An Optimized Uncertainty-Aware Training Framework for Neural Networks

Uncertainty quantification (UQ) for predictions generated by neural networks (NNs) is of vital importance in safety-critical applications. An ideal model is supposed to generate low uncertainty for correct predictions and high uncertainty for incorrect predictions. The main focus of state-of-the-art training algorithms is to optimize the NN parameters to improve the accuracy-related metrics. Training based on uncertainty metrics has been fully ignored or overlooked in the literature. This article introduces a novel uncertainty-aware training algorithm for classification tasks. A novel predictive uncertainty estimate-based objective function is defined and optimized using the stochastic gradient descent method. This new multiobjective loss function covers both accuracy and uncertainty accuracy (UA) simultaneously during training. The performance of the proposed training framework is compared from different aspects with other UQ techniques for different benchmarks. The obtained results demonstrate the effectiveness of the proposed framework for developing the NN models capable of generating reliable uncertainty estimates.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Determining the Z Scores of Children Covered by Mashhad University of Medical Sciences and Comparing it with the WHO and CDC Standards

Background: The World Health Organization has introduced two sets of child growth standards for growth assessment. These reference values may not be suitable for use in other populations. Therefore, this study aimed to determine specific Z scores in the population covered by Mashhad University of Medical Sciences in Iran.Methods: This cross-sectional study was conducted on data obtained from the evaluation of height, weight, and head circumference of children aged from 0 to 18 months visiting the healthcare centers of Mashhad University of Medical Sciences from March 2018 to March 2021. A total data of 128,472 children were extracted from the Electronic Health Records (SinaEHR®) and included in the study. Finally, the collected data were analyzed using Minitab and SPSS software (version 16).  Results: The L, M, and S parameters were used to calculate Z scores for weight, height, and head circumference. These Z scores were then compared to standard deviation values ​for each age from our study and international standards to determine any differences. Our study found that mean weight scores were 0.16 kg higher than the CDC standard and 0.34 kg higher than the WHO growth standard.  Conclusion: The provision of this exclusive reference to children's growth indicators not only allows for a more accurate evaluation but also provides the possibility of comparison with other populations using their specific growth charts. It seems that one of the best plans is to compare growth charts with international populations and national growth charts, which due to the electronization of the entire processes of the health system, is more possible than ever

Indicators of Quality of Care in Individuals With Traumatic Spinal Cord Injury: A Scoping Review

Study Design: Scoping review. Objectives: To identify a practical and reproducible approach to organize Quality of Care Indicators (QoCI) in individuals with traumatic spinal cord injury (TSCI). Methods: A comprehensive literature review was conducted in the Cochrane Central Register of Controlled Trials (CENTRAL) (Date: May 2018), MEDLINE (1946 to May 2018), and EMBASE (1974 to May 2018). Two independent reviewers screened 6092 records and included 262 full texts, among which 60 studies were included for qualitative analysis. We included studies, with no language restriction, containing at least 1 quality of care indicator for individuals with traumatic spinal cord injury. Each potential indicator was evaluated in an online, focused group discussion to define its categorization (healthcare system structure, medical process, and individuals with Traumatic Spinal Cord Injury related outcomes), definition, survey options, and scale. Results: A total of 87 indicators were identified from 60 studies screened using our eligibility criteria. We defined each indicator. Out of 87 indicators, 37 appraised the healthcare system structure, 30 evaluated medical processes, and 20 included individuals with TSCI related outcomes. The healthcare system structure included the impact of the cost of hospitalization and rehabilitation, as well as staff and patient perception of treatment. The medical processes included targeting physical activities for improvement of health-related outcomes and complications. Changes in motor score, functional independence, and readmission rates were reported as individuals with TSCI-related outcomes indicators. Conclusion: Indicators of quality of care in the management of individuals with TSCI are important for health policy strategists to standardize healthcare assessment, for clinicians to improve care, and for data collection efforts including registries

Inferring interaction type in gene regulatory networks using co-expression data

BACKGROUND: Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. RESULTS: This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. CONCLUSIONS: SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-015-0054-4) contains supplementary material, which is available to authorized users

Anti-entzünliche Wirkung von Milch bei Makrophagen

Zielsetzung: Milch und fermentierte Milchprodukte wirken entzündungshemmend auf Darmzellen. Die Rolle von Muttermilch und Kuhmilch bei der Polarisation von RAW 264.7 Makrophagenzellen wurde bisher nicht beobachtet. Hintergrund der vorliegende Studie war es im Hinblick auf die orale Mundgesundheit die Wirkung von Kuhmilch und Muttermilch auf die Beeinflussung der Entzündungsreaktionen zu analysieren. Material und Methode: Um dieses Ziel zu erreichen, wurden hier RAW 264.7 Makrophagenzellen mit Speichel, IL1 und TNF und IL4 inkubiert, um einen M1- und M2-Phänotypen mit einem wässrigen Anteil an Kuhmilch und Muttermilch zu erzeugen Die Genexpression der M1-Gene (IL1 und IL8) und der M2-Gene (ARG1 und YM1) mit der positiven Kontrolle IL4 wurden durch RT-PCR nachgewiesen. ELISA wurde für IL1 verwendet. Ergebnisse: Die erhaltenen RT-PCR-Ergebnisse zeigen, dass Milch (Kuhmilch und erhitzte Muttermilch) die provozierte Entzündung in Makrophagenzellen durch Verringerung der genetischen Expression von M1-Genen (IL1 und IL8) gesenkt hat. Wenn Muttermilch oder Kuhmilch hinzugefügt wurden (beides in pasteurisiertem Zustand), kam es zu einer signifikanten Reduktion der Entzündungsantwort. Darüber hinaus wurde die genetische Expression von M2-Genen (ARG1, YM1) mit positiver Kontrolle der IL4 durch Zusatz von Milch (Kuhmilch und erhitzte Muttermilch) erhöht, was die entzündungshemmende Wirkung von Milch bei Makrophagen bestätigt. Die erhaltenen ELISA-Ergebnisse haben gezeigt, dass die IL1-Konzentration im Überstand durch Zusatz von erhitzter Muttermilch verringert wurde, was auch die entzündungshemmende Wirkung von Milch auf die RAW264.7 Makrophagen bestätigt. Schlussfolgerung: Milch bewirkt eine Verschiebung der Makrophagen von M1 in Richtung M2-Phänotyp in vitro.2. Abstract Objective: Milk and fermented milk products have an anti-inflammatory effect on intestinal cells. The role of human milk and cows milk in the polarization of RAW 264.7 macrophage cells has not previously been reported. The background of the present study was to analyze the effects of cows milk and human milk on the effects of inflammatory reactions on oral health. Material and Methods: To achieve this goal, RAW 264.7 macrophage cells were incubated with saliva, IL1 and TNF and IL4 to produce M1 and M2 phenotypes with an aqueous content of cow's milk and heated Mother milk Gene expression of M1 genes (IL1 and IL8) and the M2 genes (ARG1 and YM1) with the positive control IL4 were detected by RT-PCR. ELISA was used for IL1. Results: The obtained RT-PCR results show that milk (cow's milk and heated Mother milk) has lowered the provoked inflammation in macrophage cells by reducing the genetic expression of M1 genes (IL1 and IL8). When Mother milk or cow's milk was added (both in pasteurized state), there was a significant reduction in the inflammatory response. In addition, the genetic expression of M2 genes (ARG1, YM1) with positive control of IL4 was increased by the addition of milk (cow's milk and heated Mother milk), confirming the anti-inflammatory effect of milk on macrophages. The ELISA results obtained showed that the IL1 concentration in the supernatant was reduced by the addition of heated breast milk, which also confirmed the anti-inflammatory effect of milk on the RAW264.7 macrophages. Conclusion: Milk causes a shift of macrophages from M1 towards the M2 phenotype in vitro.Paralleltitel laut Übersetzung des VerfassersMedizinische Universität Wien, Diplomarbeit, 2019(VLID)336474