SERV-CT: A disparity dataset from cone-beam CT for validation of endoscopic 3D reconstruction

In computer vision, reference datasets from simulation and real outdoor scenes have been highly successful in promoting algorithmic development in stereo reconstruction. Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. Previous datasets have been produced using computed tomography (CT) or structured light reconstruction on phantom or ex vivo models. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around 2 pixels and a depth accuracy of about 2 mm. A simplified reference dataset is provided consisting of endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types, including smooth specular surfaces, as well as significant variation of depth. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths covering the majority of the endoscopic image. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms

Dominant g(9/2)^2 neutron configuration in the 4+1 state of 68Zn based on new g factor measurements

The $g$ factor of the $4_1^+$ state in $^{68}$Zn has been remeasured with improved energy resolution of the detectors used. The value obtained is consistent with the previous result of a negative $g$ factor thus confirming the dominant $0g_{9/2}$ neutron nature of the $4_1^+$ state. In addition, the accuracy of the $g$ factors of the $2_1^+$, $2_2^+$ and $3_1^-$ states has been improved an d their lifetimes were well reproduced. New large-scale shell model calculations based on a $^{56}$Ni core and an $0f_{5/2}1pg_{9/2}$ model space yield a theoretical value, $g(4_1^+) = +0.008$. Although the calculated value is small, it cannot fully explain the experimental value, $g(4_1^+) = -0.37(17)$. The magnitude of the deduced B(E2) of the $4_1^+$ and $2_1^+$ transition is, however, rather well described. These results demonstrate again the importance of $g$ factor measurements for nuclear structure determination s due to their specific sensitivity to detailed proton and neutron components in the nuclear wave functions.Comment: 7 pages, 3 figs, submitted to PL

Synapsins I and II Are Not Required for Insulin Secretion from Mouse Pancreatic beta-cells

Synapsins are a family of phosphoproteins that modulate the release of neurotransmitters from synaptic vesicles. The release of insulin from pancreatic beta-cells has also been suggested to be regulated by synapsins. In this study, we have utilized a knock out mouse model with general disruptions of the synapsin I and II genes [synapsin double knockout (DKO)]. Stimulation with 20 mM glucose increased insulin secretion 9-fold in both wild-type (WT) and synapsin DKO islets, whereas secretion in the presence of 70 mM K+ and 1mM glucose was significantly enhanced in the synapsin DKO mice compared to WT. Exocytosis in single beta-cells was investigated using patch clamp. The exocytotic response, measured by capacitance measurements and elicited by a depolarization protocol designed to visualize exocytosis of vesicles from the readily releasable pool and from the reserve pool, was of the same size in synapsin DKO and WT beta-cells. The increase in membrane capacitance corresponding to readily releasable pool was approximately 50fF in both genotypes. We next investigated the voltage-dependent Ca2+ influx. In both WT and synapsin DKO beta-cells the Ca2+ current peaked at 0 mV and measured peak current (I-p) and net charge (Q) were of similar magnitude. Finally, ultrastructural data showed no variation in total number of granules (N-v) or number of docked granules (N-s) between the beta-cells from synapsin DKO mice and WT control. We conclude that neither synapsin I nor synapsin II are directly involved in the regulation of glucose-stimulated insulin secretion and Ca-2-dependent exocytosis in mouse pancreatic beta-cells. (Endocrinology 153: 2112-2119, 2012

Automatic, global registration in laparoscopic liver surgery

PURPOSE: The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D-2D global registration in laparoscopic liver interventions. METHODS: Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. RESULTS: We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. CONCLUSIONS: Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration

Consensus and contention in the priority setting process: examining the health sector in Uganda.

Health priority setting is a critical and contentious issue in low-income countries because of the high burden of disease relative to the limited resource envelope. Many sophisticated quantitative tools and policy frameworks have been developed to promote transparent priority setting processes and allocative efficiency. However, low-income countries frequently lack effective governance systems or implementation capacity, so high-level priorities are not determined through evidence-based decision-making processes. This study uses qualitative research methods to explore how key actors' priorities differ in low-income countries, using Uganda as a case study. Human resources for health, disease prevention and family planning emerge as the common priorities among actors in the health sector (although the last of these is particularly emphasized by international agencies) because of their contribution to the long-term sustainability of health-care provision. Financing health-care services is the most disputed issue. Participants from the Ugandan Ministry of Health preferentially sought to increase net health expenditure and government ownership of the health sector, while non-state actors prioritized improving the efficiency of resource use. Ultimately it is apparent that the power to influence national health outcomes lies with only a handful of decision-makers within key institutions in the health sector, such as the Ministries of Health, the largest bilateral donors and the multilateral development agencies. These power relations reinforce the need for ongoing research into the paradigms and strategic interests of these actors

Gesture Recognition in Robotic Surgery with Multimodal Attention

Automatically recognising surgical gestures from surgical data is an important building block of automated activity recognition and analytics, technical skill assessment, intra-operative assistance and eventually robotic automation. The complexity of articulated instrument trajectories and the inherent variability due to surgical style and patient anatomy make analysis and fine-grained segmentation of surgical motion patterns from robot kinematics alone very difficult. Surgical video provides crucial information from the surgical site with context for the kinematic data and the interaction between the instruments and tissue. Yet sensor fusion between the robot data and surgical video stream is non-trivial because the data have different frequency, dimensions and discriminative capability. In this paper, we integrate multimodal attention mechanisms in a two-stream temporal convolutional network to compute relevance scores and weight kinematic and visual feature representations dynamically in time, aiming to aid multimodal network training and achieve effective sensor fusion. We report the results of our system on the JIGSAWS benchmark dataset and on a new in vivo dataset of suturing segments from robotic prostatectomy procedures. Our results are promising and obtain multimodal prediction sequences with higher accuracy and better temporal structure than corresponding unimodal solutions. Visualization of attention scores also gives physically interpretable insights on network understanding of strengths and weaknesses of each sensor

Genome-wide association study of Stayability and Heifer Pregnancy in Red Angus cattle

Reproductive performance is the most important component of cattle production from the standpoint of economic sustainability of commercial beef enterprises. Heifer Pregnancy (HPG) and Stayability (STAY) genetic predictions are 2 selection tools published by the Red Angus Association of America (RAAA) to assist with improvements in reproductive performance. Given the importance of HPG and STAY to the profitability of commercial beef enterprises, the objective of this study was to identify QTL associated with both HPG and STAY in Red Angus cattle. A genome-wide association study (GWAS) was performed using deregressed HPG and STAY EBV, calculated using a single-trait animal model and a 3-generation pedigree with data from the Spring 2015 RAAA National Cattle Evaluation. Each individual animal possessed 74,659 SNP genotypes. Individual animals with a deregressed EBV reliability \u3e 0.05 were merged with the genotype file and marker quality control was performed. Criteria for sifting genotypes consisted of removing those markers where any of the following were found: average call rate less than 0.85, minor allele frequency \u3c 0.01, lack of Hardy–Weinberg equilibrium (P \u3c 0.0001), or extreme linkage disequilibrium (r2 \u3e 0.99). These criteria resulted in 2,664 animals with 62,807 SNP available for GWAS. Association studies were performed using a Bayes Cπ model in the BOLT software package. Marker significance was calculated as the posterior probability of inclusion (PPI), or the number of instances a specific marker was sampled divided by the total number of samples retained from the Markov chain Monte Carlo chains. Nine markers, with a PPI ≥ 3% were identified as QTL associated with HPG on BTA 1, 11, 13, 23, and 29. Twelve markers, with a PPI ≥ 75% were identified as QTL associated with STAY on BTA 6, 8, 9, 12, 15, 18, 22, and 23

First measurement and shell model interpretation of the g factor of the 21+ state in self-conjugate radioactive 44Ti

AbstractThe g factor of the 21+ state in radioactive 44Ti has been measured for the first time with the technique of α transfer to 40Ca beams in inverse kinematics in combination with transient magnetic fields, yielding the value, g(21+)=+0.52(15). In addition, the lifetimes of the 21+, τ=3.97(28) ps, and the 41+ states, τ=0.65(6) ps, were redetermined with higher precision using the Doppler shift attenuation method. The deduced B(E2)'s and the g factor were well explained by a full fp shell model calculation using the FPD6 effective NN interaction. The g factor can also be accounted for by a simple rotational model (g=Z/A). However, if one also considers the B(E2)'s and the E(41+)/E(21+) ratios, then an imperfect vibrator picture gives better agreement with the data

Fotografía DE DOS JÓVENES EN TONO SONRIENTE [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201