Received dose variability after administration of I-131 for treatment of hyperthyroidism in cats

Abstract Background Injectable radioactive iodine (I‐131) frequently is used to treat hyperthyroidism in cats. In human medicine, residual activity after injection of radionuclides has been reported, and the actual quantity administered is recorded after administration. Objective Our aim was to evaluate actual administered dose variability after administration of preprepared I‐131 single unit doses for the treatment of hyperthyroidism in cats. Animals Twenty‐seven cats with hyperthyroidism treated with I‐131 between April 2017 and March 2019. Methods Retrospective observational study of cats treated with preprepared single unit I‐131 doses. For each dose, the measured activity before administration and residual activity were recorded. The measured dose and the actual dose administered were compared to the prescribed dose. Results Measured activity before administration ranged from 88.4% to 103.3% of the prescribed dose. Mean residual activity was 5.2 ± 3.0 MBq (ranging from 1.5% to 15% of the prescribed dose). The actual dose administered (measured activity − residual activity) ranged from 79.1% to 100.2% of the prescribed dose. Seventeen of 28 (60.7%) of the actual administered doses differed between 10% and 20% of the prescribed dose. One administered dose had a >20% difference compared to the prescribed dose (79.10% of the prescribed dose). Conclusion and Clinical Importance Our study identified variability in the residual and actual administered activity of I‐131 as compared to the prescribed dose, which should be taken into consideration when treating cats with (predrawn) I‐131

The role of pre-eruptive gas segregation on co-eruptive deformation and SO2 emissions

The presence of exsolved gas bubbles influences measurements of both volcanic surface deformation and SO2 emissions. In a closed-system, exsolved volatiles remain within the melt but in an open-system, the decoupled gas phase can either outgas or accumulate, leading to large variations magmatic gas fraction. Here we investigate the role of gas volume fraction and gas segregation processes on magma properties and co-eruptive monitoring data. First we use thermodynamic models of gas exsolution to model gas volume fraction and magma compressibility, and use these to calculate SO2 emissions and co-eruptive volume change. We find that volume change is equally sensitive to magma compressibility and chamber compressibility over realistic parameters ranges, and both must be considered when interpreting surface deformation data. Reservoir depth and magma composition are the dominant controls on gas volume fraction, but the initial content of H2O and S have strong influences on volume change and SO2 emissions, respectively. Pre-eruptive gas accumulation produces increased SO2 emissions and muted co-eruptive deformation, while degassing has the opposite effect. We then compare our models to a compilation of data from 20 recent eruptions where measurements of volume change, SO2 emissions and erupted volume are available. To the first order, shallow reservoirs produce smaller volume changes per volume erupted and silica-poor magmas yield greater co-eruptive volume changes than silica-rich systems, consistent with closed system degassing.  Co-eruptive degassing causes high SO2 emissions during effusive eruptions. Comparison between model predictions and observations suggests that all magmatic systems experience a certain degree of outgassing prior to an eruption. Our findings are consistent with current conceptual models of transcrustal magmatic systems consisting of heterogeneous mixtures of gas and melt and have important implications for the interpretation of surface deformation and SO2 emission signals at all stages of the eruption cycle.

Context-Awareness at the Service of Sensor Fusion Systems: Inverting the Usual Scheme

Proceedings of: 11th International Work-Conference on Artificial Neural Networks (IWANN 2011). International Workshop of Intelligent systems for context-based information fusion (ISCIF 11). Torremolinos-Málaga, Spain, June 8-10, 2011Many works on context-aware systems make use of location, navigation or tracking services offered by an underlying sensor fusion module, as part of the relevant contextual information. The obtained knowledge is typically consumed only by the high level layers of the system, in spite that context itself represents a valuable source of information from which every part of the implemented system could take benefit. This paper closes the loop, analyzing how can context knowledge be applied to improve the accuracy, robustness and adaptability of sensor fusion processes. The whole theoretical analysis will be related with the indoor/outdoor navigation system implemented for a wheeled robotic platform. Some preliminary results are presented, where the context information provided by a map is integrated in the sensor fusion system.This work was supported in part by Projects ATLANTIDA, CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 DPS2008-07029-C02-02.Publicad

Augmenting forearm crutches with wireless sensors for lower limb rehabilitation

Forearm crutches are frequently used in the rehabilitation of an injury to the lower limb. The recovery rate is improved if the patient correctly applies a certain fraction of their body weight (specified by a clinician) through the axis of the crutch, referred to as partial weight bearing (PWB). Incorrect weight bearing has been shown to result in an extended recovery period or even cause further damage to the limb. There is currently no minimally invasive tool for long-term monitoring of a patient's PWB in a home environment. This paper describes the research and development of an instrumented forearm crutch that has been developed to wirelessly and autonomously monitor a patient's weight bearing over the full period of their recovery, including its potential use in a home environment. A pair of standard forearm crutches are augmented with low-cost off-the-shelf wireless sensor nodes and electronic components to provide indicative measurements of the applied weight, crutch tilt and hand position on the grip. Data are wirelessly transmitted between crutches and to a remote computer (where they are processed and visualized in LabVIEW), and the patient receives biofeedback by means of an audible signal when they put too much or too little weight through the crutch. The initial results obtained highlight the capability of the instrumented crutch to support physiotherapists and patients in monitoring usage

Deep learning for the extraction of sketches from spectral images of historical paintings

The sketches of painted cultural objects can be the most indicative of the style of paintings. Extraction of the sketches is an integral process used by conservators and art historians for documentation and for artists to learn historical painting styles through copying and painting. However, at present, sketch extraction is mainly manually drawn, which is not only time-consuming, but also subjective and dependent on experience. Therefore, both accuracy and efficiency need to be improved. In recent years, with the development of machine learning, a series of extraction methods based on edge detection have emerged. However, most of the existing methods can only perform successful extraction if the sketches are well preserved , but for the data with faded sketches or severe conservation issues, the extraction methods need to be improved. It is beneficial to extract the bands that accentuate the sketches while suppressing the effects of the degraded areas and the overlapping paints. We propose a sketch extraction method based on hyperspectral image and deep learning. Firstly, the hyperspectral image data is collected and the bands sensitive to the sketches are extracted by a prior knowledge of the sketches (e.g. near infrared bands will be chosen if the sketches are made of carbon ink), and a dataset including a large number of existing natural images is used to pre-train the bi-directional cascade network (BDCN). The network parameters in the model are then fine-tuned by using the images of painted cultural objects drawn by experts, so as to solve the problem of insufficient sketch dataset of painted cultural objects and enhance the generalization ability of the model. Finally, the U-net network is used to further suppress the noise, i.e. unwanted information, and make the sketch clearer. The experimental results show that the proposed method can not only effectively extract sketch from ideal data, but also extract clear sketches from data with faded sketches and even with noise interference. It is superior to the other six advanced based on edge detection methods in visual and objective comparison, and has a good application prospect. The proposed deep learning method is also compared with an unsupervised clustering method using Self-Organising Map (SOM) which is a ‘shallow learning’ method where pixels of similar spectra are grouped into clusters without the need for data labeling by experts

What have we learnt from EUPORIAS climate service prototypes?

The international effort toward climate services, epitomised by the development of the Global Framework for Climate Services and, more recently the launch of Copernicus Climate Change Service has renewed interest in the users and the role they can play in shaping the services they will eventually use. Here we critically analyse the results of the five climate service prototypes that were developed as part of the EU funded project EUPORIAS. Starting from the experience acquired in each of the projects we attempt to distil a few key lessons which, we believe, will be relevant to the wider community of climate service developers

Antenatal steroids in preterm labour for the prevention of neonatal deaths due to complications of preterm birth

Background In high-income countries, administration of antenatal steroids is standard care for women with anticipated preterm labour. However, although >1 million deaths due to preterm birth occur annually, antenatal steroids are not routine practice in low-income countries where most of these deaths occur