Share Beijing Stories in the Context of Media Convergence

International Exchange Center is one of the “four centers” of Beijing’s strategic positioning. Accompanied by the continuous progress and development of media, Beijing’s city image is subject to increasingly frequent exchanges among cities of different countries, and the requirements for external communication are also increasing. How to effectively promote Beijing’s external communication in the cross-cultural context and share a good “Beijing city story” of the new era? One of the most important ways is to effectively shape Beijing’s city image through the media, to improve Beijing’s influence through the media, to adjust the way of Beijing’s international communication in a timely manner, and to create communication contents that are closer to the audience’s psychology, so as to achieve a better effect of the external communication of Beijing’s city image

Anisotropic scaffolds for peripheral nerve and spinal cord regeneration

The treatment of long-gap (\u3e10 mm) peripheral nerve injury (PNI) and spinal cord injury (SCI) remains a continuous challenge due to limited native tissue regeneration capabilities. The current clinical strategy of using autografts for PNI suffers from a source shortage, while the pharmacological treatment for SCI presents dissatisfactory results. Tissue engineering, as an alternative, is a promising approach for regenerating peripheral nerves and spinal cords. Through providing a beneficial environment, a scaffold is the primary element in tissue engineering. In particular, scaffolds with anisotropic structures resembling the native extracellular matrix (ECM) can effectively guide neural outgrowth and reconnection. In this review, the anatomy of peripheral nerves and spinal cords, as well as current clinical treatments for PNI and SCI, is first summarized. An overview of the critical components in peripheral nerve and spinal cord tissue engineering and the current status of regeneration approaches are also discussed. Recent advances in the fabrication of anisotropic surface patterns, aligned fibrous substrates, and 3D hydrogel scaffolds, as well as their in vitro and in vivo effects are highlighted. Finally, we summarize potential mechanisms underlying the anisotropic architectures in orienting axonal and glial cell growth, along with their challenges and prospects

Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.Comment: Accepted by lEEE Transactions on lmage Processing 2023. Project Page: https://zparquet.github.io/Fast-Learning . Code: https://github.com/zParquet/Fast-Learnin

Runx2 transcriptome of prostate cancer cells: insights into invasiveness and bone metastasis

<p>Abstract</p> <p>Background</p> <p>Prostate cancer (PCa) cells preferentially metastasize to bone at least in part by acquiring osteomimetic properties. Runx2, an osteoblast master transcription factor, is aberrantly expressed in PCa cells, and promotes their metastatic phenotype. The transcriptional programs regulated by Runx2 have been extensively studied during osteoblastogenesis, where it activates or represses target genes in a context-dependent manner. However, little is known about the gene regulatory networks influenced by Runx2 in PCa cells. We therefore investigated genome wide mRNA expression changes in PCa cells in response to Runx2.</p> <p>Results</p> <p>We engineered a C4-2B PCa sub-line called C4-2B/Rx2^dox, in which Doxycycline (Dox) treatment stimulates Runx2 expression from very low to levels observed in other PCa cells. Transcriptome profiling using whole genome expression array followed by <it>in silico </it>analysis indicated that Runx2 upregulated a multitude of genes with prominent cancer associated functions. They included secreted factors (CSF2, SDF-1), proteolytic enzymes (MMP9, CST7), cytoskeleton modulators (SDC2, Twinfilin, SH3PXD2A), intracellular signaling molecules (DUSP1, SPHK1, RASD1) and transcription factors (Sox9, SNAI2, SMAD3) functioning in epithelium to mesenchyme transition (EMT), tissue invasion, as well as homing and attachment to bone. Consistent with the gene expression data, induction of Runx2 in C4-2B cells enhanced their invasiveness. It also promoted cellular quiescence by blocking the G1/S phase transition during cell cycle progression. Furthermore, the cell cycle block was reversed as Runx2 levels declined after Dox withdrawal.</p> <p>Conclusions</p> <p>The effects of Runx2 in C4-2B/Rx2^doxcells, as well as similar observations made by employing LNCaP, 22RV1 and PC3 cells, highlight multiple mechanisms by which Runx2 promotes the metastatic phenotype of PCa cells, including tissue invasion, homing to bone and induction of high bone turnover. Runx2 is therefore an attractive target for the development of novel diagnostic, prognostic and therapeutic approaches to PCa management. Targeting Runx2 may prove more effective than focusing on its individual downstream genes and pathways.</p

Designing Artificial Two-Dimensional Landscapes via Room-Temperature Atomic-Layer Substitution

Manipulating materials with atomic-scale precision is essential for the development of next-generation material design toolbox. Tremendous efforts have been made to advance the compositional, structural, and spatial accuracy of material deposition and patterning. The family of 2D materials provides an ideal platform to realize atomic-level material architectures. The wide and rich physics of these materials have led to fabrication of heterostructures, superlattices, and twisted structures with breakthrough discoveries and applications. Here, we report a novel atomic-scale material design tool that selectively breaks and forms chemical bonds of 2D materials at room temperature, called atomic-layer substitution (ALS), through which we can substitute the top layer chalcogen atoms within the 3-atom-thick transition-metal dichalcogenides using arbitrary patterns. Flipping the layer via transfer allows us to perform the same procedure on the other side, yielding programmable in-plane multi-heterostructures with different out-of-plane crystal symmetry and electric polarization. First-principle calculations elucidate how the ALS process is overall exothermic in energy and only has a small reaction barrier, facilitating the reaction to occur at room temperature. Optical characterizations confirm the fidelity of this design approach, while TEM shows the direct evidence of Janus structure and suggests the atomic transition at the interface of designed heterostructure. Finally, transport and Kelvin probe measurements on MoXY (X,Y=S,Se; X and Y corresponding to the bottom and top layers) lateral multi-heterostructures reveal the surface potential and dipole orientation of each region, and the barrier height between them. Our approach for designing artificial 2D landscape down to a single layer of atoms can lead to unique electronic, photonic and mechanical properties previously not found in nature

Worldwide tests of generic attractants, a promising tool for early detection of non-native cerambycid species

A large proportion of the insects which have invaded new regions and countries are emerging species, being found for the first time outside their native range. Being able to detect such species upon arrival at ports of entry before they establish in non-native countries is an urgent challenge. The deployment of traps baited with broad-spectrum semiochemical lures at ports-of-entry and other high-risk sites could be one such early detection tool. Rapid progress in the identification of semiochemicals for cerambycid beetles during the last 15 years has revealed that aggregation-sex pheromones and sex pheromones are often conserved at global levels for genera, tribes or subfamilies of the Cerambycidae. This possibly allows the development of generic attractants which attract multiple species simultaneously, especially when such pheromones are combined into blends. Here, we present the results of a worldwide field trial programme conducted during 2018-2021, using traps baited with a standardised 8-pheromone blend, usually com-plemented with plant volatiles. A total of 1308 traps were deployed at 302 sites covering simultaneously or sequentially 13 European countries, 10 Chinese provinces and some regions of the USA, Canada, Australia, Russia (Siberia) and the Caribbean (Martinique). We intended to test the following hypotheses: 1) if a species is regularly trapped in significant numbers by the blend on a continent, it increases the prob-ability that it can be detected when it arrives in other countries/continents and 2) if the blend exerts an effective, generic attraction to multiple species, it is likely that previously unknown and unexpected spe-cies can be captured due to the high degree of conservation of pheromone structures within related taxa. A total of 78,321 longhorned beetles were trapped, representing 376 species from eight subfamilies, with 84 species captured in numbers greater than 50 individuals. Captures comprised 60 tribes, with 10 tribes including more than nine species trapped on different continents. Some invasive species were captured in both the native and invaded continents. This demonstrates the potential of multipheromone lures as ef-fective tools for the detection of 'unexpected' cerambycid invaders, accidentally translocated outside their native ranges. Adding new pheromones with analogous well-conserved motifs is discussed, as well as the limitations of using such blends, especially for some cerambycid taxa which may be more attracted by the trap colour or other characteristics rather than to the chemical blend

The Secret of Love in Speed Dating

Speed dating is a popular and fast way to meet new people and find life partner in nowadays society. Four professors from Columbia university did the speed dating experiment from 2002-2004 and I use their data-set in this paper to answer the research question: what are the gender differences on selecting opposite sex partner from speed dating event, and can we eventually predict people’s decisions. In this speed dating experiment, every participant had a chance to meet a new person from opposite sex just through a 4-minute conversation. They collected everyone’s basic information such as gender, race, age and so on. Before the speed dating event they also collected participants’ hobbies, expectation about opposite sex, and what kind of person themselves are. During the event, each participants would also value how they think their partner is. In this paper, I did basic data analysis to explore gender difference and other useful information about different people’s preference on opposite sex. I try to predict males’ final decision, if they like the female who they just met, using either all information I have before or after the speed dating event. The base model I use is logistic regression model, and I improved the model by step-wise variable selection. The compared models are decision tree model, random forest model and XGBoost model. I separated the whole data set into 80% training data and 20% testing data to avoid over-fitting. The best model I finally have is XGBoost model. It has a 82.4% precision on testing data-set based on all the information we have after the speed dating event, and still a 70.2% precision on testing data-set even we only use all information before two people never actually met on the speed dating event. So we can believe that we have the ability to discover the secret of love with modern machine learning algorithms if we have enough information

The Secret of Love in Speed Dating

Speed dating is a popular and fast way to meet new people and find life partner in nowadays society. Four professors from Columbia university did the speed dating experiment from 2002-2004 and I use their data-set in this paper to answer the research question: what are the gender differences on selecting opposite sex partner from speed dating event, and can we eventually predict people’s decisions. In this speed dating experiment, every participant had a chance to meet a new person from opposite sex just through a 4-minute conversation. They collected everyone’s basic information such as gender, race, age and so on. Before the speed dating event they also collected participants’ hobbies, expectation about opposite sex, and what kind of person themselves are. During the event, each participants would also value how they think their partner is. In this paper, I did basic data analysis to explore gender difference and other useful information about different people’s preference on opposite sex. I try to predict males’ final decision, if they like the female who they just met, using either all information I have before or after the speed dating event. The base model I use is logistic regression model, and I improved the model by step-wise variable selection. The compared models are decision tree model, random forest model and XGBoost model. I separated the whole data set into 80% training data and 20% testing data to avoid over-fitting. The best model I finally have is XGBoost model. It has a 82.4% precision on testing data-set based on all the information we have after the speed dating event, and still a 70.2% precision on testing data-set even we only use all information before two people never actually met on the speed dating event. So we can believe that we have the ability to discover the secret of love with modern machine learning algorithms if we have enough information