Cosmic neutrinos at IceCube: θ13\theta_{13}, δ\delta and initial flavor composition

We discuss the prospect of extracting the values of the mixing parameters $\delta$ and $\theta_{13}$ through the detection of cosmic neutrinos in the planned and forthcoming neutrino telescopes. We take the ratio of the muon-track to shower-like events, R, as the realistic quantity that can be measured in the neutrino telescopes. We take into account several sources of uncertainties that enter the analysis. We then examine to what extent the deviation of the initial flavor composition from w_e:w_\mu:w_\tau=1:2:0 can be tested.Comment: 3 pages, 2 figures, Talk given at the TAUP 2009 conference, Rome, Italy; J. Phys. Conf. Series to appea

Pseudo-Dirac Neutrino Scenario: Cosmic Neutrinos at Neutrino Telescopes

Within the "pseudo-Dirac" scenario for massive neutrinos the existence of sterile neutrinos which are almost degenerate in mass with the active ones is hypothesized. The presence of these sterile neutrinos can affect the flavor composition of cosmic neutrinos arriving at Earth after traveling large distances from astrophysical objects. We examine the prospects of neutrino telescopes such as IceCube to probe the very tiny mass squared differences 10^(-12) eV^2<\Delta m^2<10^(-19) eV^2, by analyzing the ratio of $\mu$-track events to shower-like events. Considering various sources of uncertainties which enter this analysis, we examine the capability of neutrino telescopes to verify the validity of the pseudo-Dirac neutrino scenario and especially to discriminate it from the conventional scenario with no sterile neutrino. We also discuss the robustness of our results with respect to the uncertainties in the initial flavor ratio of neutrinos at the source.Comment: 24 pages, 5 figure

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms

© 2019 Elsevier Inc. Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25–0.78), body mass index (OR = 0.94, CI = 0.89–0.99), and diabetes (OR = 2.33, CI = 1.18–4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31–5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21–14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. Conclusions: Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. DESIGN: This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting. SETTING: The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020. PARTICIPANTS: Eleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed. RESULTS: Of 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy. CONCLUSIONS: Use of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice

Neutrino Decays over Cosmological Distances and the Implications for Neutrino Telescopes

We discuss decays of ultra-relativistic neutrinos over cosmological distances by solving the decay equation in terms of its redshift dependence. We demonstrate that there are significant conceptual differences compared to more simplified treatments of neutrino decay. For instance, the maximum distance the neutrinos have traveled is limited by the Hubble length, which means that the common belief that longer neutrino lifetimes can be probed by longer distances does not apply. As a consequence, the neutrino lifetime limit from supernova 1987A cannot be exceeded by high-energy astrophysical neutrinos. We discuss the implications for neutrino spectra and flavor ratios from gamma-ray bursts as one example of extragalactic sources, using up-to-date neutrino flux predictions. If the observation of SN 1987A implies that \nu_1 is stable and the other mass eigenstates decay with rates much smaller than their current bounds, the muon track rate can be substantially suppressed compared to the cascade rate in the region IceCube is most sensitive to. In this scenario, no gamma-ray burst neutrinos may be found using muon tracks even with the full scale experiment, whereas reliable information on high-energy astrophysical sources can only be obtained from cascade measurements. As another consequence, the recently observed two cascade event candidates at PeV energies will not be accompanied by corresponding muon tracks.Comment: 20 pages, 6 figures, 1 table. Matches published versio

Effects of Peer-education on Quality of Life in Adults with Type 2 Diabetes

Aims: Diabetes is the most prevalent metabolic disease in human being. Self-care is the most important way of preventing complications. This study aimed at investigating the effects of peer-education on quality of life in adult patients with type 2 diabetes. Materials & Methods: This semi experimental study was conducted at a diabetes clinic affiliated to Gonabad University of medical sciences, Iran in 2017 among 80 patients with type 2 diabetes. Patients were selected based on available sampling method, and they were randomly divided into two groups, namely intervention and control (40 patients each group). The data of all patients were collected by demographic and disease information questionnaire and diabetic patient quality of life (QOL) questionnaire. The present study was carried out in three main steps: In the first step (before intervention), peers were trained by the researcher during four sessions. In the second step (intervention), quality of life of patients was assessed before training; then, patients in intervention group were trained and instructed during three sessions; the control group received the usual instruction, too. In the third step (one month later), quality of life of patients in both groups was assessed. The data were analyzed by SPSS statistics software Version 20, using Chi-square, Fisher, independent t test, paired t test, Mann-Whitney U, and Wilcoxon. Findings: The mean scores of quality of life in intervention group did not have any significant difference with control group before instruction (p>0.05). After instruction, the mean scores of quality of life in the intervention group compared with the control group increased significantly (p<0.001). Conclusion: Peer education improves quality of life in adult patients with type 2 diabetes