Optical fluid and biomolecule transport with thermal fields

A long standing goal is the direct optical control of biomolecules and water for applications ranging from microfluidics over biomolecule detection to non-equilibrium biophysics. Thermal forces originating from optically applied, dynamic microscale temperature gradients have shown to possess great potential to reach this goal. It was demonstrated that laser heating by a few Kelvin can generate and guide water flow on the micrometre scale in bulk fluid, gel matrices or ice without requiring any lithographic structuring. Biomolecules on the other hand can be transported by thermal gradients, a mechanism termed thermophoresis, thermal diffusion or Soret effect. This molecule transport is the subject of current research, however it can be used to both characterize biomolecules and to record binding curves of important biological binding reactions, even in their native matrix of blood serum. Interestingly, thermophoresis can be easily combined with the optothermal fluid control. As a result, molecule traps can be created in a variety of geometries, enabling the trapping of small biomolecules, like for example very short DNA molecules. The combination with DNA replication from thermal convection allows us to approach molecular evolution with concurrent replication and selection processes inside a single chamber: replication is driven by thermal convection and selection by the concurrent accumulation of the DNA molecules. From the short but intense history of applying thermal fields to control fluid flow and biological molecules, we infer that many unexpected and highly synergistic effects and applications are likely to be explored in the future

Reorganization of a Spinal Motoneuron Nucleus following Autologous Nerve Graft in the Rat

Autologous nerve grafts are steadily regarded as the method of choice for bridging the nerve gaps resulting after peripheral nerve lesions with substance defects. Microsurgical techniques and the perineurial suture of corresponding fascicles have improved the functional results following peripheral nerve graft. However, regeneration success is often disappointing, despite the most thorough technique and expertise. The loss of spinal motoneurons associated with a nerve lesion and the growth of axon sprouts in inadequate endoneurial sheaths were held responsible as the reason for the lowered muscular strength, limited movement coordination and fine motor skills, poor differentiation and localization of sensory stimuli and for the lack of tactile gnosis. In this experimental study, it is assumed that the central effects at the level of the spinal motoneuron nuclei show an image of the peripheral misinnervation in topographical-morphological terms, and can supply an explanatory model for the functional motor deficits after peripheral nerve graft. On the other hand, the plastic changes of a motor cell column in the reinnervation process influence the structural-functional relationships of the motor units in a variety of clinically relevant ways

Turing's three philosophical lessons and the philosophy of information

In this article, I outline the three main philosophical lessons that we may learn from Turing's work, and how they lead to a new philosophy of information. After a brief introduction, I discuss his work on the method of levels of abstraction (LoA), and his insistence that questions could be meaningfully asked only by specifying the correct LoA. I then look at his second lesson, about the sort of philosophical questions that seem to be most pressing today. Finally, I focus on the third lesson, concerning the new philosophical anthropology that owes so much to Turing's work. I then show how the lessons are learned by the philosophy of information. In the conclusion, I draw a general synthesis of the points made, in view of the development of the philosophy of information itself as a continuation of Turing's work. This journal is © 2012 The Royal Society.Peer reviewe

Epitaxial graphene on SiC(0001): More than just honeycombs

The potential of graphene to impact the development of the next generation of electronics has renewed interest in its growth and structure. The graphitization of hexagonal SiC surfaces provides a viable alternative for the synthesis of graphene, with wafer-size epitaxial graphene on SiC(0001) now possible. Despite this recent progress, the exact nature of the graphene-SiC interface and whether the graphene even has a semiconducting gap remain controversial. Using scanning tunneling microscopy with functionalized tips and density functional theory calculations, here we show that the interface is a warped carbon sheet consisting of three-fold hexagon-pentagon-heptagon complexes periodically inserted into the honeycomb lattice. These defects relieve the strain between the graphene layer and the SiC substrate, while still retaining the three-fold coordination for each carbon atom. Moreover, these defects break the six-fold symmetry of the honeycomb, thereby naturally inducing a gap: the calculated band structure of the interface is semiconducting and there are two localized states near K below the Fermi level, explaining the photoemission and carbon core-level data. Nonlinear dispersion and a 33 meV gap are found at the Dirac point for the next layer of graphene, providing insights into the debate over the origin of the gap in epitaxial graphene on SiC(0001). These results indicate that the interface of the epitaxial graphene on SiC(0001) is more than a dead buffer layer, but actively impacts the physical and electronic properties of the subsequent graphene layers

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively