Can magnetic fields be detected during the inspiral of binary neutron stars?

Using accurate and fully general-relativistic simulations we assess the effect that magnetic fields have on the gravitational-wave emission produced during the inspiral and merger of magnetized neutron stars. In particular, we show that magnetic fields have an impact after the merger, because they are amplified by a Kelvin-Helmholtz instability, but also during the inspiral, most likely because the magnetic tension reduces the stellar tidal deformation for extremely large initial magnetic fields, B_0>~10^{17}G. We quantify the influence of magnetic fields by computing the overlap, O, between the waveforms produced during the inspiral by magnetized and unmagnetized binaries. We find that for any realistic magnetic field strength B_0<~10^{14}G the overlap during the inspiral is O>~0.999 and is quite insensitive to the mass of the neutron stars. Only for unrealistically large magnetic fields like B_0~10^{17}G the overlap does decrease noticeably, becoming at our resolutions O<~0.76/0.67 for stars with baryon masses M_b~1.4/1.6 Msun, respectively. Because neutron stars are expected to merge with magnetic fields ~10^{8}-10^{10}G and because present detectors are sensitive to O<~0.995, we conclude that it is very unlikely that the present detectors will be able to discern the presence of magnetic fields during the inspiral of neutron stars.Comment: 5 pages, 4 figures. Small changes to text and figures. Matches version to appear on MNRAS Letter

Boundary controllability and source reconstruction in a viscoelastic string under external traction

Treatises on vibrations devote large space to study the dynamical behavior of an elastic system subject to known external tractions. In fact, usually a "system" is not an isolated body but it is part of a chain of mechanisms which disturb the "system" for example due to the periodic rotation of shafts. This kind of problem has been rarely studied in control theory. In the specific case we shall study, the case of a viscoelastic string, the effect of such external action is on the horizontal component of the traction, and so it affects the coefficients of the corresponding wave type equation, which will be time dependent. The usual methods used in controllability are not naturally adapted to this case. For example at first sight it might seem that moment methods can only be used in case of coefficients which are constant in time. Instead, we shall see that moment methods can be extended to study controllability of a viscoelastic string subject to external traction and in particular we shall study a controllability problem which is encountered in the solution of the inverse problem consisting in the identification of a distributed disturbance source

Recognition of Instrument Passing and Group Attention for Understanding Intraoperative State of Surgical Team

Appropriate evaluation of the intraoperative state of a surgical team is essential for the improvement of teamwork and hence a safe surgical environment. Traditional methods to evaluate intraoperative team states such as interview and self-check questionnaire on each surgical team member often require human efforts, which are time-consuming and can be biased by individual recall. One effective solution is to analyze the surgical video and track the important team activities, such as whether the members are complying with the surgical procedure or are being distracted by unexpected events. However, due to the complexity of the situations in an operating room, identifying the team activities without any human effort remains challenging. In this work, we propose a novel approach that automatically recognizes and quantifies intraoperative activities from surgery videos. As a first step, we focus on recognizing two activities that especially involve multiple individuals: (a) passing of clean-packaged surgery instruments which is a representative interaction between the surgical technologists such as the circulating nurse and scrub nurse, and (b) group attention that may be attracted by unexpected events. We record surgical videos as input, and apply pose estimation and particle filters to extract individual's face orientation, body orientation, and arm raise. These results coupled with individual IDs are then sent to an estimation model that provides the probability of each target activity. Simultaneously, a person model is generated and bound to each individual, which describes all the involved activities along the timeline. We tested our method using videos of simulated activities. The results showed that the system was able to recognize instrument passing and group attention with F1 = 0.95 and F1 = 0.66, respectively. We also implemented a system with an interface that automatically annotated intraoperative activities along the video timeline, and invited feedback from surgical technologists. The results suggest that the quantified and visualized activities can help improve understanding of the intraoperative state of the surgical team

Design Elements of Pervasive Games for Elderly Players: A Social Interaction Study Case

We present the design process and evaluation of a pervasive, location-based mobile game created to act as an experiment system and allow evaluation of how different design elements can influence player behaviour, using social interaction as a study case. A feasibility study with a group of community dwelling elderly volunteers from the city of Kyoto, Japan, was performed to evaluate the system. Results showed that the choice of theme and overall design of game was adequate, and that elderly people could understand the game rules and their goals while playing. Points of improvement included reducing the complexity of game controls and changing social interaction mechanics to account for situations when there are only a few players active or players are too far apart

Promoting Physical Activity in Japanese Older Adults Using a Social Pervasive Game: Randomized Controlled Trial

Background: Pervasive games aim to create more fun and engaging experiences by mixing elements from the real world into the game world. Because they intermingle with players’ lives and naturally promote more casual gameplay, they could be a powerful strategy to stimulate physical activity among older adults. However, to use these games more effectively, it is necessary to understand how design elements of the game affect player behavior. Objective: The aim of this study was to evaluate how the presence of a specific design element, namely social interaction, would affect levels of physical activity. Methods: Participants were recruited offline and randomly assigned to control and intervention groups in a single-blind design. Over 4 weeks, two variations of the same pervasive game were compared: with social interaction (intervention group) and with no social interaction (control group). In both versions, players had to walk to physical locations and collect virtual cards, but the social interaction version allowed people to collaborate to obtain more cards. Changes in the weekly step counts were used to evaluate the effect on each group, and the number of places visited was used as an indicator of play activity. Results: A total of 20 participants were recruited (no social interaction group, n=10; social interaction group, n=10); 18 participants remained active until the end of the study (no social interaction group, n=9; social interaction group, n=9). Step counts during the first week were used as the baseline level of physical activity (no social interaction group: mean 46, 697.2, SE 7905.4; social interaction group: mean 45, 967.3, SE 8260.7). For the subsequent weeks, changes to individual baseline values (absolute/proportional) for the no social interaction group were as follows: 1583.3 (SE 3108.3)/4.6% (SE 7.2%) (week 2), 591.5 (SE 2414.5)/2.4% (SE 4.7%) (week 3), and −1041.8 (SE 1992.7)/0.6% (SE 4.4%) (week 4). For the social interaction group, changes to individual baseline values were as follows: 11520.0 (SE 3941.5)/28.0% (SE 8.7%) (week 2), 9567.3 (SE 2631.5)/23.0% (SE 5.1%) (week 3), and 7648.7 (SE 3900.9)/13.9% (SE 8.0%) (week 4). The result of the analysis of the group effect was significant (absolute change: η2=0.31, P=.04; proportional change: η2=0.30, P=.03). Correlations between both absolute and proportional change and the play activity were significant (absolute change: r=0.59, 95% CI 0.32 to 0.77; proportional change: r=0.39, 95% CI 0.08 to 0.64). Conclusions: The presence of social interaction design elements in pervasive games appears to have a positive effect on levels of physical activity. Trial Registration: Japan Medical Association Clinical Trial Registration Number JMA-IIA00314; https://tinyurl.com/y5nh6ylr (Archived by WebCite at http://www.webcitation.org/761a6MVAy

NFE2-Related transcription factor 2 coordinates antioxidant defense with thyroglobulin production and iodination in the thyroid gland

Background: The thyroid gland has a special relationship with oxidative stress. While generation of oxidative substances is part of normal iodide metabolism during thyroid hormone synthesis, the gland must also defend itself against excessive oxidation in order to maintain normal function. Antioxidant and detoxification enzymes aid thyroid cells to maintain homeostasis by ameliorating oxidative insults, including during exposure to excess iodide, but the factors that coordinate their expression with the cellular redox status are not known. The antioxidant response system comprising the ubiquitously expressed NFE2-related transcription factor 2 (Nrf2) and its redox-sensitive cytoplasmic inhibitor Kelch-like ECH-associated protein 1 (Keap1) defends tissues against oxidative stress, thereby protecting against pathologies that relate to DNA, protein, and/or lipid oxidative damage. Thus, it was hypothesized that Nrf2 should also have important roles in maintaining thyroid homeostasis. Methods: Ubiquitous and thyroid-specific male C57BL6J Nrf2 knockout (Nrf2-KO) mice were studied. Plasma and thyroids were harvested for evaluation of thyroid function tests by radioimmunoassays and of gene and protein expression by real-time polymerase chain reaction and immunoblotting, respectively. Nrf2-KO and Keap1-KO clones of the PCCL3 rat thyroid follicular cell line were generated using CRISPR/Cas9 technology and were used for gene and protein expression studies. Software-predicted Nrf2 binding sites on the thyroglobulin enhancer were validated by site-directed in vitro mutagenesis and chromatin immunoprecipitation. Results: The study shows that Nrf2 mediates antioxidant transcriptional responses in thyroid cells and protects the thyroid from oxidation induced by iodide overload. Surprisingly, it was also found that Nrf2 has a dramatic impact on both the basal abundance and the thyrotropin-inducible intrathyroidal abundance of thyroglobulin (Tg), the precursor protein of thyroid hormones. This effect is mediated by cell-autonomous regulation of Tg gene expression by Nrf2 via its direct binding to two evolutionarily conserved antioxidant response elements in an upstream enhancer. Yet, despite upregulating Tg levels, Nrf2 limits Tg iodination both under basal conditions and in response to excess iodide. Conclusions: Nrf2 exerts pleiotropic roles in the thyroid gland to couple cell stress defense mechanisms to iodide metabolism and the thyroid hormone synthesis machinery, both under basal conditions and in response to excess iodide.Fil: Ziros, Panos G. Lausanne University; SuizaFil: Habeos, Ioannis. Patras University; GreciaFil: Chartoumpekis, Dionysios V. University of Pittsburgh; Estados UnidosFil: Ntalampyra, Eleni. Universite de Lausanne; SuizaFil: Somm, Emmanuel. Universite de Lausanne; SuizaFil: Renaud, Cédric O.. Universite de Lausanne; SuizaFil: Bongiovanni, Massimo. Institute Of Pathology Locarno; SuizaFil: Trougakos, Ioannis P. Universidad Nacional y Kapodistríaca de Atenas; GreciaFil: Yamamoto, Masayuki. University Of Tohoku; JapónFil: Kensler, Thomas W.. University of Pittsburgh at Johnstown; Estados UnidosFil: Santisteban, Pilar. Universidad Autónoma de Madrid; EspañaFil: Carrasco, Nancy. University of Yale. School of Medicine; Estados UnidosFil: Ris Stalpers, Carrie. Academic Medical Center; Países BajosFil: Amendola, Elena. Universidad de Nápoles; ItaliaFil: Liao, Xiao-Hui. University of Chicago; Estados UnidosFil: Rossich, Luciano Esteban. Comisión Nacional de Energía Atómica de Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Thomasz, Lisa. Comisión Nacional de Energía Atómica de Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Juvenal, Guillermo Juan. Comisión Nacional de Energía Atómica de Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Refetoff, Samuel. University of Chicago; Estados UnidosFil: Sykiotis, Gerasimos P.. Universite de Lausanne; Suiz

Integrating Preprocessing Operations into Deep Learning Model: Case Study of Posttreatment Visual Acuity Prediction

Designing a deep neural network model that integrates clinical images with other electronic medical records entails various preprocessing operations. Preprocessing of clinical images often requires trimming of parts of the lesions shown in the images, whereas preprocessing of other electronic medical records requires vectorization of these records; for example, patient age is often converted into a categorical vector of 10-year intervals. Although these preprocessing operations are critical to the performance of the classification model, there is no guarantee that the preprocessing step chosen is appropriate for model training. The ability to integrate these preprocessing operations into a deep neural network model and to train the model, including the preprocessing operations, can help design a multi-modal medical classification model. This study proposes integration layers of preprocessing, both for clinical images and electronic medical records, in deep neural network models. Preprocessing of clinical images is realized by a vision transformer layer that selectively adopts the parts of the images requiring attention. The preprocessing of other medical electrical records is performed by adopting full-connection layers and normalizing these layers. These proposed preprocessing-integrated layers were verified using a posttreatment visual acuity prediction task in ophthalmology as a case study. This prediction task requires clinical images as well as patient profile data corresponding to each patient's posttreatment logMAR visual acuity. The performance of a heuristically designed prediction model was compared with the performance of the prediction model that includes the proposed preprocessing integration layers. The mean square errors between predicted and correct results were 0.051 for the heuristic model and 0.054 for the proposed model. Experimental results showed that the proposed model utilizing preprocessing integration layers achieved nearly the same performance as the heuristically designed model

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Insight into glucocorticoid receptor signalling through interactome model analysis

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling