Topological defect lasers

We demonstrate topological defect lasers in a GaAs membrane with embedded InAs quantum dots. By introducing a disclination to a square-lattice of elliptical air holes, we obtain spatially confined optical resonances with high quality factor. Such resonances support powerflow vortices, and lase upon optical excitation of quantum dots, embedded in the structure. The spatially inhomogeneous variation of the unit cell orientation adds another dimension to the control of a lasing mode, enabling the manipulation of its field pattern and energy flow landscape

Photonic Band Gap Effects in Two-dimensional Polycrystalline and Amorphous Structures

We study numerically the density of optical states (DOS) in two-dimensional photonic structures with short-range positional order, and observe a clear transition from polycrystalline to amorphous photonic systems. In polycrystals, photonic band gaps (PBGs) are formed within individual do- mains, which leads to a depletion of the DOS similar to that in periodic structures. In amorphous photonic media, the domain sizes are too small to form PBGs, thus the depletion of the DOS is weakened significantly. The critical domain size that separates the polycrystalline and amorphous regimes is determined by the attenuation length of Bragg scattering, which depends not only on the degree of positional order but also the refractive index contrast of the photonic material. Even with relatively low refractive index contrast, we find that modest short-range positional order in photonic structures enhances light confinement via collective scattering and interference.Comment: 20 pages, 10 figure

Investigation of the Dimensional Variation of Microstructures Through the μMIM Process

The mass production of components with dimensions in the micron and sub-micron range is anticipated to be one of the leading technology areas for the present century and to be of high market potential. Micro metal injection molding (μMIM) has the potential to be an important contributor to this industry as it can produce precise metallic microstructures in large quantities at a relatively low production cost. The μMIM process is a miniaturization of metal injection molding (MIM) methods. The process comprises of four main steps: mixing, injection molding, debinding and sintering. A metallic powder is mixed with a binder system to form the feedstock. The feedstock is then injection molded into the required shape and the binder removed via thermal or other means. The final microstructures are obtained by sintering the remaining powder in a controlled environment. In this work, the dimensional variation of the microstructures, in particular the warpage, roughness and volume variation, at each stage of the μMIM process was quantified and compared. The results of a preliminary study of the sensitivity of warpage of the microstructures to the packing pressure are also reported.Singapore-MIT Alliance (SMA

The role of input noise in transcriptional regulation

Even under constant external conditions, the expression levels of genes fluctuate. Much emphasis has been placed on the components of this noise that are due to randomness in transcription and translation; here we analyze the role of noise associated with the inputs to transcriptional regulation, the random arrival and binding of transcription factors to their target sites along the genome. This noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, global models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally.Comment: 11 pages, 5 figures minor correction

Distributed flow optimization and cascading effects in weighted complex networks

We investigate the effect of a specific edge weighting scheme $\sim (k_i k_j)^{\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficiency upon the correlations between the topology and weights. By studying the severity of cascades for different control parameter $\beta$, we find that network resilience to cascading overloads and network throughput is optimal for the same value of $\beta$ over the range of node capacities and available bandwidth

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

The integration of InGaP LEDs with CMOS on 200 mm silicon wafers

The integration of photonics and electronics on a converged silicon CMOS platform is a long pursuit goal for both academe and industry. We have been developing technologies that can integrate III-V compound semiconductors and CMOS circuits on 200 mm silicon wafers. As an example we present our work on the integration of InGaP light-emitting diodes (LEDs) with CMOS. The InGaP LEDs were epitaxially grown on high-quality GaAs and Ge buffers on 200 mm (100) silicon wafers in a MOCVD reactor. Strain engineering was applied to control the wafer bow that is induced by the mismatch of coefficients of thermal expansion between III-V films and silicon substrate. Wafer bonding was used to transfer the foundry-made silicon CMOS wafers to the InGaP LED wafers. Process trenches were opened on the CMOS layer to expose the underneath III-V device layers for LED processing. We show the issues encountered in the 200 mm processing and the methods we have been developing to overcome the problems

A novel PKC activating molecule promotes neuroblast differentiation and delivery of newborn neurons in brain injuries

Neural stem cells are activated within neurogenic niches in response to brain injuries. This results in the production of neuroblasts, which unsuccessfully attempt to migrate toward the damaged tissue. Injuries constitute a gliogenic/non-neurogenic niche generated by the presence of anti-neurogenic signals, which impair neuronal differentiation and migration. Kinases of the protein kinase C (PKC) family mediate the release of growth factors that participate in different steps of the neurogenic process, particularly, novel PKC isozymes facilitate the release of the neurogenic growth factor neuregulin. We have demonstrated herein that a plant derived diterpene, (EOF2; CAS number 2230806-06-9), with the capacity to activate PKC facilitates the release of neuregulin 1, and promotes neuroblasts differentiation and survival in cultures of subventricular zone (SVZ) isolated cells in a novel PKC dependent manner. Local infusion of this compound in mechanical cortical injuries induces neuroblast enrichment within the perilesional area, and noninvasive intranasal administration of EOF2 promotes migration of neuroblasts from the SVZ towards the injury, allowing their survival and differentiation into mature neurons, being some of them cholinergic and GABAergic. Our results elucidate the mechanism of EOF2 promoting neurogenesis in injuries and highlight the role of novel PKC isozymes as targets in brain injury regeneration