Prognostic Prediction of Cytogenetically Normal Acute Myeloid Leukemia Based on a Gene Expression Model

Acute myeloid leukemia (AML) refers to a heterogeneous group of hematopoietic malignancies. The well-known European Leukemia Network (ELN) stratifies AML patients into three risk groups, based primarily on the detection of cytogenetic abnormalities. However, the prognosis of cytogenetically normal AML (CN-AML), which is the largest AML subset, can be hard to define. Moreover, the clinical outcomes associated with this subgroup are diverse. In this study, using transcriptome profiles collected from CN-AML patients in the BeatAML cohort, we constructed a robust prognostic Cox model named NEST (Nine-gEne SignaTure). The validity of NEST was confirmed in four external independent cohorts. Moreover, the risk score predicted by the NEST model remained an independent prognostic factor in multivariate analyses. Further analysis revealed that the NEST model was suitable for bone marrow mononuclear cell (BMMC) samples but not peripheral blood mononuclear cell (PBMC) samples, which indirectly indicated subtle differences between BMMCs and PBMCs. Our data demonstrated the robustness and accuracy of the NEST model and implied the importance of the immune dysfunction in the leukemogenesis that occurs in CN-AML, which shed new light on the further exploration of molecular mechanisms and treatment guidance for CN-AML.

OB Nest Randomized Controlled Trial: A Cost Comparison of Reduced Visit Compared to Traditional Prenatal Care

BackgroundTraditional prenatal care includes up to 13 in person office visits, and the cost of this care is not well-described. Alternative models are being explored to better meet the needs of patients and providers. OB Nest is a telemedicine-enhanced program with a reduced frequency of in-person prenatal visits. The cost implications of connected care services added to prenatal care packages are unclear.MethodsUsing data from the OB Nest randomized, controlled trial we analyzed the provider and staff time associated with prenatal care in the traditional and OB Nest models. Fewer visits were required for OB Nest, but given the compensatory increase in connected care activity and supplies, the actual cost difference is not known.Nursing and provider staff time was prospectively recorded for all patients enrolled in the OB Nest clinical trial. Published 2015 national wages for healthcare workers were used to calculate the actual labor cost of providing either traditional or OB Nest prenatal care in 2015 US dollars. Overhead expenses and opportunity costs were not considered.ResultsTotal provider cost was decreased caring for the OB Nest participants, but nursing cost was increased. OB Nest care required an average of 160.8 (+/- 45.0) minutes provider time and 87.8 (+/- 25.1) minutes nursing time, compared to 215.0 (+/- 71.6) and 99.6 (+/- 29.7) minutes for traditional prenatal care (P<0.01). This translated into decreased provider cost and increased nursing cost (P<0.01). Supply costs increased, travel costs declined, and overhead costs declined in the OB Nest model. ConclusionsIn this trial, labor cost for OB Nest prenatal care was 34% higher than for traditional prenatal care. The increased cost is largely attributable to additional nursing connected care time, and in some practice settings may be offset by decreased overhead costs and increased provider billing opportunities. Future efforts will be focused on development of digital solutions for some routine nursing tasks to decrease the overall cost of the model. Trial Registrations: ClinicalTrials.gov Identifier: NCT02082275

Simulation and validation of long-term ground surface subsidence in continuous permafrost, western Arctic Canada

&lt;p&gt;Ground surface subsidence caused by the melt of excess ice is a key geomorphic process in permafrost regions. Subsidence can damage infrastructure, alter ecology and hydrology, and influence carbon cycling. The Geological Survey of Canada maintains a network of thaw tubes in northwestern Canada, which records annual thaw penetration, active-layer thickness, and ground surface elevation changes at numerous sites. Measurements from the early 1990s from 17 sites in the Mackenzie Delta area have highlighted persistent increases in thaw penetration in response to rising air temperatures. These increases in thaw penetration have been accompanied by significant ground surface subsidence (~5 to 20 cm) at 10 ice rich sites, with a median subsidence rate of 0.4 cm a&lt;sup&gt;-1&lt;/sup&gt; (min: 0.2, max: 0.8 cm a&lt;sup&gt;-1&lt;/sup&gt;). Here we present preliminary results comparing these long-term field data to simulations for two observation sites using the Northern Ecosystem Soil Temperature (NEST) model. NEST has been modified to include a routine that accounts for ground surface subsidence caused by the melt of excess ground ice. The excess ice content of upper permafrost in the simulations was estimated based on ratios between thaw penetration and subsidence measured at each thaw tube. The NEST simulations begin in 1901, and there is little ground surface subsidence until the 1980s. The simulated rate of ground surface subsidence increases in the 1990s. The modelled ground surface subsidence is in good agreement with the measured annual magnitudes and longer-term patterns over the measurement period from 1992 to 2017. This preliminary assessment indicates that the modified NEST model is capable of predicting gradual thaw subsidence in ice-rich permafrost environments over decadal timescales.&lt;/p&gt;

Gearbox Fault Prediction of Wind Turbine Based on Improved NEST Model

This paper studies a fault prediction method for wind turbine gearbox. It uses grey relation analysis to get modeling variables, and makes sample data getting good integrity and redundancy by similarity analysis. Thus it gets the reduced process memory matrix, and trains the improved nonlinear state estimation (NEST) model. When the gearbox fails, the model residual will exceed the threshold value, and the model will give an early warning. Combined with the actual operation data of a wind turbine, the effectiveness and accuracy of the improved model are verified.

Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information

Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan.

An Evaluation of Paired Regional/Convection-Allowing Forecast Vertical Thermodynamic Profiles in Warm-Season, Thunderstorm-Supporting Environments

This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.