Noncanonical Amino Acids in the Interrogation of Cellular Protein Synthesis

Proteins in living cells can be made receptive to bioorthogonal chemistries through metabolic labeling with appropriately designed noncanonical amino acids (ncAAs). In the simplest approach to metabolic labeling, an amino acid analog replaces one of the natural amino acids specified by the protein’s gene (or genes) of interest. Through manipulation of experimental conditions, the extent of the replacement can be adjusted. This approach, often termed residue-specific incorporation, allows the ncAA to be incorporated in controlled proportions into positions normally occupied by the natural amino acid residue. For a protein to be labeled in this way with an ncAA, it must fulfill just two requirements: (i) the corresponding natural amino acid must be encoded within the sequence of the protein at the genetic level, and (ii) the protein must be expressed while the ncAA is in the cell. Because this approach permits labeling of proteins throughout the cell, it has enabled us to develop strategies to track cellular protein synthesis by tagging proteins with reactive ncAAs. In procedures similar to isotopic labeling, translationally active ncAAs are incorporated into proteins during a “pulse” in which newly synthesized proteins are tagged. The set of tagged proteins can be distinguished from those made before the pulse by bioorthogonally ligating the ncAA side chain to probes that permit detection, isolation, and visualization of the labeled proteins. Noncanonical amino acids with side chains containing azide, alkyne, or alkene groups have been especially useful in experiments of this kind. They have been incorporated into proteins in the form of methionine analogs that are substrates for the natural translational machinery. The selectivity of the method can be enhanced through the use of mutant aminoacyl tRNA synthetases (aaRSs) that permit incorporation of ncAAs not used by the endogenous biomachinery. Through expression of mutant aaRSs, proteins can be tagged with other useful ncAAs, including analogs that contain ketones or aryl halides. High-throughput screening strategies can identify aaRS variants that activate a wide range of ncAAs. Controlled expression of mutant synthetases has been combined with ncAA tagging to permit cell-selective metabolic labeling of proteins. Expression of a mutant synthetase in a portion of cells within a complex cellular mixture restricts labeling to that subset of cells. Proteins synthesized in cells not expressing the synthetase are neither labeled nor detected. In multicellular environments, this approach permits the identification of the cellular origins of labeled proteins. In this Account, we summarize the tools and strategies that have been developed for interrogating cellular protein synthesis through residue-specific tagging with ncAAs. We describe the chemical and genetic components of ncAA-tagging strategies and discuss how these methods are being used in chemical biology

Joint Resource Optimization for Multicell Networks with Wireless Energy Harvesting Relays

This paper first considers a multicell network deployment where the base station (BS) of each cell communicates with its cell-edge user with the assistance of an amplify-and-forward (AF) relay node. Equipped with a power splitter and a wireless energy harvester, the self-sustaining relay scavenges radio frequency (RF) energy from the received signals to process and forward the information. Our aim is to develop a resource allocation scheme that jointly optimizes (i) BS transmit powers, (ii) received power splitting factors for energy harvesting and information processing at the relays, and (iii) relay transmit powers. In the face of strong intercell interference and limited radio resources, we formulate three highly-nonconvex problems with the objectives of sum-rate maximization, max-min throughput fairness and sum-power minimization. To solve such challenging problems, we propose to apply the successive convex approximation (SCA) approach and devise iterative algorithms based on geometric programming and difference-of-convex-functions programming. The proposed algorithms transform the nonconvex problems into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our algorithms converge to the locally optimal solutions that satisfy the Karush-Kuhn-Tucker conditions of the original nonconvex problems. We then extend our results to the case of decode-and-forward (DF) relaying with variable timeslot durations. We show that our resource allocation solutions in this case offer better throughput than that of the AF counterpart with equal timeslot durations, albeit at a higher computational complexity. Numerical results confirm that the proposed joint optimization solutions substantially improve the network performance, compared with cases where the radio resource parameters are individually optimized

Simple Combined Model for Nonlinear Excitations in DNA

We propose a new simple model for DNA denaturation bases on the pendulum model of Englander\cite{A1} and the microscopic model of Peyrard {\it et al.},\cite{A3} so called "combined model". The main parameters of our model are: the coupling constant $k$ along each strand, the mean stretching $y^\ast$ of the hydrogen bonds, the ratio of the damping constant and driven force $\gamma/F$. We show that both the length $L$ of unpaired bases and the velocity $v$ of kinks depend on not only the coupling constant $k$ but also the temperature $T$. Our results are in good agreement with previous works.Comment: 6 pages, 10 figures, submitted to Phys. Rev.

Control Of Wind-Induced Motion Of Tall Buildings Using Smart Facade Systems

The development of non-load bearing curtain walling technology around the turn of the 20th centre along with an effort to reduce the energy consumption of the building and dependence on artificial lightening, the development of high performance glass and efficient building system has seen architectural trends move toward maximising glass surface areas in order to optimise natural light. This presents an opportunity to also investigate the façade system potential to become a filter for wind-induced vibration. The façade has been rarely considered or designed as a potential windinduced vibration absorber for tall buildings. In this paper the potential of utilizing a moveable exterior façade in a double-skin façade system is investigated and shown that with optimal choices of materials for stiffness and damping of brackets connecting the two skins, a substantial portion of wind-induced vibration energy can be dissipated which leads to avoiding expensive lateral stiffening systems and/or space consuming large damper systems such as tuned mass or liquid dampers. The works have demonstrated that up to 50% of response caused by winds can be absorbed by a smart and efficient façade design, including purely passive systems with constant stiffness and damping or better, by a smart system possessing variable stiffness for different phases of façade movement

Control of wind-induced motion of tall buildings using smart façade systems

The development of non-load bearing curtain walling technology around the turn of the 20th centre along with the effects to reduce the energy consumption of the building and dependence on artificial lightening, as well as the development of high performance glass and efficient building systems has seen ar-chitectural trends to move toward maximising glass surface areas in order to optimise natural daylight. This present study shows the potential offaçade systems potential to become an energy absorber of wind-induced vibrations. The façade has been rarely considered or designed as a potential wind-induced vibration absorber for tall buildings in the past. In this paper the potential of utilizing a moveable exterior façade in a double-skin façade is investigated and shown that with optimal choices of materials for stiffness and damping of brackets connecting the two skins, a substantial portion of wind-induced vibration energy can be dissipated which leads to avoiding expensive lateral stiffening systems and/or space consuming large damper systems such as tuned mass or liquid dampers. The work has demonstrated that up to 50% of response caused by winds can be absorbed by a smart and efficient façade design, including purely passive systems with constant stiffness and damping or better, by a smart a system possessing variable stiffness for different phases of façade move-ment

Frustration Effects in Antiferromagnetic FCC Heisenberg Films

We study the effects of frustration in an antiferromagnetic film of FCC lattice with Heisenberg spin model including an Ising-like anisotropy. Monte Carlo (MC) simulations have been used to study thermodynamic properties of the film. We show that the presence of the surface reduces the ground state (GS) degeneracy found in the bulk. The GS is shown to depend on the surface in-plane interaction $J_s$ with a critical value at which ordering of type I coexists with ordering of type II. Near this value a reentrant phase is found. Various physical quantities such as layer magnetizations and layer susceptibilities are shown and discussed. The nature of the phase transition is also studied by histogram technique. We have also used the Green's function (GF) method for the quantum counterpart model. The results at low-$T$ show interesting effects of quantum fluctuations. Results obtained by the GF method at high $T$ are compared to those of MC simulations. A good agreement is observed.Comment: 11 pages, 19 figures, submitted to J. Phys.: Condensed Matte

A deep level set method for image segmentation

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone