The Transgenic RNAi Project (TRiP), a Drosophila melanogaster functional genomics platform at Harvard Medical School, was initiated in 2008 to generate and distribute a genome-scale collection of RNA interference (RNAi) fly stocks. To date, it has generated >15,000 RNAi fly stocks. As this covers most Drosophila genes, we have largely transitioned to development of new resources based on CRISPR technology. Here, we present an update on our libraries of publicly available RNAi and CRISPR fly stocks, and focus on the TRiP-CRISPR overexpression (TRiP-OE) and TRiP-CRISPR knockout (TRiP-KO) collections. TRiP-OE stocks express single guide RNAs targeting upstream of a gene transcription start site. Gene activation is triggered by coexpression of catalytically dead Cas9 fused to an activator domain, either VP64-p65-Rta or Synergistic Activation Mediator. TRiP-KO stocks express one or two single guide RNAs targeting the coding sequence of a gene or genes. Cutting is triggered by coexpression of Cas9, allowing for generation of indels in both germline and somatic tissue. To date, we have generated >5000 TRiP-OE or TRiP-KO stocks for the community. These resources provide versatile, transformative tools for gene activation, gene repression, and genome engineering.

Being relatively simple and practical, Drosophila transgenic RNAi is the technique of top priority choice to quickly study genes with pleiotropic functions. However, drawbacks have emerged over time, such as high level of false positive and negative results. To overcome these shortcomings and increase efficiency, specificity and versatility, we develop a next generation transgenic RNAi system. With this system, the leaky expression of the basal promoter is significantly reduced, as well as the heterozygous ratio of transgenic RNAi flies. In addition, it has been first achieved to precisely and efficiently modulate highly expressed genes. Furthermore, we increase versatility which can simultaneously knock down multiple genes in one step. A case illustration is provided of how this system can be used to study the synthetic developmental effect of histone acetyltransferases. Finally, we have generated a collection of transgenic RNAi lines for those genes that are highly homologous to human disease genes.

No abstract

No abstract

The Transgenic RNAi Project (TRiP), a Drosophila functional genomics platform at Harvard Medical School, was initiated in 2008 to generate and distribute a genome-scale collection of RNAi fly stocks. To date, the TRiP has generated >15,000 RNAi fly stocks. As this covers most Drosophila genes, we have largely transitioned to development of new resources based on CRISPR technology. Here, we present an update on our libraries of publicly available RNAi and CRISPR fly stocks, and focus on the TRiP-CRISPR overexpression (TRiP-OE) and TRiP-CRISPR knockout (TRiP-KO) collections. TRiP-OE stocks express sgRNAs targeting upstream of a gene transcription start site. Gene activation is triggered by co-expression of catalytically dead Cas9 (dCas9) fused to an activator domain, either VP64-p65-Rta (VPR) or Synergistic Activation Mediator (SAM). TRiP-KO stocks express one or two sgRNAs targeting the coding sequence of a gene or genes, allowing for generation of indels in both germline and somatic tissue. To date, we have generated more than 5,000 CRISPR-OE or -KO stocks for the community. These resources provide versatile, transformative tools for gene activation, gene repression, and genome engineering.

A site-specific modification of aldehyde-containing proteins using a tandem Knoevenagel-intra Michael addition reaction was developed. The reaction featured fast kinetics (50 M s) and favorable stoichiometry. Various functionalities could be introduced into the protein with little impact on its function and conformation. The reaction was successfully applied in the labeling of living cells.

Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world’s largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. To the best of our knowledge, such a network structure has not been found in other railway systems, or in transportation systems in general. Follow-up studies are needed to reveal the formation mechanisms of this hierarchical network structure.

Vehicle collision avoidance system (CAS) is a control system that can guide the vehicle into a collision-free safe region in the presence of other objects on road. Common CAS functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, these CASs focus on mitigating or avoiding potential crashes with the preceding cars and objects. They are not effective fo1• crash scenalios with vehicles from the rear-end 01• lateral directions. This paper proposes a novel collision avoidance system that will provide the vehicle with all around (360-degree) collision avoidance capability. A lisk evaluation model is developed to calculate potential lisk levels by considering sunounding vehicles (according to their relative positions, velocities, and accelerations) and using a predictive occupancy map (POM). By using the POM, the safest path with the minimum lisk values is chosen from 12 acceleration based trajecto1•y directions. The global optimal trajectory is then planned using the optimal rapidly explo1•ing random tree (RRT*) algolithm. The planned vehicle motion profile is generated as the reference for future control. Simulation results show that the developed POM-based CAS demonstrates effective operations to mitigate the potential crashes in both lateral and rear-end crash scenalios. I. IN1RODUCTIONAutonomous vehicles (AVs) have become a popular research area in both the automotive industty and academia with the objective of minimizing risks and enhancing safety and comfort. Based on the National Highway Traffic Safety Administt•ation (NHTSA), smashing into the rear of the car ahead is the top cause of vehicle accidents, contt-ibuting up around 30% of all tt•affic accidents annually [I]. Collision avoidance system (CAS) is one of vehicle active safety technologies for dealing with both lane-departure and fo1ward-collision problems, which has been designed and implemented on some production vehicles. D. Sam [2] concluded that most road accidents occured due to human error, and over 90% of those accidents were caused by visual information acquisition problems. However, most of the currently developed CASs were designed to mitigate crashes based on the e1rnrs caused by the ego vehicle (e.g., driver distt•action and drowsiness [3]) and static objects on road, such as lane markings, road edges, and parked