Nonlinear growth generates age changes in the moments of the frequency distribution: the example of height in puberty

Higher moments of the frequency distribution of child height and weight change with age, particularly during puberty, though why is not known. Our aims were to confirm that height skewness and kurtosis change with age during puberty, to devise a model to explain why, and to test the model by analyzing the data longitudinally. Heights of 3245 Christ's Hospital School boys born during 1927-1956 were measured twice termly from 9 to 20 years (n = 129 508). Treating the data as independent, the mean, standard deviation (SD), skewness, and kurtosis were calculated in 40 age groups and plotted as functions of age t. The data were also analyzed longitudinally using the nonlinear random-effects growth model H( t) = h( t - epsilon) + alpha, with H( t) the cross-sectional data, h( t) the individual mean curve, and epsilon and alpha subject-specific random effects reflecting variability in age and height at peak height velocity (PHV). Mean height increased monotonically with age, while the SD, skewness, and kurtosis changed cyclically with, respectively, 1, 2, and 3 turning points. Surprisingly, their age curves corresponded closely in shape to the first, second, and third derivatives of the mean height curve. The growth model expanded as a Taylor series in e predicted such a pattern, and the longitudinal analysis showed that adjusting for age at PHV on a multiplicative scale largely removed the trends in the higher moments. A nonlinear growth process where subjects grow at different rates, such as in puberty, generates cyclical changes in the higher moments of the frequency distribution

Toward faithful templates for non-spinning binary black holes using the effective-one-body approach

We present an accurate approximation of the full gravitational radiation waveforms generated in the merger of non-eccentric systems of two non-spinning black holes. Utilizing information from recent numerical relativity simulations and the natural flexibility of the effective-one-body (EOB) model, we extend the latter so that it can successfully match the numerical relativity waveforms during the last stages of inspiral, merger and ringdown. By ``successfully'' here, we mean with phase differences < 8% of a gravitational-wave cycle accumulated by the end of the ringdown phase, maximizing only over time of arrival and initial phase. We obtain this result by simply adding a 4-post-Newtonian order correction in the EOB radial potential and determining the (constant) coefficient by imposing high-matching performances with numerical waveforms of mass ratios m1/m2 = 1, 3/2, 2 and 4, m1 and m2 being the individual black-hole masses. The final black-hole mass and spin predicted by the numerical simulations are used to determine the ringdown frequency and decay time of three quasi-normal-mode damped sinusoids that are attached to the EOB inspiral-(plunge) waveform at the EOB light-ring. The EOB waveforms might be tested and further improved in the future by comparison with extremely long and accurate inspiral numerical-relativity waveforms. They may already be employed for coherent searches and parameter estimation of gravitational waves emitted by non-spinning coalescing binary black holes with ground-based laser-interferometer detectors.Comment: 15 pages, 9 figure

Methionine Adenosyltransferase 1a (MAT1A) Enhances Cell Survival During Chemotherapy Treatment and is Associated with Drug Resistance in Bladder Cancer PDX Mice.

Bladder cancer is among the top ten most common cancers, with about ~380,000 new cases and ~150,000 deaths per year worldwide. Tumor relapse following chemotherapy treatment has long been a significant challenge towards completely curing cancer. We have utilized a patient-derived bladder cancer xenograft (PDX) platform to characterize molecular mechanisms that contribute to relapse following drug treatment in advanced bladder cancer. Transcriptomic profiling of bladder cancer xenograft tumors by RNA-sequencing analysis, before and after relapse, following a 21-day cisplatin/gemcitabine drug treatment regimen identified methionine adenosyltransferase 1a (MAT1A) as one of the significantly upregulated genes following drug treatment. Survey of patient tumor sections confirmed elevated levels of MAT1A in individuals who received chemotherapy. Overexpression of MAT1A in 5637 bladder cancer cells increased tolerance to gemcitabine and stalled cell proliferation rates, suggesting MAT1A upregulation as a potential mechanism by which bladder cancer cells persist in a quiescent state to evade chemotherapy

When Are Cyber Blackouts in Modern Service Networks Likely?: A Network Oblivious Theory on Cyber (Re)Insurance Feasibility

Service liability interconnections among globally networked IT- and IoT-driven service organizations create potential channels for cascading service disruptions worth billions of dollars, due to modern cyber-crimes such as DDoS, APT, and ransomware attacks. A natural question that arises in this context is: What is the likelihood of a cyber-blackout?, where the latter term is defined as the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this article, we investigate this question in general as a function of service chain networks and different cyber-loss distribution types. We show somewhat surprisingly (and discuss the potential practical implications) that, following a cyber-attack, the effect of (a) a network interconnection topology and (b) a wide range of loss distributions on the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations are mostly very small. The primary rationale behind these results are attributed to degrees of heterogeneity in the revenue base among organizations and the Increasing Failure Rate property of popular (i.i.d/non-i.i.d) loss distributions, i.e., log-concave cyber-loss distributions. The result will enable risk-averse cyber-riskmanagers to safely infer the impact of cyber-attacks in a worst-case network and distribution oblivious setting.Peer reviewe