Explaining Weapon System Sustainment\u27s Impact to Aircraft Availability

This research focused on understanding the phenomena behind the cost growth of Weapon System Sustainment (WSS) and the simultaneous degradation in USAF aircraft system availability. The primary modelling technique used was Ordinary Least Squares (OLS) while incorporating temporal effect. Other studies have looked at cost factors related to the Flying Hour Program, flying conditions and age. This study found empirical relationships between each of the four WSS business processes and the lead time in months it takes to realize improvements in system aircraft availability

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Antenatal Determinants of Bronchopulmonary Dysplasia and Late Respiratory Disease in Preterm Infants

RATIONALE: Mechanisms contributing to chronic lung disease after preterm birth are incompletely understood. OBJECTIVES: To identify antenatal risk factors associated with increased risk for bronchopulmonary dysplasia (BPD) and respiratory disease during early childhood after preterm birth, we performed a prospective, longitudinal study of 587 preterm infants with gestational age less than 34 weeks and birth weights between 500 and 1,250 g. METHODS: Data collected included perinatal information and assessments during the neonatal intensive care unit admission and longitudinal follow-up by questionnaire until 2 years of age. MEASUREMENTS AND MAIN RESULTS: After adjusting for covariates, we found that maternal smoking prior to preterm birth increased the odds of having an infant with BPD by twofold (P = 0.02). Maternal smoking was associated with prolonged mechanical ventilation and respiratory support during the neonatal intensive care unit admission. Preexisting hypertension was associated with a twofold (P = 0.04) increase in odds for BPD. Lower gestational age and birth weight z-scores were associated with BPD. Preterm infants who were exposed to maternal smoking had higher rates of late respiratory disease during childhood. Twenty-two percent of infants diagnosed with BPD and 34% of preterm infants without BPD had no clinical signs of late respiratory disease during early childhood. CONCLUSIONS: We conclude that maternal smoking and hypertension increase the odds for developing BPD after preterm birth, and that maternal smoking is strongly associated with increased odds for late respiratory morbidities during early childhood. These findings suggest that in addition to the BPD diagnosis at 36 weeks, other factors modulate late respiratory outcomes during childhood. We speculate that measures to reduce maternal smoking not only will lower the risk for preterm birth but also will improve late respiratory morbidities after preterm birth

Preclinical Evidence for the Use of Sunitinib Malate in the Treatment of Plexiform Neurofibromas

Plexiform neurofibromas (pNF) are pathognomonic nerve and soft tissue tumors of neurofibromatosis type I (NF1), which are highly resistant to conventional chemotherapy and associated with significant morbidity/mortality. Disruption of aberrant SCF/c-Kit signaling emanating from the pNF microenvironment induced the first ever objective therapeutic responses in a recent phase 2 trial. Sunitinib malate is a potent, highly selective RTK inhibitor with activity against c-Kit, PDGFR, and VEGFR, which have also been implicated in the pathogenesis of these lesions. Here, we evaluate the efficacy of sunitinib malate in a preclinical Krox20;Nf1flox/− pNF murine model. Experimental Design Proliferation, β-hexosaminidase release (degranulation), and Erk1/2 phosphorylation were assessed in sunitinib treated Nf1+/− mast cells and fibroblasts, respectively. Krox20;Nf1flox/− mice with established pNF were treated sunitinib or PBS-vehicle control for a duration of 12 weeks. pNF metabolic activity was monitored by serial [18F]DG-PET/CT imaging. Results Sunitinib suppressed multiple in vitro gain-in-functions of Nf1+/− mast cells and fibroblasts and attenuated Erk1/2 phosphorylation. Sunitinib treated Krox20;Nf1flox/− mice exhibited significant reductions in pNF size, tumor number, and FDG uptake compared to control mice. Histopathology revealed reduced tumor cellularity and infiltrating mast cells, markedly diminished collagen deposition, and increased cellular apoptosis in sunitinib treated pNF. Conclusions Collectively, these results demonstrate the efficacy of sunitinib in reducing tumor burden in Krox20;Nf1flox/− mice. These preclinical findings demonstrate the utility of inhibiting multiple RTKs in pNF and provide insights into the design of future clinical trials

The jellification of north temperate lakes.

Calcium (Ca) concentrations are decreasing in softwater lakes across eastern North America and western Europe. Using long-term contemporary and palaeo-environmental field data, we show that this is precipitating a dramatic change in Canadian lakes: the replacement of previously dominant pelagic herbivores (Ca-rich Daphnia species) by Holopedium glacialis, a jelly-clad, Ca-poor competitor. In some lakes, this transformation is being facilitated by increases in macro-invertebrate predation, both from native (Chaoborus spp.) and introduced (Bythotrephes longimanus) zooplanktivores, to which Holopedium, with its jelly coat, is relatively invulnerable. Greater representation by Holopedium within cladoceran zooplankton communities will reduce nutrient transfer through food webs, given their lower phosphorus content relative to daphniids, and greater absolute abundances may pose long-term problems to water users. The dominance of jelly-clad zooplankton will likely persist while lakewater Ca levels remain low.This work was primarily supported by grants from the Natural Sciences and Engineering Research Council of Canada and funding from the Ontario Ministry of the Environment.This is the accepted manuscript. The final version is available at http://rspb.royalsocietypublishing.org/content/282/1798/20142449

Clinical and Laboratory characteristics of patients with COVID-19 Infection and Deep Venous Thrombosis

Objective: Early reports suggest that patients with novel coronavirus disease-2019 (COVID-19) infection carry a significant risk of altered coagulation with an increased risk for venous thromboembolic events. This report investigates the relationship of significant COVID-19 infection and deep venous thrombosis (DVT) as reflected in the patient clinical and laboratory characteristics. Methods: We reviewed the demographics, clinical presentation, laboratory and radiologic evaluations, results of venous duplex imaging and mortality of COVID-19-positive patients (18-89 years) admitted to the Indiana University Academic Health Center. Using oxygen saturation, radiologic findings, and need for advanced respiratory therapies, patients were classified into mild, moderate, or severe categories of COVID-19 infection. A descriptive analysis was performed using univariate and bivariate Fisher's exact and Wilcoxon rank-sum tests to examine the distribution of patient characteristics and compare the DVT outcomes. A multivariable logistic regression model was used to estimate the adjusted odds ratio of experiencing DVT and a receiver operating curve analysis to identify the optimal cutoff for d-dimer to predict DVT in this COVID-19 cohort. Time to the diagnosis of DVT from admission was analyzed using log-rank test and Kaplan-Meier plots. Results: Our study included 71 unique COVID-19-positive patients (mean age, 61 years) categorized as having 3% mild, 14% moderate, and 83% severe infection and evaluated with 107 venous duplex studies. DVT was identified in 47.8% of patients (37% of examinations) at an average of 5.9 days after admission. Patients with DVT were predominantly male (67%; P = .032) with proximal venous involvement (29% upper and 39% in the lower extremities with 55% of the latter demonstrating bilateral involvement). Patients with DVT had a significantly higher mean d-dimer of 5447 ± 7032 ng/mL (P = .0101), and alkaline phosphatase of 110 IU/L (P = .0095) than those without DVT. On multivariable analysis, elevated d-dimer (P = .038) and alkaline phosphatase (P = .021) were associated with risk for DVT, whereas age, sex, elevated C-reactive protein, and ferritin levels were not. A receiver operating curve analysis suggests an optimal d-dimer value of 2450 ng/mL cutoff with 70% sensitivity, 59.5% specificity, and 61% positive predictive value, and 68.8% negative predictive value. Conclusions: This study suggests that males with severe COVID-19 infection requiring hospitalization are at highest risk for developing DVT. Elevated d-dimers and alkaline phosphatase along with our multivariable model can alert the clinician to the increased risk of DVT requiring early evaluation and aggressive treatmen

Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data

Over the last few years, experimental data on the fluctuations in gene activity between individual cells and within the same cell over time have confirmed that gene expression is a “noisy” process. This variation is in part due to the small number of molecules taking part in some of the key reactions that are involved in gene expression. One of the consequences of this is that protein production often occurs in bursts, each due to a single promoter or transcription factor binding event. Recently, the distribution of the number of proteins produced in such bursts has been experimentally measured, offering a unique opportunity to study the relative importance of different sources of noise in gene expression. Here, we provide a derivation of the theoretical probability distribution of these bursts for a wide variety of different models of gene expression. We show that there is a good fit between our theoretical distribution and that obtained from two different published experimental datasets. We then prove that, irrespective of the details of the model, the burst size distribution is always geometric and hence determined by a single parameter. Many different combinations of the biochemical rates for the constituent reactions of both transcription and translation will therefore lead to the same experimentally observed burst size distribution. It is thus impossible to identify different sources of fluctuations purely from protein burst size data or to use such data to estimate all of the model parameters. We explore methods of inferring these values when additional types of experimental data are available

Proteomic profiling of high risk medulloblastoma reveals functional biology

Genomic characterization of medulloblastoma has improved molecular risk classification but struggles to define functional biological processes, particularly for the most aggressive subgroups. We present here a novel proteomic approach to this problem using a reference library of stable isotope labeled medulloblastoma-specific proteins as a spike-in standard for accurate quantification of the tumor proteome. Utilizing high-resolution mass spectrometry, we quantified the tumor proteome of group 3 medulloblastoma cells and demonstrate that high-risk MYC amplified tumors can be segregated based on protein expression patterns. We cross-validated the differentially expressed protein candidates using an independent transcriptomic data set and further confirmed them in a separate cohort of medulloblastoma tissue samples to identify the most robust proteogenomic differences. Interestingly, highly expressed proteins associated with MYC-amplified tumors were significantly related to glycolytic metabolic pathways via alternative splicing of pyruvate kinase (PKM) by heterogeneous ribonucleoproteins (HNRNPs). Furthermore, when maintained under hypoxic conditions, these MYC-amplified tumors demonstrated increased viability compared to non-amplified tumors within the same subgroup. Taken together, these findings highlight the power of proteomics as an integrative platform to help prioritize genetic and molecular drivers of cancer biology and behavior

Searching for a Stochastic Background of Gravitational Waves with LIGO

The Laser Interferometer Gravitational-wave Observatory (LIGO) has performed the fourth science run, S4, with significantly improved interferometer sensitivities with respect to previous runs. Using data acquired during this science run, we place a limit on the amplitude of a stochastic background of gravitational waves. For a frequency independent spectrum, the new limit is $\Omega_{\rm GW} < 6.5 \times 10^{-5}$. This is currently the most sensitive result in the frequency range 51-150 Hz, with a factor of 13 improvement over the previous LIGO result. We discuss complementarity of the new result with other constraints on a stochastic background of gravitational waves, and we investigate implications of the new result for different models of this background.Comment: 37 pages, 16 figure

The characterization of Salmonella enterica serotypes isolated from the scalder tank water of a commercial poultry processing plant: Recovery of a multidrug-resistant Heidelberg strain

The recent multistate outbreak of a multidrug-resistant (MDR) Salmonella Heidelberg strain from commercial poultry production highlights the need to better understand the reservoirs of these zoonotic pathogens within the commercial poultry production and processing environment. As part of a larger study looking at temporal changes in microbial communities within the major water tanks within a commercial processing facility, this paper identifies and characterizes Salmonella enterica isolated from the water in a final scalder tank at 3 times during a typical processing day: prior to the birds entering the tank (start), halfway through the processing day (mid), and after the final birds were scalded (end). Over 3 consecutive processing days, no Salmonella were recovered from start-of-day water samples, while a total of 56 Salmonella isolates were recovered from the mid-day and end-of-day scalder water samples. Traditional and newer PCR-based serotyping methods eventually identified these isolates as either group C3 S. Kentucky (n = 45) and group B S. Heidelberg (n = 11). While none of the S. Kentucky isolates possessed any resistances to the antimicrobials tested, all S. Heidelberg isolates were found to be multidrug resistant to 5 specific antimicrobials representing 3 antimicrobial classes. Due to the potential public health impact of S. Heidelberg and the recent nationwide poultry-associated outbreak of multidrug-resistant S. Heidelberg, future studies should focus on understanding the transmission and environmental growth dynamics of this serotype within the commercial poultry processing plant environment.Keywords: S. Heidelberg, Scalder water, Multidrug resistanc