results of both connecting independent models(e.g.,CNN-LSTM)and direct unbiased co-modeling(e.g.,3DCNN).Besides,a long-lasting tradition on this task with deep learning models is to just use 8 or 16 consecutive frames as input,making it hard to extract discriminative motion features.In this work,we propose a novel network structure called ResLNet(Deep Residual LSTM network),which can take longer inputs(e.g.,of 64 frames)and have convolutions collaborate with LSTM more effectively under the residual structure to learn better spatial-temporal representations than ever without the cost of extra computations with the proposed embedded variable stride convolution.The superiority of this proposal and its ablation study are shown on the three most popular benchmark datasets:Kinetics,HMDB51,and UCF101.The proposed network could be adopted for various features,such as RGB and optical flow.Due to the limitation of the computation power of our experiment equipment and the real-time requirement,the proposed network is tested on the RGB only and shows great performance.