An economical arterial-pulse-wave transducer

Transducer records arterial pulses externally. Device uses thin plastic membrane which is fluid coupled to pressure sensitive transistor. Transistor is connected to amplifier which, in turn, is connected to recorder. End section is threaded to accept suitable holder and contains pressure relief vent allowing transistor to sense only pressure levels greater than atmospheric

Arterial pulse wave pressure transducer

An arterial pulse wave pressure transducer is introduced. The transducer is comprised of a fluid filled cavity having a flexible membrane disposed over the cavity and adapted to be placed on the skin over an artery. An arterial pulse wave creates pressure pulses in the fluid which are transduced, by a pressure sensitive transistor in direct contact with the fluid, into an electric signal. The electrical signal is representative of the pulse waves and can be recorded so as to monitor changes in the elasticity of the arterial walls

An extension of the Kreiss stability theorem to families of matrices of unbounded order

AbstractThe Kreiss matrix theorem asserts three necessary and sufficient conditions for a family of matrices of fixed finite order to be L2-stable: a resolvent condition (R), a triangularization condition (S) and a Hermitian norm condition (H). We extend the Kreiss theorem to families of matrices of finite but unbounded order with the restriction that the degrees of the minimal polynomials of all matrices in the family are less than a fixed constant. For such matrix families, we show that (R) and (H) remain necessary and sufficient for L2-stability, while (S) must be replaced by a somewhat stronger “block triangularization” condition (S′). This extended Kreiss theorem permits a corresponding extension of the Buchanan stability theorem

Multi-objective calibration of a surface water-groundwater flow model in an irrigated agricultural region: Yaqui Valley, Sonora, Mexico

Multi-objective optimization was used to calibrate a regional surface water-groundwater model of the Yaqui Valley, a 6800 km² irrigated agricultural region located along the Sea of Cortez in Sonora, Mexico. The model simulates three-dimensional groundwater flow coupled to one-dimensional surface water flow in the irrigation canals. It accounts for the spatial distribution of annual recharge from irrigation, subsurface drainage, agricultural pumping, and irrigation canal seepage. The main advantage of the calibration method is that it accounts for both parameter and model structural uncertainty. In this case, results show that the effect of including the process of bare soil evaporation is significantly greater than the effects of parameter uncertainty. Furthermore, by treating the different objectives independently, a better identification of the model parameters is achieved compared to a single-objective approach, since the various objectives are sensitive to different parameters. The simulated water balance shows that 15&ndash;20% of the water that enters the irrigation canals is lost by seepage to groundwater. The main discharge mechanisms in the Valley are crop evapotranspiration (53%), non-agricultural evapotranspiration and bare soil evaporation (19%), surface drainage to the Sea of Cortez (15%), and groundwater pumping (9%). In comparison, groundwater discharge to the estuary was relatively insignificant (less than 1%). The model was further refined by identifying zonal <i>K_v</i> and <i>K_h</i> values based on a spatial analysis of the model residuals

Detecting and Classifying Nuclei on a Budget

The benefits of deep neural networks can be hard to realise in medical imaging tasks because training sample sizes are often modest. Pre-training on large data sets and subsequent transfer learning to specific tasks with limited labelled training data has proved a successful strategy in other domains. Here, we implement and test this idea for detecting and classifying nuclei in histology, important tasks that enable quantifiable characterisation of prostate cancer. We pre-train a convolutional neural network for nucleus detection on a large colon histology dataset, and examine the effects of fine-tuning this network with different amounts of prostate histology data. Results show promise for clinical translation. However, we find that transfer learning is not always a viable option when training deep neural networks for nucleus classification. As such, we also demonstrate that semi-supervised ladder networks are a suitable alternative for learning a nucleus classifier with limited data

Juno: a Python-based graphical package for optical system design

This report introduces Juno, a modular Python package for optical design and simulation. Juno consists of a complete library that includes a graphical user interface to design and visualise arbitrary optical elements, set up wave propagation simulations and visualise their results. To ensure an efficient visualisation of the results, all simulation data are stored in a structured database that can filter and sort the output. Finally, we present a practical use case for Juno, where optical design and fabrication are interlaced in a feedback cycle. The presented data show how to compare the simulated and the measured propagation; if a difference or unexpected behaviour is found, we show how to convert and import the optical element profile from a profilometer measurement. The propagation through the profile can provide immediate feedback about the quality of the element and a measure of the effects brought by differences between the idealised and the actual profile, therefore, allowing to exclude the experimental errors and to weigh every aspect of fabrication errors.Comment: The software is available at https://github.com/DeMarcoLab/jun

Model and Feature Selection in Hidden Conditional Random Fields with Group Regularization

Proceedings of: 8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013). Salamanca, September 11-13, 2013.Sequence classification is an important problem in computer vision, speech analysis or computational biology. This paper presents a new training strategy for the Hidden Conditional Random Field sequence classifier incorporating model and feature selection. The standard Lasso regularization employed in the estimation of model parameters is replaced by overlapping group-L1 regularization. Depending on the configuration of the overlapping groups, model selection, feature selection,or both are performed. The sequence classifiers trained in this way have better predictive performance. The application of the proposed method in a human action recognition task confirms that fact.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485)Publicad

Cross-View Action Recognition from Temporal Self-Similarities

This paper concerns recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating the high stability of self-similarities under view changes. Self-similarity descriptors are also shown stable under action variations within a class as well as discriminative for action recognition. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multi-view correspondence estimation. Instead, it relies on weak geometric cues captured by self-similarities and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public datasets, it has similar or superior performance compared to related methods and it performs well even in extreme conditions such as when recognizing actions from top views while using side views for training only

Vif is a RNA chaperone that could temporally regulate RNA dimerization and the early steps of HIV-1 reverse transcription

HIV-1 Vif (viral infectivity factor) is associated with the assembly complexes and packaged at low level into the viral particles, and is essential for viral replication in non-permissive cells. Viral particles produced in the absence of Vif exhibit structural defects and are defective in the early steps of reverse transcription. Here, we show that Vif is able to anneal primer tRNALys3 to the viral RNA, to decrease pausing of reverse transcriptase during (–) strand strong-stop DNA synthesis, and to promote the first strand transfer. Vif also stimulates formation of loose HIV-1 genomic RNA dimers. These results indicate that Vif is a bona fide RNA chaperone. We next studied the effects of Vif in the presence of HIV-1 NCp, which is a well-established RNA chaperone. Vif inhibits NCp-mediated formation of tight RNA dimers and hybridization of tRNALys3, while it has little effects on NCp-mediated strand transfer and it collaborates with nucleocapsid (NC) to increase RT processivity. Thus, Vif might negatively regulate NC-assisted maturation of the RNA dimer and early steps of reverse transcription in the assembly complexes, but these inhibitory effects would be relieved after viral budding, thanks to the limited packaging of Vif in the virions