Comparative evaluation of a novel, moderately hypofractionated radiation protocol in 56 dogs with symptomatic intracranial neoplasia

BACKGROUND: Use of strongly hypofractionated radiation treatments in dogs with intracranial neoplasia did not improve outcomes and yielded increased rates of toxicosis. OBJECTIVES: To evaluate safety and efficacy of a new, moderately hypofractionated radiation protocol of 10 × 4 Gy compared to a standard protocol. ANIMALS: Convenience sample of 56 client-owned dogs with primary symptomatic brain tumors. METHODS: Retrospective observational study. Twenty-six dogs were assigned to the control standard protocol of 20 × 2.5 Gy (group A) and 30 dogs to the new protocol of 10 × 4 Gy (group B), assigned on owners' informed consent. Statistical analysis was conducted under the "as treated" regime, using Kaplan-Meier and Cox-regression analysis. Treatment was delivered with technically advanced image-guided radiation therapy. The 2 treatment groups were compared in terms of outcome and signs of toxicosis. RESULTS: Overall progression-free interval (PFI) and overall survival (OS) time were favorable, with 663 (95%CI: 497;828) and 637 (95%CI: 403;870) days, respectively. We found no significant difference between the two groups: PFI for dogs in group A vs B was 608 (95%CI: 437;779) days and mean (median not reached) 863 (95%CI: 644;1083) days, respectively (P = .89), and OS for dogs in group A vs B 610 (95%CI: 404;816) and mean (median not reached) 796 (95%CI: 586;1007) days (P = .83). CONCLUSION AND CLINICAL IMPORTANCE: In conclusion, 10 × 4 Gy is a safe and efficient protocol for treatment of primary intracranial neoplasia and future dose escalation can be considered

Report on the implementation of the recommendations of the Task Force on the Quality of Audits of Governmental Units

https://egrove.olemiss.edu/aicpa_guides/1724/thumbnail.jp

Alternativa

VILADOT-CEDO

Report of the Task Force on the Quality of Audits of Governmental Units : March 1987

https://egrove.olemiss.edu/aicpa_assoc/1385/thumbnail.jp

Moderation of antipsychotic-induced weight gain by energy balance gene variants in the RUPP autism network risperidone studies.

Second-generation antipsychotic exposure, in both children and adults, carries significant risk for excessive weight gain that varies widely across individuals. We queried common variation in key energy balance genes (FTO, MC4R, LEP, CNR1, FAAH) for their association with weight gain during the initial 8 weeks in the two NIMH Research Units on Pediatric Psychopharmacology Autism Network trials (N=225) of risperidone for treatment of irritability in children/adolescents aged 4-17 years with autism spectrum disorders. Variants in the cannabinoid receptor (CNR)-1 promoter (P=1.0 × 10(-6)), CNR1 (P=9.6 × 10(-5)) and the leptin (LEP) promoter (P=1.4 × 10(-4)) conferred robust-independent risks for weight gain. A model combining these three variants was highly significant (P=1.3 × 10(-9)) with a 0.85 effect size between lowest and highest risk groups. All results survived correction for multiple testing and were not dependent on dose, plasma level or ethnicity. We found no evidence for association with a reported functional variant in the endocannabinoid metabolic enzyme, fatty acid amide hydrolase, whereas body mass index-associated single-nucleotide polymorphisms in FTO and MC4R showed only trend associations. These data suggest a substantial genetic contribution of common variants in energy balance regulatory genes to individual antipsychotic-associated weight gain in children and adolescents, which supersedes findings from prior adult studies. The effects are robust enough to be detected after only 8 weeks and are more prominent in this largely treatment naive population. This study highlights compelling directions for further exploration of the pharmacogenetic basis of this concerning multifactorial adverse event

Nebraska Internet Evaluation Project: Year 2 Progress Report

The goals of the Internet Evaluation Project, undertaken cooperatively by the University of Nebraska at Omaha and the Nebraska Consortium of Educational Service Units, focus on a long range assessment of Internet integration into the K-12 Nebraska schools and the support delivered by the Educational Service Units (ESUs). The purpose of this report is to relate progress, after 24 months, of a comprehensive evaluation process, which is examining the impact on teachers, students, and schools. In addition to a pre-training and post-training teacher survey data, information is being gathered from machine-based ESU server support data, and observed classroom uses and projects. Each of the teacher survey, server, and innovative use data sources was examined for related implications, with cross-referencing between sources conducted when appropriate. General implications include: (1) significant progress is being made for the implementation of LB 452, and LB 860 promises to also assist in Internet integration; (2) community interest is continuing to parallel educational interest; (3) statewide dialogue is becoming increasingly important; and (4) Nebraska continues to play a national leadership role. Appendices provide the pre- and post-training surveys; pre- and post-training survey graphs; the Internet coordinator\u27s data request form; the innovative user electronic mail protocol; and innovative user interview protocol

Demonstration of lightweight gamma spectrometry systems in urban environments

Urban areas present highly complex radiation environments; with small scale features resulting from different construction materials, topographic effects and potential anthropogenic inputs from past industrial activity or other sources. Mapping of the radiation fields in urban areas allows a detailed assessment of exposure pathways for the people who live and work there, as well as locating discrete sources of activity that may warrant removal to mitigate dose to the general public. These areas also present access difficulties for radiometric mapping using vehicles or aircraft. A lightweight portable gamma spectrometry system has been used to survey sites in the vicinity of Glasgow to demonstrate the possibilities of radiometric mapping of urban areas, and to investigate the complex radiometric features such areas present. Variations in natural activity due to construction materials have been described, the presence of 137Cs used to identify relatively undisturbed ground, and a previously unknown NORM feature identified. The effect of topographic enclosure on measurements of activity concentration has been quantified. The portable system is compared with the outputs that might be expected from larger vehicular or airborne systems. For large areas airborne surveys are the most cost effective approach, but provide limited spatial resolution, vehicular surveys can provide sparse exploratory data rapidly or detailed mapping of open areas where off-road access is possible. Backpack systems are ideally suited to detailed surveys of small areas, especially where vehicular access is difficult

Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach

Altres ajuts: The work of Yonatan Tarazona Coronel has been partially funded by American Program in GIS and Remote Sensing and National Program of Scholarships and Educational Credit (PRONABEC-Peru) through RJ: Nº 4276-2018-MINEDU/VMGI-PRONABEC-OBE and RJ: Nº 942-2019-MINEDU/VMGI-PRONABEC-OBE.This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the time series, we reduced seasonality using Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. Single and multi-temporal filters were used to reduce speckle noise from Synthetic Aperture Radar (SAR) images (i.e., ALOS PALSAR and Sentinel-1B) before fusing them with optical images through Principal Component Analysis (PCA). We detected only one change in the two PV series using a non-seasonal detection approach, as well as in the fused images through five machine learning algorithms that were calibrated with Cross-Validation (CV) and Monte Carlo Cross-Validation (MCCV). In total, four categories were obtained: forest, cropland, bare soil, and water. We then compared the change map obtained with time series and that obtained with the classification algorithms with the best calibration performance, revealing an overall accuracy of 92.91% and 91.82%, respectively. For statistical comparisons, we used deforestation reference data. Finally, we conclude with some discussions and reflections on the advantages and disadvantages of the detections made with time series and machine learning algorithms, as well as the contribution of SAR images to the classifications, among other aspects