Complex Function by Design Using Spatially Pre-Structured Synthetic Microbial Communities: Degradation of Pentachlorophenol in the Presence of Hg(II)

Naturally occurring microbes perform a variety of useful functions, with more complex processes requiring multiple functions performed by communities of multiple microbes. Synthetic biology via genetic engineering may be used to achieve desired multiple functions, e.g. multistep chemical and biological transformations, by adding genes to a single organism, but this is sometimes not possible due to incompatible metabolic requirements or not desirable in certain applications, especially in medical or environmental applications. Achieving multiple functions by mixing microbes that have not evolved to function together may not work due to competition of microbes, or lack of interactions among microbes. In nature, microbial communities are commonly spatially structured. Here, we tested whether spatial structure can be used to create a community of microbes that can perform a function they do not perform individually or when simply mixed. We constructed a core–shell fiber with Sphingobium chlorophenolicum, a pentachlorophenol (PCP) degrader, in the core layer and Ralstonia metallidurans, a mercuric ion (Hg(II)) reducer, in the shell layer as a structured community using microfluidic laminar flow techniques. We applied a mixture of PCP and Hg(II) to either a simple well-mixed culture broth (i.e. the unstructured community) or the spatially structured core–shell fibers. We found that without spatial structure, the community was unable to degrade PCP in the presence of Hg(II) because S. chlorophenolicum is sensitive to Hg(II). In contrast, with spatial structure in a core–shell fiber system, S. chlorophenolicum in a core layer was protected by R. metallidurans deposited in a shell layer, and the community was able to completely remove both PCP and Hg(II) from a mixture. The appropriate size of the core–shell fiber was determined by the Damköhler number—the timescale of removal of Hg(II) was on the same order of the timescale of diffusion of Hg(II) through the outer layer when the shell layer was on the order of B200 mm. Ultimately, with the ease of a child putting together ‘Legos’ to build a complex structure, using this approach one may be able to put together microorganisms to build communities that perform functions in vitro or even in vivo, e.g. as in a ‘‘microbiome on a pill’’

The pattern of foreign property investment in Vietnam: the apartment market in Ho Chi Minh City

As globalization proceeds, transnational property development is increasing. Especially in emerging markets, foreign developers’ influence in changing the local landscape is becoming significant. In this research, the behavioral patterns of foreign developers in the apartment market of Ho Chi Minh City, Vietnam were identified. To understand the dynamics of foreign developers, the types of products that were being created, where the investments were located, and the differences in development strategies adopted by foreign developers in comparison to domestic counterparts were identified. To accomplish this, data on apartment projects and statistics were collected, and a series of spatial analyses including sieve mapping, histogram analysis, factor analysis and logistic regression was conducted. In addition, closer examination was made of specific cases to understand the dynamics among foreign and domestic developers, also allowing the identification of some regularities in the patterns of foreign developments. Besides presenting detailed results, this paper also seeks to account for the conditions that appear to have generated these patterns and characteristics

Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation

Uncertainty estimation is an important research area to make deep neural networks (DNNs) more trustworthy. While extensive research on uncertainty estimation has been conducted with unimodal data, uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for unimodal data by providing the reliability of Gaussian processes with efficient and powerful DNNs. While NPs hold significant potential for multimodal uncertainty estimation, the adaptation of NPs for multimodal data has not been carefully studied. To bridge this gap, we propose Multimodal Neural Processes (MNPs) by generalising NPs for multimodal uncertainty estimation. Based on the framework of NPs, MNPs consist of several novel and principled mechanisms tailored to the characteristics of multimodal data. In extensive empirical evaluation, our method achieves state-of-the-art multimodal uncertainty estimation performance, showing its appealing robustness against noisy samples and reliability in out-of-distribution detection with faster computation time compared to the current state-of-the-art multimodal uncertainty estimation method.Comment: Accepted to NeurIPS 202

Modulation of Field Emission Resonance on photodetachment of negative ions on surface

The interaction between the field emission resonance states and the photodetached electron in an electric field is studied by semiclassical theory. An analytical expression of the photodetachment cross section is derived in the framework. It is found that the Stark shifted image state modulates the photodetachment cross section by adding irregular staircase or smooth oscillation in the spectrum. When the photodetached electron is trapped in Stark shifted image potential well, the detachment spectrum displays an irregular staircase structure which corresponds to the modified Rydberg series. While the photodetached electron is not bound by the surface potential well, the cross secton contains only a smooth oscillation due to the reflection of electronic wave by the field or the surface.Comment: 30 pages, 6 figure

Advances in 3D Bioprinting for Cancer Biology and Precision Medicine: From Matrix Design to Application

The tumor microenvironment is highly complex owing to its heterogeneous composition and dynamic nature. This makes tumors difficult to replicate using traditional 2D cell culture models that are frequently used for studying tumor biology and drug screening. This often leads to poor translation of results between in vitro and in vivo and is reflected in the extremely low success rates of new candidate drugs delivered to the clinic. Therefore, there has been intense interest in developing 3D tumor models in the laboratory that are representative of the in vivo tumor microenvironment and patient samples. 3D bioprinting is an emerging technology that enables the biofabrication of structures with the virtue of providing accurate control over distribution of cells, biological molecules, and matrix scaffolding. This technology has the potential to bridge the gap between in vitro and in vivo by closely recapitulating the tumor microenvironment. Here, a brief overview of the tumor microenvironment is provided and key considerations in biofabrication of tumor models are discussed. Bioprinting techniques and choice of bioinks for both natural and synthetic polymers are also outlined. Lastly, current bioprinted tumor models are reviewed and the perspectives of how clinical applications can greatly benefit from 3D bioprinting technologies are offered

Design and Mechanical Compatibility of Nylon Bionic Cancellous Bone Fabricated by Selective Laser Sintering

In order to avoid the stress shielding phenomenon in orthopedic bionic bone implantation, it is necessary to consider the design of mechanical compatible implants imitating the host bone. In this study, we developed a novel cancellous bone structure design method aimed at ensuring the mechanical compatibility between the bionic bone and human bone by means of computer-aided design (CAD) and finite element analysis technology (specifically, finite element modeling (FEM)). An orthogonal lattice model with volume porosity between 59% and 96% was developed by means of CAD. The effective equivalent elastic modulus of a honeycomb structure with square holes was studied by FEM simulation. With the purpose of verifying the validity of the cancellous bone structure design method, the honeycomb structure was fabricated by selective laser sintering (SLS) and the actual equivalent elastic modulus of the honeycomb structure was measured with a uniaxial compression test. The experimental results were compared with the FEM values and the predicted values. The results showed that the stiffness values of the designed structures were within the acceptable range of human cancellous bone of 50-500 MPa, which was similar to the stiffness values of human vertebrae L1 and L5. From the point of view of mechanical strength, the established cellular model can effectively match the elastic modulus of human vertebrae cancellous bone. The functional relationship between the volume porosity of the nylon square-pore honeycomb structure ranging from 59% to 96% and the effective elastic modulus was established. The effect of structural changes related to the manufacture of honeycomb structures on the equivalent elastic modulus of honeycomb structures was studied quantitatively by finite element modeling