Water Microgrids: The Future of Water Infrastructure Resilience

AbstractMicrogrids have recently come into vogue as a potential solution to address the increasing number of power outages caused by extreme weather events that impact our cities and communities. Such events – often precipitated by increasing global temperatures and climate change – have repercussions that expand beyond damages to a city's electric infrastructure. Water infrastructure is similarly vulnerable to extreme weather events, resulting in significant impacts to clean water distribution, wastewater treatment, and stormwater management. Given this similarity, and other value drivers to be outlined, this paper proposes leveraging concepts behind electricity microgrids to develop a unified framework for microgrid application to promote water resilience in the face of our changing climate.Many parallels can be drawn between the electric grid and water infrastructure considering both are utilities that generate, store, and distribute an essential product that has been identified as a basic human right. Also similar to the electric grid, water infrastructure is aged and costly to redevelop. For both industries, microgrids are a potential solution that addresses aged infrastructure concerns while also being potentially more cost effective. In addition, by leveraging legacy infrastructural components while developing a new system within the system, microgrids provide redundancy, fortify vulnerabilities and secure the resource supply chain. This paper will investigate parallel components of electric and water infrastructure to provide a vision for future resilient water microgrids

Introduction to Cybersecurity and Government Minitrack

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Association of Minimal Residual Disease With Superior Survival Outcomes in Patients With Multiple Myeloma: A Meta-analysis

Importance: Numerous studies have evaluated the prognostic value of minimal residual disease (MRD) in patients with multiple myeloma (MM). Most studies were small and varied in terms of patient population, treatment, and MRD assessment methods. Objective: To evaluate the utility of MRD detection in patients with newly diagnosed MM. Data Sources: A Medline search was conducted for articles published in English between January 1990 and January 2016. Study Selection: Eligible studies reported MRD status and progression-free survival (PFS) or overall survival (OS) in 20 or more patients following treatment. Among 405 articles identified, 21 met the initial eligibility criteria and were included in the analysis. Data Extraction and Synthesis: Information on patient characteristics, treatment, MRD assessment, and outcomes were extracted using a standard form. Main Outcomes and Measures: The impact of MRD status on PFS and OS was assessed by pooling data from relevant trials. Data were adjusted to allow for different proportions of patients with MRD in different studies, and analyzed using the Peto method. Forest plots were created based on Cox model analysis. Other prespecified research questions were addressed qualitatively. Results: Fourteen studies (n = 1273) provided data on the impact of MRD on PFS, and 12 studies (n = 1100) on OS. Results were reported specifically in patients who had achieved conventional complete response (CR) in 5 studies for PFS (n = 574) and 6 studies for OS (n = 616). An MRD-negative status was associated with significantly better PFS overall (hazard ratio [HR], 0.41; 95% CI, 0.36-0.48; P < .001) and in studies specifically looking at CR patients (HR, 0.44; 95% CI, 0.34-0.56; P < .001). Overall survival was also favorable in MRD-negative patients overall (HR, 0.57; 95% CI, 0.46-0.71; P < .001) and in CR patients (HR, 0.47; 95% CI, 0.33-0.67; P < .001). Tests of heterogeneity found no significant differences among the studies for PFS and OS. Conclusions and Relevance: Minimal residual disease-negative status after treatment for newly diagnosed MM is associated with long-term survival. These findings provide quantitative evidence to support the integration of MRD assessment as an end point in clinical trials of MM

Lost but Not Forgotten—The Economics of Improving Patient Retention in AIDS Treatment Programs

Gregory Bisson and Jeffrey Stringer discuss the implications of a new study showing how loss to follow-up affects the effectiveness of a public sector HIV program in Côte d'Ivoire

Renal Cell Carcinoma Perfusion before and after Radiofrequency Ablation Measured with Dynamic Contrast Enhanced MRI: A Pilot Study

Aim: To investigate if the early treatment effects of radiofrequency ablation (RFA) on renal cell carcinoma (RCC) can be detected with dynamic contrast enhanced (DCE)-MRI and to correlate RCC perfusion with RFA treatment time. Materials and methods: 20 patients undergoing RFA of their 21 RCCs were evaluated with DCE-MRI before and at one month after RFA treatment. Perfusion was estimated using the maximum slope technique at two independent sittings. Total RCC blood flow was correlated with total RFA treatment time, tumour location, size and histology. Results: DCE-MRI examinations were successfully evaluated for 21 RCCs (size from 1.3 to 4 cm). Perfusion of the RCCs decreased significantly (p < 0.0001) from a mean of 203 (±80) mL/min/100 mL before RFA to 8.1 (±3.1) mL/min/100 mL after RFA with low intra-observer variability (r ≥ 0.99, p < 0.0001). There was an excellent correlation (r = 0.95) between time to complete ablation and pre-treatment total RCC blood flow. Tumours with an exophytic location exhibit the lowest mean RFA treatment time. Conclusion: DCE-MRI can detect early treatment effects by measuring RCC perfusion before and after RFA. Perfusion significantly decreases in the zone of ablation, suggesting that it may be useful for the assessment of treatment efficacy. Pre-RFA RCC blood flow may be used to predict RFA treatment time

Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling

We designed a mathematical model to describe and quantify the mechanisms and dynamics of tumor growth, cell-kill and resistance as they affect durations of benefit after cancer treatment. Our aim was to explore how treatment efficacy may be related to primary tumor characteristics, with the potential to guide future trial design and appropriate selection of therapy. Assuming a log-normal distribution of both resistant disease and tumor doubling times generates disease-free survival (DFS) or invasive DFS curves with specific shapes. Using a multivariate mathematical model, both treatment and tumor characteristics are related to quantified resistant disease and tumor regrowth rates by allowing different mean values for the influence of different treatments or clinical subtypes on these two log-normal distributions. Application of the model to the CALGB 9741 adjuvant breast cancer trial showed that dose-dense therapy was estimated to achieve an extra 3/4 log of cell-kill compared to standard therapy, but only in patients with more rapidly growing ER-negative tumors. Application of the model to the AZURE trial of adjuvant bisphosphonate treatment suggested that the 5-year duration of zoledronic acid was adequate for ER-negative tumors, but may not be so for ER-positive cases, with increased recurrences after ceasing the intervention. Mathematical models can identify different effects of treatment by subgroup and may aid in treatment design, trial analysis, and appropriate selection of therapy. They may provide a more appropriate and insightful tool than the conventional Cox model for the statistical analysis of response durations

Entangled-State Cycles of Atomic Collective-Spin States

We study quantum trajectories of collective atomic spin states of $N$ effective two-level atoms driven with laser and cavity fields. We show that interesting ``entangled-state cycles'' arise probabilistically when the (Raman) transition rates between the two atomic levels are set equal. For odd (even) $N$, there are $(N+1)/2$ ($N/2$) possible cycles. During each cycle the $N$-qubit state switches, with each cavity photon emission, between the states $(|N/2,m>\pm |N/2,-m>)/\sqrt{2}$, where $|N/2,m>$ is a Dicke state in a rotated collective basis. The quantum number $m$ ($>0$), which distinguishes the particular cycle, is determined by the photon counting record and varies randomly from one trajectory to the next. For even $N$ it is also possible, under the same conditions, to prepare probabilistically (but in steady state) the Dicke state $|N/2,0>$, i.e., an $N$-qubit state with $N/2$ excitations, which is of particular interest in the context of multipartite entanglement.Comment: 10 pages, 9 figure

Serum microRNA array analysis identifies miR-140-3p, miR-33b-3p and miR-671-3p as potential osteoarthritis biomarkers involved in metabolic processes.

Background: MicroRNAs (miRNAs) in circulation have emerged as promising biomarkers. In this study, we aimed to identify a circulating miRNA signature for osteoarthritis (OA) patients and in combination with bioinformatics analysis to evaluate the utility of selected differentially expressed miRNAs in the serum as potential OA biomarkers. Methods: Serum samples were collected from 12 primary OA patients, and 12 healthy individuals were screened using the Agilent Human miRNA Microarray platform interrogating 2549 miRNAs. Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic performance of the deregulated miRNAs. Expression levels of selected miRNAs were validated by quantitative real-time PCR (qRT-PCR) in all serum and in articular cartilage samples from OA patients (n = 12) and healthy individuals (n = 7). Bioinformatics analysis was used to investigate the involved pathways and target genes for the above miRNAs. Results: We identified 279 differentially expressed miRNAs in the serum of OA patients compared to controls. Two hundred and five miRNAs (73.5%) were upregulated and 74 (26.5%) downregulated. ROC analysis revealed that 77 miRNAs had area under the curve (AUC) > 0.8 and p < 0.05. Bioinformatics analysis in the 77 miRNAs revealed that their target genes were involved in multiple signaling pathways associated with OA, among which FoxO, mTOR, Wnt, pI3K/akt, TGF-β signaling pathways, ECM-receptor interaction, and fatty acid biosynthesis. qRT-PCR validation in seven selected out of the 77 miRNAs revealed 3 significantly downregulated miRNAs (hsa-miR-33b-3p, hsa-miR-671-3p, and hsa-miR-140-3p) in the serum of OA patients, which were in silico predicted to be enriched in pathways involved in metabolic processes. Target-gene analysis of hsa-miR-140-3p, hsa-miR-33b-3p, and hsa-miR-671-3p revealed that InsR and IGFR1 were common targets of all three miRNAs, highlighting their involvement in regulation of metabolic processes that contribute to OA pathology. Hsa-miR-140-3p and hsa-miR-671-3p expression levels were consistently downregulated in articular cartilage of OA patients compared to healthy individuals. Conclusions: A serum miRNA signature was established for the first time using high density resolution miR-arrays in OA patients. We identified a three-miRNA signature, hsa-miR-140-3p, hsa-miR-671-3p, and hsa-miR-33b-3p, in the serum of OA patients, predicted to regulate metabolic processes, which could serve as a potential biomarker for the evaluation of OA risk and progression.Peer reviewedFinal Published versio