Study of thermal bubble motion in microchannel

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.The micro capillary pumped loop system (MCPL) is a highly efficient device for heat transfer because the main driving force is a result of thermo-bubbles in micro-channel. In this study, the scaling effect with respect to the dimensional geometry of MCPL was studied for improving the heat transfer performance. The results showed that when a larger heating power was provided by microheater, the growing rate of thermal bubble was faster. Generally speaking, injecting a larger amount of working fluids resulted in faster thermo bubble motion. When the size of channel was scaled down, the nucleation of thermal bubble occurred easily and a positive performance of heat transfer was expected. These findings will be useful to the further optimal design of MCPL.This study is funded by the National Science Council of the Republic of China, Taiwan, under Contract No. NSC- 98-2221-E-197 –018

Suppression of DNA-damage checkpoint signaling by Rsk-mediated phosphorylation of Mre11.

Ataxia telangiectasia mutant (ATM) is an S/T-Q-directed kinase that is critical for the cellular response to double-stranded breaks (DSBs) in DNA. Following DNA damage, ATM is activated and recruited by the MRN protein complex [meiotic recombination 11 (Mre11)/DNA repair protein Rad50/Nijmegen breakage syndrome 1 proteins] to sites of DNA damage where ATM phosphorylates multiple substrates to trigger cell-cycle arrest. In cancer cells, this regulation may be faulty, and cell division may proceed even in the presence of damaged DNA. We show here that the ribosomal s6 kinase (Rsk), often elevated in cancers, can suppress DSB-induced ATM activation in both Xenopus egg extracts and human tumor cell lines. In analyzing each step in ATM activation, we have found that Rsk targets loading of MRN complex components onto DNA at DSB sites. Rsk can phosphorylate the Mre11 protein directly at S676 both in vitro and in intact cells and thereby can inhibit the binding of Mre11 to DNA with DSBs. Accordingly, mutation of S676 to Ala can reverse inhibition of the response to DSBs by Rsk. Collectively, these data point to Mre11 as an important locus of Rsk-mediated checkpoint inhibition acting upstream of ATM activation

Unsupervised Texture Transfer from Images to Model Collections

Large 3D model repositories of common objects are now ubiquitous and are increasingly being used in computer graphics and computer vision for both analysis and synthesis tasks. However, images of objects in the real world have a richness of appearance that these repositories do not capture, largely because most existing 3D models are untextured. In this work we develop an automated pipeline capable of transporting texture information from images of real objects to 3D models of similar objects. This is a challenging problem, as an object's texture as seen in a photograph is distorted by many factors, including pose, geometry, and illumination. These geometric and photometric distortions must be undone in order to transfer the pure underlying texture to a new object --- the 3D model. Instead of using problematic dense correspondences, we factorize the problem into the reconstruction of a set of base textures (materials) and an illumination model for the object in the image. By exploiting the geometry of the similar 3D model, we reconstruct certain reliable texture regions and correct for the illumination, from which a full texture map can be recovered and applied to the model. Our method allows for large-scale unsupervised production of richly textured 3D models directly from image data, providing high quality virtual objects for 3D scene design or photo editing applications, as well as a wealth of data for training machine learning algorithms for various inference tasks in graphics and vision

Ubiquitylation of p53 by the APC/C inhibitor Trim39.

Tripartite motif 39 (Trim39) is a RING domain-containing E3 ubiquitin ligase able to inhibit the anaphase-promoting complex (APC/C) directly. Through analysis of Trim39 function in p53-positive and p53-negative cells, we have found, surprisingly, that p53-positive cells lacking Trim39 could not traverse the G1/S transition. This effect did not result from disinhibition of the APC/C. Moreover, although Trim39 loss inhibited etoposide-induced apoptosis in p53-negative cells, apoptosis was enhanced by Trim39 knockdown in p53-positive cells. Furthermore, we show here that the Trim39 can directly bind and ubiquitylate p53 in vitro and in vivo, leading to p53 degradation. Depletion of Trim39 significantly increased p53 protein levels and cell growth retardation in multiple cell lines. We found that the relative importance of Trim39 and the well-characterized p53-directed E3 ligase, murine double minute 2 (MDM2), varied between cell types. In cells that were relatively insensitive to the MDM2 inhibitor, nutlin-3a, apoptosis could be markedly enhanced by siRNA directed against Trim39. As such, Trim39 may serve as a potential therapeutic target in tumors with WT p53 when MDM2 inhibition is insufficient to elevate p53 levels and apoptosis

Consistent Two-Flow Network for Tele-Registration of Point Clouds

Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds

Learning Shape Priors for Single-View 3D Completion and Reconstruction

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://shapehd.csail.mit.edu

Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.Accessory loci are shown to have similar frequencies in diverse Streptococcus pneumoniae populations, suggesting negative frequency-dependent selection drives post-vaccination population restructuring