Actionable Insights on Philadelphia Crime Hot-Spots: Clustering and Statistical Analysis to Inform Future Crime Legislation

Philadelphia's problem with high crime rates continues to be exacerbated as Philadelphia's residents, community leaders, and law enforcement officials struggle to address the root causes of the problem and make the city safer for all. In this work, we deeply understand crime in Philadelphia and offer novel insights for crime mitigation within the city. Open source crime data from 2012-2022 was obtained from OpenDataPhilly. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was used to cluster geographic locations of crimes. Clustering of crimes within each of 21 police districts was performed, and temporal changes in cluster distributions were analyzed to develop a Non-Systemic Index (NSI). Home Owners' Loan Corporation (HOLC) grades were tested for associations with clusters in police districts labeled `systemic.' Crimes within each district were highly clusterable, according to Hopkins' Mean Statistics. NSI proved to be a good measure of differentiating systemic ($<$ 0.06) and non-systemic ($\geq$ 0.06) districts. Two systemic districts, 19 and 25, were found to be significantly correlated with HOLC grade (p $=2.02 \times 10^{-19}$, p $=1.52 \times 10^{-13}$). Philadelphia crime data shows a high level of heterogeneity between districts. Classification of districts with NSI allows for targeted crime mitigation strategies. Policymakers can interpret this work as a guide to interventions

FLIPS: Federated Learning using Intelligent Participant Selection

This paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In particular, we examine the benefits of label distribution clustering on participant selection in federated learning. FLIPS clusters parties involved in an FL training job based on the label distribution of their data apriori, and during FL training, ensures that each cluster is equitably represented in the participants selected. FLIPS can support the most common FL algorithms, including FedAvg, FedProx, FedDyn, FedOpt and FedYogi. To manage platform heterogeneity and dynamic resource availability, FLIPS incorporates a straggler management mechanism to handle changing capacities in distributed, smart community applications. Privacy of label distributions, clustering and participant selection is ensured through a trusted execution environment (TEE). Our comprehensive empirical evaluation compares FLIPS with random participant selection, as well as two other "smart" selection mechanisms - Oort and gradient clustering using two real-world datasets, two different non-IID distributions and three common FL algorithms (FedYogi, FedProx and FedAvg). We demonstrate that FLIPS significantly improves convergence, achieving higher accuracy by 17 - 20 % with 20 - 60 % lower communication costs, and these benefits endure in the presence of straggler participants

Treatment and Outcomes of Non-Small-Cell Lung Cancer Patients with High Comorbidity

Background: The life expectancy of untreated non-small-cell lung cancer (NSCLC) is dismal, while treatment for NSCLC improves survival. The presence of comorbidities is thought to play a significant role in the decision to treat or not treat a given patient. We aim to evaluate the association of comorbidities with the survival of patients treated for NSCLC. Methods: We performed a retrospective study of patients aged ≥66 years with invasive NSCLC between the years 2007 and 2011 in the Surveillance, Epidemiology, and End Results Kentucky Cancer Registry. Comorbidity was measured using the Klabunde Comorbidity Index (KCI), and univariate and multivariate logistic regression models were used to measure association between receiving treatment and comorbidity. Kaplan-Meier plots were constructed to estimate time-to-event outcomes. Results: A total of 4014 patients were identified; of this, 94.9% were white and 55.7% were male. The proportion of patients who did not receive any treatment was 8.7%, 3.9%, 19.1%, and 23.5% for stages I, II, III, and IV, respectively (p \u3c 0.0001). In multivariate analysis, older age, higher stage, and higher comorbidity (KCI ≥ 3) were associated with a lower likelihood of receiving any treatment. The median overall survival (OS) for untreated and KCI=0 was 17.7 months for stages I and II, 2.3 months for stage III, and 1.3 months for stage IV. The median OS for treated and KCI=0 was 58.9 months for stages I and II, 16.8 months for stage III, and 5.8 months for stage IV (p \u3c 0.01). Treatment was an independent predictor of OS in multivariate analysis that included KCI scores. Conclusion: Our data suggest that lung cancer patients may derive a survival benefit from therapies, regardless of the presence of comorbidities, although the degree of benefit seems to decrease with higher KCI scores

Knowledge Translation Consensus Conference: Research Methods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72708/1/j.1553-2712.2007.tb02378.x.pd

Serology assays used in SARS-CoV-2 seroprevalence surveys worldwide: a systematic review and meta-analysis of assay features, testing algorithms, and performance

Many serological assays to detect SARS-CoV-2 antibodies were developed during the COVID-19 pandemic. Differences in the detection mechanism of SARS-CoV-2 serological assays limited the comparability of seroprevalence estimates for populations being tested. We conducted a systematic review and meta-analysis of serological assays used in SARS-CoV-2 population seroprevalence surveys, searching for published articles, preprints, institutional sources, and grey literature between 1 January 2020, and 19 November 2021. We described features of all identified assays and mapped performance metrics by the manufacturers, third-party head-to-head, and independent group evaluations. We compared the reported assay performance by evaluation source with a mixed-effect beta regression model. A simulation was run to quantify how biased assay performance affects population seroprevalence estimates with test adjustment. Among 1807 included serosurveys, 192 distinctive commercial assays and 380 self-developed assays were identified. According to manufacturers, 28.6% of all commercial assays met WHO criteria for emergency use (sensitivity [Sn.] &gt;= 90.0%, specificity [Sp.] &gt;= 97.0%). However, manufacturers overstated the absolute values of Sn. of commercial assays by 1.0% [0.1, 1.4%] and 3.3% [2.7, 3.4%], and Sp. by 0.9% [0.9, 0.9%] and 0.2% [-0.1, 0.4%] compared to third-party and independent evaluations, respectively. Reported performance data was not sufficient to support a similar analysis for self-developed assays. Simulations indicate that inaccurate Sn. and Sp. can bias seroprevalence estimates adjusted for assay performance; the error level changes with the background seroprevalence. The Sn. and Sp. of the serological assay are not fixed properties, but varying features depending on the testing population. To achieve precise population estimates and to ensure the comparability of seroprevalence, serosurveys should select assays with high performance validated not only by their manufacturers and adjust seroprevalence estimates based on assured performance data. More investigation should be directed to consolidating the performance of self-developed assays

A rapid in vivo screen for pancreatic ductal adenocarcinoma therapeutics

Pancreatic ductal adenocarcinoma (PDA) is the fourth leading cause of cancer-related deaths in the United States, and is projected to be second by 2025. It has the worst survival rate among all major cancers. Two pressing needs for extending life expectancy of affected individuals are the development of new approaches to identify improved therapeutics, addressed herein, and the identification of early markers. PDA advances through a complex series of intercellular and physiological interactions that drive cancer progression in response to organ stress, organ failure, malnutrition, and infiltrating immune and stromal cells. Candidate drugs identified in organ culture or cell-based screens must be validated in preclinical models such as KIC (p48Cre;LSL-KrasG12D;Cdkn2af/f) mice, a genetically engineered model of PDA in which large aggressive tumors develop by 4 weeks of age. We report a rapid, systematic and robust in vivo screen for effective drug combinations to treat Kras-dependent PDA. Kras mutations occur early in tumor progression in over 90% of human PDA cases. Protein kinase and G-protein coupled receptor (GPCR) signaling activates Kras. Regulators of G-protein signaling (RGS) proteins are coincidence detectors that can be induced by multiple inputs to feedback-regulate GPCR signaling. We crossed Rgs16::GFP bacterial artificial chromosome (BAC) transgenic mice withKIC mice and show that the Rgs16::GFP transgene is a KrasG12D-dependent marker of all stages of PDA, and increases proportionally to tumor burden in KIC mice. RNA sequencing (RNA-Seq) analysis of cultured primary PDA cells reveals characteristics of embryonic progenitors of pancreatic ducts and endocrine cells, and extraordinarily high expression of the receptor tyrosine kinase Axl, an emerging cancer drug target. In proof-of-principle drug screens, we find that weanling KIC mice with PDA treated for 2 weeks with gemcitabine (with or without Abraxane) plus inhibitors of Axl signaling (warfarin and BGB324) have fewer tumor initiation sites and reduced tumor size compared with the standard-of-care treatment. Rgs16::GFP is therefore an in vivo reporter of PDA progression and sensitivity to new chemotherapeutic drug regimens such as Axl-targeted agents. This screening strategy can potentially be applied to identify improved therapeutics for other cancers

Erratum to: Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species

Upon publication of the original article [1], it was noticed that Dr Papanicolaou&rsquo;s surname was spelt incorrectly. The correct spelling is &ldquo;Papanicolaou&rdquo;, as shown in the author list of this erratum.Additional co-authors: A. Anderson, S. Asgari, P. G. Board, A. Bretschneider, P. M. Campbell, T. Chertemps, J. T. Christeller, C. W. Coppin, S. J. Downes, G. Duan, C. A. Farnsworth, R. T. Good, L. B. Han, Y. C. Han, K. Hatje, I. Horne, Y. P. Huang, D. S. T. Hughes, E. Jacquin-Joly, W. James, S. Jhangiani, M. Kollmar, S. S. Kuwar, S. Li, N-Y. Liu, M. T. Maibeche, J. R. Miller, N. Montagne, T. Perry, J. Qu, S. V. Song, G. G. Sutton, H. Vogel, B. P. Walenz, W. Xu, H-J. Zhang, Z. Zou, P. Batterham, O. R. Edwards, R. Feyereisen, R. A. Gibbs, D. G. Heckel, A. McGrath, C. Robin, S. E. Scherer, K. C. Worley, Y. D. W