Preferential binding of HIF-1 to transcriptionally active loci determines cell-type specific response to hypoxia

ChIP-chip and microarray expression studies show that, in response to hypoxia, HIF-1 preferentially binds to and up-regulates already active genes

Synchronization behavior of a weakly damped far-resonance vibrating system

In order to reveal synchronization characteristics of a weakly damped system with two rotors mounted on different vibrating bodies, we propose a simplified physical model. Vibration of the system is discussed by the average method, which can separate fast motions (high frequency) from slow motions (low frequency). Theoretical research shows that vibration torque is the key factor to balance the energy distribution between rotors. For the system with rotational frequencies larger than the nature frequencies, the coupling characteristic frequency or characteristic frequency curve should be considered. As the coupling frequency is close to the characteristic frequency, or the vibration state is close to the characteristic frequency curve, self-synchronization of two rotors can be obtained easily

Angle stability and outflow in dual blade ab interno trabeculectomy with active versus passive chamber management.

PurposeTo compare intraoperative angle stability and postoperative outflow of two ab interno trabeculectomy devices that excise the trabecular meshwork with or without active aspiration and irrigation. We hypothesized that anterior segment optical coherence tomography (AS-OCT) allows for a quantitative comparison of intraoperative angle stability in a microincisional glaucoma surgery (MIGS) pig eye training model.MethodsTwelve freshly enucleated porcine eyes were measured with AS-OCT at baseline, at the beginning of the procedure and at its conclusion to determine the anterior chamber depth (ACD) and the nasal angle α in degrees. The right and left eye of pairs were randomly assigned to an active dual blade goniectome (aDBG) and a passive dual blade goniectome (pDBG) group, respectively. The aDBG had irrigation and aspiration ports while the pDBG required surgery under viscoelastic. We performed the procedures using our MIGS training system with a standard, motorized ophthalmic operating microscope. We estimated outflow by obtaining canalograms with fluorescent spheres.ResultsIn aDBG, the nasal angle remained wide open during the procedure at above 90° and did not change towards the end (100±10%, p = 0.9). In contrast, in pDBG, ACD decreased by 51±19% to 21% below baseline (p<0.01) while the angle progressively narrowed by 40±12% (p<0.001). Canalograms showed a similar extent of access to the outflow tract with the aDBG and the pDBG (p = 0.513). The average increase for the aDBG in the superonasal and inferonasal quadrants was between 27 to 31% and for the pDBG between 15 to 18%.ConclusionAS-OCT demonstrated that active irrigation and aspiration improved anterior chamber maintenance and ease of handling with the aDBG in this MIGS training model. The immediate postoperative outflow was equally good with both devices

Coreset Selection with Prioritized Multiple Objectives

Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale data, so that training only on the subset practically performs on par with full data. When coreset selection is applied in realistic scenes, under the premise that the identified coreset has achieved comparable model performance, practitioners regularly desire the identified coreset can have a size as small as possible for lower costs and greater acceleration. Motivated by this desideratum, for the first time, we pose the problem of "coreset selection with prioritized multiple objectives", in which the smallest coreset size under model performance constraints is explored. Moreover, to address this problem, an innovative method is proposed, which maintains optimization priority order over the model performance and coreset size, and efficiently optimizes them in the coreset selection procedure. Theoretically, we provide the convergence guarantee of the proposed method. Empirically, extensive experiments confirm its superiority compared with previous strategies, often yielding better model performance with smaller coreset sizes

Diurnal pattern of nitrous oxide emissions from a sewage-enriched river : references to IPCC indirect emission factor

Poster Sessio

Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm

Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative. Motivated by noisy multi-class learning, the introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms for noisy multi-label learning. However, estimating multi-label noise transition matrices remains a challenging task, as most existing estimators in noisy multi-class learning rely on anchor points and accurate fitting of noisy class posteriors, which is hard to satisfy in noisy multi-label learning. In this paper, we address this problem by first investigating the identifiability of class-dependent transition matrices in noisy multi-label learning. Building upon the identifiability results, we propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors. Specifically, we first estimate the occurrence probability of two noisy labels to capture noisy label correlations. Subsequently, we employ sample selection techniques to extract information implying clean label correlations, which are then used to estimate the occurrence probability of one noisy label when a certain clean label appears. By exploiting the mismatches in label correlations implied by these occurrence probabilities, we demonstrate that the transition matrix becomes identifiable and can be acquired by solving a bilinear decomposition problem. Theoretically, we establish an estimation error bound for our multi-label transition matrix estimator and derive a generalization error bound for our statistically consistent algorithm. Empirically, we validate the effectiveness of our estimator in estimating multi-label noise transition matrices, leading to excellent classification performance

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

biological space