When does group norm or group identity predict cooperation in a public goods dilemma? The moderating effects of idiocentrism and allocentrism

In this study we examined how perceived group norm and group identity influence individual cooperative behavior in a public goods dilemma across cultural settings. Six hundred and eight students in the United States and People's Republic of China participated in a laboratory experiment in which group norm and group identity were manipulated and the individual cultural orientations of idiocentrism and allocentrism were measured. We found that idiocentrism and allocentrism moderated the relationship between perceived group norm and cooperation but not between group identity and cooperation. In particular, members who endorsed allocentrism to a greater extent cooperated more when they perceived a more cooperative group norm than did members who endorsed lower levels of allocentrism. On the other hand, people scored high on idiocentrism cooperated less when perceiving a more cooperative norm than did people scored low on idiocentrism. The results suggest that allocentrics are not cooperative in every context but are rather highly sensitive to social cues whereas idiocentrics, while tending to behave in a way that maximizes personal outcomes at the expense of the group, are also somewhat aloof to the situation

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Antennas for 20/30 GHz and beyond

Antennas of 20/30 GHz and higher frequency, due to the small wavelength, offer capabilities for many space applications. With the government-sponsored space programs (such as ACTS) in recent years, the industry has gone through the learning curve of designing and developing high-performance, multi-function antennas in this frequency range. Design and analysis tools (such as the computer modelling used in feedhorn design and reflector surface and thermal distortion analysis) are available. The components/devices (such as BFN's, weight modules, feedhorns and etc.) are space-qualified. The manufacturing procedures (such as reflector surface control) are refined to meet the stringent tolerance accompanying high frequencies. The integration and testing facilities (such as Near-Field range) also advance to facilitate precision assembling and performance verification. These capabilities, essential to the successful design and development of high-frequency spaceborne antennas, shall find more space applications (such as ESGP) than just communications

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Three-dimensional single gyroid photonic crystals with a mid-infrared bandgap

A gyroid structure is a distinct morphology that is triply periodic and consists of minimal isosurfaces containing no straight lines. We have designed and synthesized amorphous silicon (a-Si) mid-infrared gyroid photonic crystals that exhibit a complete bandgap in infrared spectroscopy measurements. Photonic crystals were synthesized by deposition of a-Si/Al2O3 coatings onto a sacrificial polymer scaffold defined by two-photon lithography. We observed a 100% reflectance at 7.5 \mum for single gyroids with a unit cell size of 4.5 \mum, in agreement with the photonic bandgap position predicted from full-wave electromagnetic simulations, whereas the observed reflection peak shifted to 8 um for a 5.5 \mum unit cell size. This approach represents a simulation-fabrication-characterization platform to realize three-dimensional gyroid photonic crystals with well-defined dimensions in real space and tailored properties in momentum space

Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes

We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare these results with conventional derivative-free global search algorithms, such as (single-fidelity) Gaussian Processes optimization scheme, and Particle Swarm Optimization---a commonly used method in nanophotonics community, which is implemented in Lumerical commercial photonics software. We demonstrate the performance of various numerical optimization approaches on several pre-collected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget.Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials. arXiv admin note: substantial text overlap with arXiv:1811.0075

Synthesis of Nonspherical Microcapsules through Controlled Polyelectrolyte Coating of Hydrogel Templates

We report a simple approach to fabricate custom-shape microcapsules using hydrogel templates synthesized by stop flow lithography. Cargo-containing microcapsules were made by coating hydrogel particles with a single layer of poly-l-lysine followed by a one-step core degradation and capsule cross-linking procedure. We determined appropriate coating conditions by investigating the effect of pH, ionic strength, and prepolymer composition on the diffusion of polyelectrolytes into the oppositely charged hydrogel template. We also characterized the degradation of the templating core by tracking the diffusivity of nanoparticles embedded within the hydrogel. Unlike any other technique, this approach allows for easy fabrication of microcapsules with internal features (e.g., toroids) and selective surface modification of Janus particles using any polyelectrolyte. These soft, flexible capsules may be useful for therapeutic applications as well as fundamental studies of membrane mechanics.United States. Army Research Office (Institute for Collaborative Biotechnologies. Grant W911NF-09-0001)National Science Foundation (U.S.) (Grants CMMI-1120724 and DMR-1006147