Models of E‐Government: Are They Correct? An Empirical Assessment

Research into e-government is relatively new. Nevertheless, much contemporary thinking and writing about e-government is driven by normative models that appeared less than a decade ago. The authors present empirical evidence from three surveys of local e-government in the United States to test whether these models are accurate or useful for understanding the actual development of e-government. They find that local e-government is mainly informational, with a few transactions but virtually no indication of the high-level functions predicted in the models. Thus, the models do not accurately describe or predict the development of e-government, at least among American local governments. These models, though intellectually interesting, are purely speculative, having been developed without linkage to the literature about information technology and government. The authors offer grounded observations about e-government that will useful to scholars and practitioners alike.     

Evaluating Bioinformatic Pipeline Performance for Forensic Microbiome Analysis*,†,‡

Microbial communities have potential evidential utility for forensic applications. However, bioinformatic analysis of high-throughput sequencing data varies widely among laboratories. These differences can potentially affect microbial community composition and downstream analyses. To illustrate the importance of standardizing methodology, we compared analyses of postmortem microbiome samples using several bioinformatic pipelines, varying minimum library size or minimum number of sequences per sample, and sample size. Using the same input sequence data, we found that three open-source bioinformatic pipelines, MG-RAST, mothur, and QIIME2, had significant differences in relative abundance, alpha-diversity, and beta-diversity, despite the same input data. Increasing minimum library size and sample size increased the number of low-abundant and infrequent taxa detected. Our results show that bioinformatic pipeline and parameter choice affect results in important ways. Given the growing potential application of forensic microbiology to the criminal justice system, continued research on standardizing computational methodology will be important for downstream applications.     

Sample Mining and Data Mining: Combined Real-Time and Retrospective Approaches for the Identification of Emerging Novel Psychoactive Substances

Novel psychoactive substances (NPS) are synthetic drugs that pose serious public health and safety concerns. A multitude of NPS have been identified in the United States, often implicated in forensic investigations. The most common and effective manner for identifying NPS is by use of mass spectrometry and the true utility lies within nontargeted acquisition techniques. During this study, a liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) assay was developed, validated, and implemented for forensic toxicology testing. A SCIEX TripleTOF™ 5600 + with SWATH^® acquisition was used. Resulting data were compared against an extensive library database containing more than 800 compounds. The LC-QTOF-MS assay was applied to the reanalysis of biological sample extracts to discover emergent NPS. More than 3,000 sample extracts were analyzed, and more than 20 emerging NPS were detected for the first time. Among these were isopropyl-U-47700, 3,4-methylenedioxy-U-47700, fluorofuranylfentanyl, N-methyl norfentanyl, 2F-deschloroketamine, 3,4-methylenedioxy-alpha-PHP, eutylone, and N-ethyl hexedrone.