Automated Failure Explanation Through Execution Comparison

When fixing a bug in software, developers must build an understanding or explanation of the bug and how the bug flows through a program. The effort that developers must put into building this explanation is costly and laborious. Thus, developers need tools that can assist them in explaining the behavior of bugs. Dynamic slicing is one technique that can effectively show how a bug propagates through an execution up to the point where a program fails. However, dynamic slices are large because they do not just explain the bug itself; they include extra information that explains any observed behavior that might be connected to the bug. Thus, the explanation of the bug is hidden within this other tangentially related information. This dissertation addresses the problem and shows how a failing execution and a correct execution may be compared in order to construct explanations that include only information about what caused the bug. As a result, these automated explanations are significantly more concise than those explanations produced by existing dynamic slicing techniques. To enable the comparison of executions, we develop new techniques for dynamic analyses that identify the commonalities and differences between executions. First, we devise and implement the notion of a point within an execution that may exist across multiple executions. We also note that comparing executions involves comparing the state or variables and their values that exist within the executions at different execution points. Thus, we design an approach for identifying the locations of variables in different executions so that their values may be compared. Leveraging these tools, we design a system for identifying the behaviors within an execution that can be blamed for a bug and that together compose an explanation for the bug. These explanations are up to two orders of magnitude smaller than those produced by existing state of the art techniques. We also examine how different choices of a correct execution for comparison can impact the practicality or potential quality of the explanations produced via our system

Multiple metabolomics of uropathogenic E. coli reveal different information content in terms of metabolic potential compared to virulence factors.

No single analytical method can cover the whole metabolome and the choice of which platform to use may inadvertently introduce chemical selectivity. In order to investigate this we analysed a collection of uropathogenic Escherichia coli. The selected strains had previously undergone extensive characterisation using classical microbiological methods for a variety of metabolic tests and virulence factors. These bacteria were analysed using Fourier transform infrared (FT-IR) spectroscopy; gas chromatography mass spectrometry (GC-MS) after derivatisation of polar non-volatile analytes; as well as reversed-phase liquid chromatography mass spectrometry in both positive (LC-MS(+ve)) and negative (LC-MS(-ve)) electrospray ionisation modes. A comparison of the discriminatory ability of these four methods with the metabolic test and virulence factors was made using Procrustes transformations to ascertain which methods produce congruent results. We found that FT-IR and LC-MS(-ve), but not LC-MS(+ve), were comparable with each other and gave highly similar clustering compared with the virulence factors tests. By contrast, FT-IR and LC-MS(-ve) were not comparable to the metabolic tests, and we found that the GC-MS profiles were significantly more congruent with the metabolic tests than the virulence determinants. We conclude that metabolomics investigations may be biased to the analytical platform that is used and reflects the chemistry employed by the methods. We therefore consider that multiple platforms should be employed where possible and that the analyst should consider that there is a danger of false correlations between the analytical data and the biological characteristics of interest if the full metabolome has not been measured

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Cent'anni di sindacato nel Veneto

Trace elements are commonly present as components of metabolic enzymes, hormones and antioxidants in human milk. Previous studies have reported single or few elements in relatively large volumes of human milk using complex, time-consuming and expensive methods involving microwave-assisted acid digestion and extraction using tetramethylammonium hydroxide at various temperatures. We report here a validated alkaline dissolution method using ethylenediaminetetraacetic acid, ammonia solution, isopropanol and Triton X-100 to simultaneously determine trace elements in 0.2 mL samples of human milk by inductively coupled plasma mass spectrometry (ICP-MS). The trace elements zinc (Zn), copper (Cu), selenium (Se), manganese (Mn), iodine (I), iron (Fe), molybdenum (Mo), bromine (Br), chromium (Cr), cobalt (Co), lead (Pb), nickel (Ni), silver (Ag), cadmium (Cd), arsenic (As), bismuth (Bi), aluminium (Al),antimony (Sb), vanadium (V), thallium (Tl) and uranium (U)were detected, and the method was applied quantitatively to 12 samples of human milk. The results for method validation showed good sensitivity, accuracy and repeatability for Zn, Cu, Se, Mn, I, Fe, Mo and Br. The mean±SD of these elements in the above human milk samples (μg/L) were 1390.6 ± 211.5, 220.8 ± 32.9, 14.3 ± 5.8, 1.37 ± 0.14, 113.5 ± 17.1, 47.3± 99.9, 0.37± 0.12 and 812.6 ± 127.7, respectively. This method is precise, reliable, straightforward and cost effective in the determination of trace elements simultaneously in small sample volumes of human milk. Method application permits routine monitoring of several elements and the ongoing assessment of trace element nutrition in breast milk. It is the first method to highlight the relatively high Br levels present in human mil

Additional file 1 of An open-label pilot trial assessing tolerability and feasibility of LSD microdosing in patients with major depressive disorder (LSDDEP1)

Additional file 1. Participant information sheet and informed consent form