Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.Comment: 15 pages, 13 figures, submitted to IEEE Transactions on Wireless Communication

Report from ‘ESCP 2021 Virtual’: the 16th Scientific and Annual Conference of the European Society of Coloproctology, 22–24 September 2021

Coloproctology; ConferenceColoproctologia; ConferènciaColoproctología; ConferenciaOpen Access Funding provided by Universita degli Studi della Campania Luigi Vanvitelli within the CRUI-CARE Agreement

Report from 'ESCP 2021 Virtual': the 16th Scientific and Annual Conference of the European Society of Coloproctology, 22-24 September 2021

5MAY652-6562

Screening policies, preventive measures and in-hospital infection of COVID-19 in global surgical practices

none14siThis research was funded in part by the European Society of Degenerative Disease. The study was registered with an analysis plan on ClinicalTrials.gov (NCT04344197).Background In a surgical setting, COVID-19 patients may trigger in-hospital outbreaks and have worse postoperative outcomes. Despite these risks, there have been no consistent statements on surgical guidelines regarding the perioperative screening or management of COVID-19 patients, and we do not have objective global data that describe the current conditions surrounding this issue. This study aimed to clarify the current global surgical practice including COVID-19 screening, preventive measures and in-hospital infection under the COVID-19 pandemic, and to clarify the international gaps on infection control policies among countries worldwide.Methods During April 2-8, 2020, a cross-sectional online survey on surgical practice was distributed to surgeons worldwide through international surgical societies, social media and personal contacts. Main outcome and measures included preventive measures and screening policies of COVID-19 in surgical practice and centers' experiences of in-hospital COVID-19 infection. Data were analyzed by country's cumulative deaths number by April 8, 2020 (high risk, >5000; intermediate risk, 100-5000; low risk, <100).Results A total of 936 centers in 71 countries responded to the survey (high risk, 330 centers; intermediate risk, 242 centers; low risk, 364 centers). In the majority (71.9%) of the centers, local guidelines recommended preoperative testing based on symptoms or suspicious radiologic findings. Universal testing for every surgical patient was recommended in only 18.4% of the centers. In-hospital COVID-19 infection was reported from 31.5% of the centers, with higher rates in higher risk countries (high risk, 53.6%; intermediate risk, 26.4%; low risk, 14.8%; P<0.001). Of the 295 centers that experienced in-hospital COVID-19 infection, 122 (41.4%) failed to trace it and 58 (19.7%) reported the infection originating from asymptomatic patients/staff members. Higher risk countries adopted more preventive measures including universal testing, routine testing of hospital staff and use of dedicated personal protective equipment in operation theatres, but there were remarkable discrepancies across the countries.Conclusions This large international survey captured the global surgical practice under the COVID-19 pandemic and highlighted the insufficient preoperative screening of COVID-19 in the current surgical practice. More intensive screening programs will be necessary particularly in severely affected countries/institutions.openBellato, Vittoria; Konishi, Tsuyoshi; Pellino, Gianluca; An, Yongbo; Piciocchi, Alfonso; Sensi, Bruno; Siragusa, Leandro; Khanna, Krishn; Pirozzi, Brunella Maria; Franceschilli, Marzia; Campanelli, Michela; Efetov, Sergey; Sica, Giuseppe S; Feo, C;Bellato, Vittoria; Konishi, Tsuyoshi; Pellino, Gianluca; An, Yongbo; Piciocchi, Alfonso; Sensi, Bruno; Siragusa, Leandro; Khanna, Krishn; Pirozzi, Brunella Maria; Franceschilli, Marzia; Campanelli, Michela; Efetov, Sergey; Sica, Giuseppe S; Feo,

Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique

Objectives/Hypothesis: To develop a deep-learning–based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P <.001), polyps (91% vs. 86%, P <.001), leukoplakia (91% vs. 65%, P <.001), and malignancy (90% vs. 54%, P <.001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. Level of Evidence: NA Laryngoscope, 130:E686–E693, 2020

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Effects of David Deer Grazing on Soil Bacterial and Fungal Communities in an Eastern Coastal Wetland of China

The grazing activity of animals has a significant role on the environmental modification of land. In the coastal wetlands of eastern China, long-term David deer (Elaphurus davidianus) grazing has caused the degradation of various ecological elements in the area. Still, few studies have been reported concerning the effects of David deer grazing on the soil microorganisms of their habitats. We analyzed the community structure of soil bacteria and fungi in an area of continuous annual grazing and another area without traces of David deer grazing so as to learn about the effects of deer grazing on the soil microbial community structure in a spatial instead of temporal way, in preparation for improving the environment for deer survival. David deer grazing drastically changed the physicochemical characteristics of the soil, accelerating the alkalinization process and inhibiting the buildup of nutrients. There were differences in the microbial community structure between the grazed and the control areas, with bacteria predominating. The control had a higher level of bacterial and fungal alpha-diversity than the grazed area. The makeup of the soil’s microbial community was also influenced, except for the dominant microbial at the phylum level. In addition to the establishment of numerous complex fungal functional types, David deer grazing increased the number of bacterial functional types linked to the carbon cycle. The impacts of soil pH and urease activity on bacterial and fungi populations were highlighted using the redundancy analysis. This study demonstrates that David deer grazing changes and complicates microbial functional kinds of composition, as well as modifies the composition of the soil’s microbial community, improving the soil nutrient cycling process, mainly the carbon element