Equitability revisited: why the “equitable threat score” is not equitable

In the forecasting of binary events, verification measures that are “equitable” were defined by Gandin and Murphy to satisfy two requirements: 1) they award all random forecasting systems, including those that always issue the same forecast, the same expected score (typically zero), and 2) they are expressible as the linear weighted sum of the elements of the contingency table, where the weights are independent of the entries in the table, apart from the base rate. The authors demonstrate that the widely used “equitable threat score” (ETS), as well as numerous others, satisfies neither of these requirements and only satisfies the first requirement in the limit of an infinite sample size. Such measures are referred to as “asymptotically equitable.” In the case of ETS, the expected score of a random forecasting system is always positive and only falls below 0.01 when the number of samples is greater than around 30. Two other asymptotically equitable measures are the odds ratio skill score and the symmetric extreme dependency score, which are more strongly inequitable than ETS, particularly for rare events; for example, when the base rate is 2% and the sample size is 1000, random but unbiased forecasting systems yield an expected score of around −0.5, reducing in magnitude to −0.01 or smaller only for sample sizes exceeding 25 000. This presents a problem since these nonlinear measures have other desirable properties, in particular being reliable indicators of skill for rare events (provided that the sample size is large enough). A potential way to reconcile these properties with equitability is to recognize that Gandin and Murphy’s two requirements are independent, and the second can be safely discarded without losing the key advantages of equitability that are embodied in the first. This enables inequitable and asymptotically equitable measures to be scaled to make them equitable, while retaining their nonlinearity and other properties such as being reliable indicators of skill for rare events. It also opens up the possibility of designing new equitable verification measures

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient’s survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient’s likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises

Neuroprotective Effects of Bone Marrow Mesenchymal Stem Cells on Bilateral Common Carotid Arteries Occlusion Model of Cerebral Ischemia in Rat

Cell therapy is the most advanced treatment of the cerebral ischemia, nowadays. Herein, we discuss the neuroprotective effects of bone marrow mesenchymal stem cells (BMSCs) on rat hippocampal cells following intravenous injection of these cells in an ischemia-reperfusion model. Adult male Wistar rats were divided into 5 groups: control, sham (surgery without blockage of common carotid arteries), ischemia (common carotid arteries were blocked for 30 min prior to reperfusion), vehicle (7 days after ischemia PBS was injected via the tail vein), and treatment (injections of BMSC into the tail veins 7 days after ischemia). We performed neuromuscular and vestibulomotor function tests to assess behavioral function and, finally, brains were subjected to hematoxylin and eosin (H&E), anti-Brdu immunohistochemistry, and TUNEL staining. The ischemia group had severe apoptosis. The group treated with BMSCs had a lower mortality rate and also had significant improvement in functional recovery (P<0.001). Ischemia-reperfusion for 30 min causes damage and extensive neuronal death in the hippocampus, especially in CA1 and CA3 regions, leading to several functional and neurological deficits. In conclusion, intravenous injection of BMSCs can significantly decrease the number of apoptotic neurons and significantly improve functional recovery, which may be a beneficial treatment method for ischemic injuries. © 2016 Bagher Pourheydar et al

Factors Associated With Intraventricular Hemorrhage in Very Low Birth Weight Neonates in Mousavi Hospital in Zanjan in 2012-2013

Background: Intraventricular hemorrhage (IVH) is one of the most important causes of cognitive and motor disorder in children with very low birth weight and is associated with high mortality and disability rate. Objectives: The aim of this study was to determine IVH risk factors in the first days of life in neonates weighing less than 1500g (VLBW) so that the results can contribute to the improvement of the therapeutic function of the delivery room and ultimately IVH risk prevention. Methods: This descriptive study was conducted on 110 VLBW neonates who were admitted to the hospital affiliated to Zanjan University of Medical Sciences during the years 2012-2013 Zanjan-Iran. Parameters including gender, birth weight, birth Apgar, regimens, and type of delivery were recorded in the questionnaire and data analysis was conducted using Chi-square test in SPSS. Results: From 110 studied neonates, 21(19%) had IVH, of which 11(52%), 5(23.8%) and 5(23.8%) suffered from Grade I, II and III IVH, respectively. Meanwhile, among the studied variables, recovery steps were taken in the delivery room in the IVH group. The cranial ultrasonography was carried out for these neonates in the first 72 h of birth and they were categorized as Grade one to four, based on evidence of brain hemorrhage. There was a significant difference between maternity and infant information and without IVH; but it was not statistically significant. Conclusion: According to the present study, the recovery process seemed to be a risk factor for the incidence of IVH in neonates; therefore, the health level of neonates can be improved by optimizing the mentioned process and reducing this risk factor

Experimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content

The hydrothermal carbonization (HTC) process has been found to consistently improve biomass fuel characteristics by raising the higher heating value (HHV) of the hydrochar as process severity is increased. However, this is usually associated with a decrease in the solid yield (SY) of hydrochar, making it difficult to determine the optimal operating conditions to obtain the highest energy yield (EY), which combines the two parameters. In this study, a graph-based genetic programming (GP) method was used for developing correlations to predict HHV, SY, and EY for hydrochars based on published values from 42 biomasses and a broad range of HTC experimental systems and operating conditions, i.e., 5 ≤ holding time (min) ≤ 2208, 120 ≤ temperature (°C) ≤ 300, and 0. 0096 ≤ biomass to water ratio ≤ 0.5. In addition, experiments were carried out with 5 pomaces at 4 temperatures and two reactor scales, 1 L and 18.75 L. The correlations were evaluated using this experimental data set in order to estimate prediction errors in similar experimental systems. The use of the correlations to predict HTC conditions to achieve the maximum EY is demonstrated for three common feedstocks, wheat straw, sewage sludge, and a fruit pomace. The prediction was confirmed experimentally with pomace at the optimized HTC conditions; we observed 6.9 % error between the measured and predicted EY %. The results show that the correlations can be used to predict the optimal operating conditions to produce hydrochar with the desired fuel characteristics with a minimum of actual HTC runs

Small Interfering RNA–Mediated Suppression of Proislet Amyloid Polypeptide Expression Inhibits Islet Amyloid Formation and Enhances Survival of Human Islets in Culture

OBJECTIVE—Islet amyloid, formed by aggregation of the β-cell peptide islet amyloid polypeptide (IAPP; amylin), is a pathological characteristic of pancreatic islets in type 2 diabetes. Toxic IAPP aggregates likely contribute to the progressive loss of β-cells in this disease. We used cultured human islets as an ex vivo model of amyloid formation to investigate whether suppression of proIAPP expression would inhibit islet amyloid formation and enhance β-cell survival and function

Changes in Selected Organic and Inorganic Compounds in the Hydrothermal Carbonization Process Liquid While in Storage

Although many studies have investigated the hydrothermal transformation of feedstock biomass, little is known about the stability of the compounds present in the process liquid after the carbonization process is completed. The physicochemical characteristics of hydrothermal carbonization (HTC) liquid products may change over storage time, diminishing the amount of desired products or producing unwanted contaminants. These changes may restrict the use of HTC liquid products. Here, we investigate the effect of storage temperature (20, 4, and −18 °C) and time (weeks 1-12) on structural and compositional changes of selected organic compounds and physicochemical characteristics of the process liquid from the HTC of digested cow manure. ANOVA showed that the storage time has a significant effect on the concentrations of almost all of the selected organic compounds, except acetic acid. Considerable changes in the composition of the process liquid took place at all studied temperatures, including deep freezing at −18 °C. Prominent is the polymerization of aromatic compounds with the formation of precipitates, which settle over time. This, in turn, influences the inorganic compounds present in the liquid phase by chelating or selectively adsorbing them. The implications of these results on the further processing of the process liquid for various applications are discussed

Methods and pharmaceutical compositions for the cardioprotection

The present invention relates to methods and pharmaceutical compositions for cardioprotection of subjects who experienced a myocardial infarction. In particular, the present invention relates to a ligand of the sonic hedgehog signaling pathway for use in the cardioprotection of a subject who experienced a myocardial infarction

One-loop Vilkovisky-DeWitt Counterterms for 2D Gravity plus Scalar Field Theory

The divergent part of the one-loop off-shell effective action is computed for a single scalar field coupled to the Ricci curvature of 2D gravity ($c \phi R$), and self interacting by an arbitrary potential term $V(\phi)$. The Vilkovisky-DeWitt effective action is used to compute gauge-fixing independent results. In our background field/covariant gauge we find that the Liouville theory is finite on shell. Off-shell, we find a large class of renormalizable potentials which include the Liouville potential. We also find that for backgrounds satisfying $R=0$, the Liouville theory is finite off shell, as well.Comment: 19 pages, OKHEP 92-00

Extremal Dependence Indices: improved verification measures for deterministic forecasts of rare binary events

Copyright © 2011 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or [email protected] forecasts of rare events is challenging, in part because traditional performance measures degenerate to trivial values as events become rarer. The extreme dependency score was proposed recently as a nondegenerating measure for the quality of deterministic forecasts of rare binary events. This measure has some undesirable properties, including being both easy to hedge and dependent on the base rate. A symmetric extreme dependency score was also proposed recently, but this too is dependent on the base rate. These two scores and their properties are reviewed and the meanings of several properties, such as base-rate dependence and complement symmetry that have caused confusion are clarified. Two modified versions of the extreme dependency score, the extremal dependence index, and the symmetric extremal dependence index, are then proposed and are shown to overcome all of its shortcomings. The new measures are nondegenerating, base-rate independent, asymptotically equitable, harder to hedge, and have regular isopleths that correspond to symmetric and asymmetric relative operating characteristic curves