Stretching Method-Based Operational Modal Analysis of An Old Masonry Lighthouse.

We present in this paper a structural health monitoring study of the Egyptian lighthouse of Rethymnon in Crete, Greece. Using structural vibration data collected on a limited number of sensors during a 3-month period, we illustrate the potential of the stretching method for monitoring variations in the natural frequencies of the structure. The stretching method compares two signals, the current that refers to the actual state of the structure, with the reference one that characterizes the structure at a reference healthy condition. For the structure under study, an 8-day time interval is used for the reference quantity while the current quantity is computed using a time window of 24 h. Our results indicate that frequency shifts of 1% can be detected with high accuracy allowing for early damage assessment. We also provide a simple numerical model that is calibrated to match the natural frequencies estimated using the stretching method. The model is used to produce possible damage scenarios that correspond to 1% shift in the first natural frequencies. Although simple in nature, this model seems to deliver a realistic response of the structure. This is shown by comparing the response at the top of the structure to the actual measurement during a small earthquake. This is a preliminary study indicating the potential of the stretching method for structural health monitoring of historical monuments. The results are very promising. Further analysis is necessary requiring the deployment of the instrumentation (possibly with additional instruments) for a longer period of time

Characterization of methyl-, 3-deoxy-, and methyl-deoxysugars in marine high molecular weight dissolved organic matter

Author Posting. © Elsevier B.V., 2007. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Organic Geochemistry 38 (2007): 884-896, doi:10.1016/j.orggeochem.2007.02.005.Nuclear magnetic resonance spectroscopy of marine high molecular weight dissolved organic matter (HMWDOM) in surface waters show that >50% of the carbon is a compositionally well-defined family of acylated-polysaccharides that are conserved across ocean basins. However, acid hydrolysis of HMWDOM followed by chromatographic analyses recover only 10-20% of the carbon as neutral, amino, and acidic sugars. Most carbohydrate in HMWDOM therefore remains uncharacterized. Here we use acid hydrolysis followed by Ag+ and Pb2+ cation exchange chromatography to separate HMWDOM hydrolysis products for characterization by 1-D and 2-D NMR spectroscopy. In addition to neutral sugars identified in past studies, we find 3-Omethylglucose, 3-O-methylrhamnose, 2-O-methylrhamnose and 2-O-methylfucose. We also find 3-deoxysugars to be present, although their complete structures could not be determined. Methyl sugars are widely distributed in plant and bacterial structural carbohydrates, such as cell wall polysaccharides, and their presence in HMWDOM suggests that structural carbohydrates may contribute to DOM in surface seawater. We find most HMWDOM carbohydrate is not depolymerized by acid hydrolysis, and that the nonhydrolyzable component includes 6-deoxysugars.Funding was provided by the Ocean Carbon Sequestration Research Program, Biological and Environmental Research (BER), U.S. Department of Energy grant DEFG0200ER62999 and the National Sciences Foundation Chemical Oceanography Program grant OCE 9818654. Christos Panagiotopoulos received support through the Postdoctoral Fellowship Program of the Woods Hole Oceanographic Institution, and DJR received support through the Stanley Watson Chair in Oceanography

Cavernous Malformations of the Central Nervous System: An International Consensus Statement

Introduction: Cavernous malformations (CM) of the central nervous system constitute rare vascular lesions. They are usually asymptomatic, which has allowed their management to become quite debatable. Even when they become symptomatic their optimal mode and timing of treatment remains controversial. Research question: A consensus may navigate neurosurgeons through the decision-making process of selecting the optimal treatment for asymptomatic and symptomatic CMs. Material and methods: A 17-item questionnaire was developed to address controversial issues in relation to aspects of the treatment, surgical planning, optimal surgical strategy for specific age groups, the role of stereotactic radiosurgery, as well as a follow-up pattern. Consequently, a three-stage Delphi process was ran through 19 invited experts with the goal of reaching a consensus. The agreement rate for reaching a consensus was set at 70%. Results: A consensus for surgical intervention was reached on the importance of the patient’s age, symptomatology, and hemorrhagic recurrence; and the CM’s location and size. The employment of advanced MRI techniques is considered of value for surgical planning. Observation for asymptomatic eloquent or deep-seated CMs represents the commonest practice among our panel. Surgical resection is considered when a deep-seated CM becomes symptomatic or after a second bleeding episode. Asymptomatic, image-proven hemorrhages constituted no indication for surgical resection for our panelists. Consensus was also reached on not resecting any developmental venous anomalies, and on resecting the associated hemosiderin rim only in epilepsy cases. Discussion and conclusion: Our Delphi consensus provides an expert common practice for specific controversial issues of CM patient management

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14