Smad2 mediates Activin/Nodal signaling in mesendoderm differentiation of mouse embryonic stem cells

Although Activin/Nodal signaling regulates pluripotency of human embryonic stem （ES） cells, how this signaling acts in mouse ES cells remains largely unclear. To investigate this, we confirmed that mouse ES cells possess active Smad2-mediated Activin/Nodal signaling and found that Smad2-mediated Activin/Nodal signaling is dispensable for self-renewal maintenance but is required for proper differentiation toward the mesendoderm lineage. To gain insights into the underlying mechanisms, Smad2-associated genes were identified by genome-wide chromatin immu- noprecipitation-chip analysis. The results showed that there is a transcriptional correlation between Smad2 binding and Activin/Nodal signaling modulation, and that the development-related genes were enriched among the Smad2- bound targets. We further identified Tapbp as a key player in mesendoderm differentiation of mouse ES cells acting downstream of the Activin/Nodal-Smad2 pathway. Taken together, our findings suggest that Smad2-mediated Activin/Nodal signaling orchestrates mesendoderm lineage commitment of mouse ES cells through direct modulation of corresponding developmental regulator expression.

Application of deep learning technique to the sea surface height prediction in the South China Sea

A deep-learning-based method,called ConvLSTMP3,is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs,in which the spatial features are“learned”by convolutional operations while the temporal features are tracked by long short term memory(LSTM).Trained by a reanalysis dataset of the South China Sea(SCS),ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer.Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4%averaged over a 15-d prediction period.In particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model.Given the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction,and could be an alternative way in the operational prediction for ocean environments in the future.

In-situ 3D contour measurement for laser powder bed fusion based on phase guidance

In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.

Characterization methods for additive manufacturing

Additive manufacturing(AM),also generally known as 3D print-ing,is one of the most disruptive technologies that has been widely used in automobile,aerospace,biomedical,weapons,and other industrial fields.Compared with traditional manufacturing technologies,AM has many significant advantages,including rapid production,easy operation,less material waste and labor costs,high efficiency,and is easy to realize personalized design and fab-rication especially for complex components.

Remediation of Steel Slag on Acidic Soil Contaminated by Heavy Metal

The technology of in situ immobilization with amendments is an important measure that remediates the soil contaminated by heavy metals, and selecting economical and effective amendments is the key. The effects and mechanism of steel slag, the silicon-rich alkaline byproduct which can remediate acidic soil contaminated by heavy metal, are mainly introduced in this paper to provide theory reference for future research. Firstly, the paper analyzes current research situation of in situ immobilization with amendments. Then, it introduces the main physicochemical properties of steel slag, and the effect on soil pH value as well as heavy metal activity. Besides, the paper elaborates the promoting effect on silicon-requiring plant and the strengthening mechanism for its resistant capability of heavy metal. According to the analysis, the application of steel slag could be a potential valuable strategy to remediate acidic soil contaminated by heavy metal by modifying the transformation of heavy metals in both soil and plant, so that the translocation of heavy metal in food chain is reduced.

Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

With the rapid development of the Internet,a large number of private protocols emerge on the network.However,some of them are constructed by attackers to avoid being analyzed,posing a threat to computer network security.The blockchain uses the P2P protocol to implement various functions across the network.Furthermore,the P2P protocol format of blockchain may differ from the standard format specification,which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them.Therefore,the ability to distinguish different types of unknown network protocols is vital for network security.In this paper,we propose an unsupervised clustering algorithm based on maximum frequent sequences for binary protocols,which can distinguish various unknown protocols to provide support for analyzing unknown protocol formats.We mine the maximum frequent sequences of protocolmessage sets in bytes.Andwe calculate the fuzzymembership of the protocolmessage to each maximum frequent sequence,which is based on fuzzy set theory.Then we construct the fuzzy membership vector for each protocol message.Finally,we adopt K-means++to split different types of protocol messages into several clusters and evaluate the performance by calculating homogeneity,integrity,and Fowlkes and Mallows Index(FMI).Besides,the clustering algorithms based onNeedleman–Wunsch and the fixed-length prefix are compared with the algorithm presented in this paper.Compared with these traditional clustering methods,we demonstrate a certain improvement in the clustering performance of our work.

A Key Management Scheme for ZigBee in AMI of Smart Grid

Advanced metering infrastructure( AMI) is a critical part of the smart grid,and ZigBee is playing an increasingly important role in AMI.The cyber security is the prerequisite to ensure the reliable operation of AMI.To guarantee the ZigBee communication security in AMI,a key management scheme based on the elliptic curve cryptosystem( ECC) is proposed.According to the ways of information transformation in AMI,the scheme is categorized as unicast communication key management process and multicast communication key management process.And in the scheme,the selection of elliptic curve,the calculation of ZigBee node's ECC public key and private key,the establishment and distribution of the link key in unicast communication,and the establishment and distribution of the network key in multicast communication are elaborated.The analysis results show that the proposed key management scheme is secure,and consumes less memory and energy,thus,can meet the demands of communication security of AMI.

Fine-tune of intrinsic ERK activity by extrinsic BMP signaling in mouse embryonic stem cells

Embryonic stem(ES)cells hold great promise in regen-erative medicine and it is an urgent task to understand the underlying molecular mechanisms that control ES cell fate choice between self-renewal and differentiation.In mouse ES cells,extrinsic leukemia inhibitory factor(LIF)and bone morphogenetic protein(BMP)signaling pathways play pivotal roles in maintaining the self-renewal status under serum and feeder free culture conditions.Intrinsic extracellularsignal regulated kinase(ERK)activity is also important in determining mouse ES cell fate-low ERK activity keeps mouse ES cell self-renewal while high ERK activity drives differentiation.

Genome-wide Analysis of Smad Targets Reveals the Role of BMP Signaling in Embryonic Stem Cell Fate Determination

Embryonic stem (ES) cells are under precise control of both intrinsic self-renewal gene regulatory network and extrinsic growth factor-triggered signaling cascades.