Wind Data From Radar Echoes

Series statement on cover: "Technical Report no. 1"Listed in ISWS Publications Catalog (1995, p. 14) as Contract Report no. 1, with the series statement Technical Report no. 1 included in the citation.published or submitted for publicationis peer reviewedOpe

An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival.

BackgroundIn 2016, the World Health Organization reclassified the definition of glioblastoma (GBM), dividing these tumors into isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant GBM, where the vast majority of GBMs are IDH-wild-type. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogram for IDH-wild-type patients with newly diagnosed GBM.MethodsData were obtained from newly diagnosed GBM patients from the Ohio Brain Tumor Study (OBTS) and the University of California San Francisco (UCSF) for diagnosis years 2007-2017 with the following variables: age at diagnosis, sex, extent of resection, concurrent radiation/temozolomide (TMZ) status, Karnofsky Performance Status (KPS), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and IDH mutation status. Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the OBTS data and independently validated using the UCSF data. Models were internally validated using 10-fold cross-validation and externally validated by plotting calibration curves.ResultsA final nomogram was validated for IDH-wild-type newly diagnosed GBM. Factors that increased the probability of survival included younger age at diagnosis, female sex, having gross total resection, having concurrent radiation/TMZ, having a high KPS, and having MGMT methylation.ConclusionsA nomogram that calculates individualized survival probabilities for IDH-wild-type patients with newly diagnosed GBM could be useful to physicians for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: https://gcioffi.shinyapps.io/Nomogram_For_IDH_Wildtype_GBM_H_Gittleman/

Moisture content analysis of wooden bridges

The article deals with assessing the impact of moisture content conditions in wood mass of the wood bridges constructions on their lifespan in Central Europe. Wood moisture content as one of main factors influencing the wooden elements mechanical properties was studied on seventeen wooden bridge constructions. The dependence of temperature and relative humidity on material moisture content was observed in summer season and also in winter season. The lifespan of historical and modern wood structures was discussed as well.Web of Science64354453

Survival nomograms after curative neoadjuvant chemotherapy and radical surgery for stage IB2-IIIB cervical cancer

PURPOSE: The purpose of this study was to develop nomograms for predicting the probability of overall survival (OS) and progression-free survival (PFS) in locally advanced cervical cancer treated with neoadjuvant chemotherapy and radical surgery. MATERIALS AND METHODS: Nomograms to predict the 5-year OS rates and the 2-year PFS rates were constructed. Calibration plots were constructed, and concordance indices were calculated. Evaluated variableswere body mass index, age, tumor size, tumor histology, grading, lymphovascular space invasion, positive parametria, and positive lymph nodes. RESULTS: In total 245 patients with locally advanced cervical cancer who underwent neoadjuvant chemotherapy and radical surgery were included for the construction of the nomogram. The 5-year OS and PFS were 72.6% and 66%, respectively. Tumor size, grading, and parametria status affected the rate of OS, whereas tumor size and positive parametria were the main independent PFS prognostic factors. CONCLUSION: We constructed a nomogram based on clinicopathological features in order to predict 2-year PFS and 5-year OS in locally advanced cervical cancer primarily treated with neoadjuvant chemotherapy followed by radical surgery. This tool might be particularly helpful for assisting in the follow-up of cervical cancer patients who have not undergone concurrent chemoradiotherapy

Patients With Kidney Cancer

To develop a preoperative prognostic model in order to predict recurrence-free survival in patients with nonmetastatic kidney cancer.A multi-institutional data base of 1889 patients who underwent surgical resection between 1987 and 2007 for kidney cancer was retrospectively analyzed. Preoperative variables were defined as age, gender, presentation, size, presence of radiological lymph nodes and clinical stage. Univariate and multivariate analyses of the variables were performed using the Cox proportional hazards regression model. A model was developed with preoperative variables as predictors of recurrence after nephrectomy. Internal validation was performed by Harrells concordance index.The median follow-up was 23.6 months (1222 months). During the follow-up, 258 patients (13.7) developed cancer recurrence. The median follow-up for patients who did not develop recurrence was 25 months. The median time from surgery to recurrence was 13 months. The 5-year freedom from recurrence probability was 78.6. All variables except age were associated with freedom from recurrence in multivariate analyses (P 0.05). Age was marginally significant in the univariate analysis. All variables were included in the predictive model. The calculated c-index was 0.747.This preoperative model utilizes easy to obtain clinical variables and predicts the likelihood of development of recurrent disease in patients with kidney tumors

Аналіз впливу маси пасажира на модель ідентифікації злітної ваги та центрівки літака

This article provides a new model of the take-off weight and balance of the aircraft determining process, as well as the weight distribution of passengers’ law. These materials allow a simulation of one of the main operations in the preparation for departure process. Зменшити вплив похибок оцінювання ваги та центрівки літака методів завантажувального графіка на результат прийняття рішення про виліт. У статті здійснено визначення змісту технологічних операцій при використанні завантажувального графіка літака для визначення його злітної ваги та центрівки. На цій основі розроблена імітаційна модель технологічного процесу яка дозволить визначити апостеріорні ймовірності прийняття хибного рішення. Доведено, що закон розподілу мас людини наближається до нормального та визначені його параметри. В статті показано, що поява помилок першого та другого роду при прийнятті рішення про можливість вильоту пов’язана перш за все з неточностями виміру первинних параметрів та похибками у графічних побудовах

Shuttle landing facility cloud cover study: Climatological analysis and two tenths cloud cover rule evaluation

The two-tenths cloud cover rule in effect for all End Of Mission (EOM) STS landings at the Kennedy Space Center (KSC) states: 'for scattered cloud layers below 10,000 feet, cloud cover must be observed to be less than or equal to 0.2 at the de-orbit burn go/no-go decision time (approximately 90 minutes before landing time)'. This rule was designed to protect against a ceiling (below 10,000 feet) developing unexpectedly within the next 90 minutes (i.e., after the de-orbit burn decision and before landing). The Applied Meteorological Unit (AMU) developed and analyzed a database of cloud cover amounts and weather conditions at the Shuttle Landing Facility for a five-year (1986-1990) period. The data indicate the best time to land the shuttle at KSC is during the summer while the worst time is during the winter. The analysis also shows the highest frequency of landing opportunities occurs for the 0100-0600 UTC and 1300-1600 UTC time periods. The worst time of the day to land a shuttle is near sunrise and during the afternoon. An evaluation of the two-tenths cloud cover rule for most data categorizations has shown that there is a significant difference in the proportions of weather violations one and two hours subsequent to initial conditions of 0.2 and 0.3 cloud cover. However, for May, Oct., 700 mb northerly wind category, 1500 UTC category, and 1600 UTC category there is some evidence that the 0.2 cloud cover rule may be overly conservative. This possibility requires further investigation. As a result of these analyses, the AMU developed nomograms to help the Spaceflight Meteorological Group (SMG) and the Cape Canaveral Forecast Facility (CCFF) forecast cloud cover for EOM and Return to Launch Site (RTLS) at KSC. Future work will include updating the two tenths database, further analysis of the data for several categorizations, and developing a proof of concept artificial neural network to provide forecast guidance of weather constraint violations for shuttle landings

Do frailty and comorbidity indices improve risk prediction of 28-day ED reattendance? Reanalysis of an ED discharge nomogram for older people

Background: In older people, quantification of risk of reattendance after emergency department (ED) discharge is important to provide adequate post ED discharge care in the community to appropriately targeted patients at risk. Methods: We reanalysed data from a prospective observational study, previously used for derivation of a nomogram for stratifying people aged 65 and older at risk for ED reattendance. We investigated the potential effect of comorbidity load and frailty by adding the Charlson or Elixhauser comorbidity index and a ten-item frailty measure from our data to develop four new nomograms. Model I and model F built on the original nomogram by including the frailty measure with and without the addition of the Charlson comorbidity score; model E adapted for efficiency in the time-constrained environment of ED was without the frailty measure; and model P manually constructed in a purposeful stepwise manner and including only statistically significant variables. Areas under the ROC curve of models were compared. The primary outcome was any ED reattendance within 28 days of discharge. Results: Data from 1357 patients were used. The point estimate of the respective areas under ROC were 0.63 (O), 0.63 (I), 0.68 (E), 0.71 (P) and 0.63 (F). Conclusion: Addition of a comorbidity index to our previous model improves stratifying elderly at risk of ED reattendance. Our frailty measure did not demonstrate any additional predictive benefit

Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

ObjectivesEvaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children's demographic information (7 questions) to predict the child's oral health status and need for treatment.MethodsData were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity.ResultsThe toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile).ConclusionsSurvey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits' predicted oral health scores can be used to stratify samples in oral health research.Knowledge transfer statementThis study creates the oral health toolkits that combine self- and proxy- reported short forms with children's demographic characteristics to predict children's oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status

Urinary CE-MS peptide marker pattern for detection of solid tumors

Urinary profiling datasets, previously acquired by capillary electrophoresis coupled to mass-spectrometry were investigated to identify a general urinary marker pattern for detection of solid tumors by targeting common systemic events associated with tumor-related inflammation. A total of 2,055 urinary profiles were analyzed, derived from a) a cancer group of patients (n = 969) with bladder, prostate, and pancreatic cancers, renal cell carcinoma, and cholangiocarcinoma and b) a control group of patients with benign diseases (n = 556), inflammatory diseases (n = 199) and healthy individuals (n = 331). Statistical analysis was conducted in a discovery set of 676 cancer cases and 744 controls. 193 peptides differing at statistically significant levels between cases and controls were selected and combined to a multi-dimensional marker pattern using support vector machine algorithms. Independent validation in a set of 635 patients (293 cancer cases and 342 controls) showed an AUC of 0.82. Inclusion of age as independent variable, significantly increased the AUC value to 0.85. Among the identified peptides were mucins, fibrinogen and collagen fragments. Further studies are planned to assess the pattern value to monitor patients for tumor recurrence. In this proof-of-concept study, a general tumor marker pattern was developed to detect cancer based on shared biomarkers, likely indicative of cancer-related features