Fitting Effective Diffusion Models to Data Associated with a "Glassy Potential": Estimation, Classical Inference Procedures and Some Heuristics

A variety of researchers have successfully obtained the parameters of low dimensional diffusion models using the data that comes out of atomistic simulations. This naturally raises a variety of questions about efficient estimation, goodness-of-fit tests, and confidence interval estimation. The first part of this article uses maximum likelihood estimation to obtain the parameters of a diffusion model from a scalar time series. I address numerical issues associated with attempting to realize asymptotic statistics results with moderate sample sizes in the presence of exact and approximated transition densities. Approximate transition densities are used because the analytic solution of a transition density associated with a parametric diffusion model is often unknown.I am primarily interested in how well the deterministic transition density expansions of Ait-Sahalia capture the curvature of the transition density in (idealized) situations that occur when one carries out simulations in the presence of a "glassy" interaction potential. Accurate approximation of the curvature of the transition density is desirable because it can be used to quantify the goodness-of-fit of the model and to calculate asymptotic confidence intervals of the estimated parameters. The second part of this paper contributes a heuristic estimation technique for approximating a nonlinear diffusion model. A "global" nonlinear model is obtained by taking a batch of time series and applying simple local models to portions of the data. I demonstrate the technique on a diffusion model with a known transition density and on data generated by the Stochastic Simulation Algorithm.Comment: 30 pages 10 figures Submitted to SIAM MMS (typos removed and slightly shortened

Minimax Estimation of Nonregular Parameters and Discontinuity in Minimax Risk

When a parameter of interest is nondifferentiable in the probability, the existing theory of semiparametric efficient estimation is not applicable, as it does not have an influence function. Song (2014) recently developed a local asymptotic minimax estimation theory for a parameter that is a nondifferentiable transform of a regular parameter, where the nondifferentiable transform is a composite map of a continuous piecewise linear map with a single kink point and a translation-scale equivariant map. The contribution of this paper is two fold. First, this paper extends the local asymptotic minimax theory to nondifferentiable transforms that are a composite map of a Lipschitz continuous map having a finite set of nondifferentiability points and a translation-scale equivariant map. Second, this paper investigates the discontinuity of the local asymptotic minimax risk in the true probability and shows that the proposed estimator remains to be optimal even when the risk is locally robustified not only over the scores at the true probability, but also over the true probability itself. However, the local robustification does not resolve the issue of discontinuity in the local asymptotic minimax risk

Linkage mapping of the gpdA gene of Aspergillus nidulans

In the last few years many genes of several Aspergillus species have been cloned and sequenced. For many of these genes mutant alleles and genetic linkage data are also available. However, for those genes for which no mutant alleles have been isolated, genetic mapping was not possible. Here we report linkage mapping of the glyceraldehyde-3- phosphate dehydrogenase gene (gpdA) of A. nidulans for which no mutant alleles have been isolated. The method used is applicable to all other cloned genes

Adaptive Alert Management for Balancing Optimal Performance among Distributed CSOCs using Reinforcement Learning

Large organizations typically have Cybersecurity Operations Centers (CSOCs) distributed at multiple locations that are independently managed, and they have their own cybersecurity analyst workforce. Under normal operating conditions, the CSOC locations are ideally staffed such that the alerts generated from the sensors in a work-shift are thoroughly investigated by the scheduled analysts in a timely manner. Unfortunately, when adverse events such as increase in alert arrival rates or alert investigation rates occur, alerts have to wait for a longer duration for analyst investigation, which poses a direct risk to organizations. Hence, our research objective is to mitigate the impact of the adverse events by dynamically and autonomously re-allocating alerts to other location(s) such that the performances of all the CSOC locations remain balanced. This is achieved through the development of a novel centralized adaptive decision support system whose task is to re-allocate alerts from the affected locations to other locations. This re-allocation decision is non-trivial because the following must be determined: (1) timing of a re-allocation decision, (2) number of alerts to be re-allocated, and (3) selection of the locations to which the alerts must be distributed. The centralized decision-maker (henceforth referred to as agent) continuously monitors and controls the level of operational effectiveness-LOE (a quantified performance metric) of all the locations. The agent's decision-making framework is based on the principles of stochastic dynamic programming and is solved using reinforcement learning (RL). In the experiments, the RL approach is compared with both rule-based and load balancing strategies. By simulating real-world scenarios, learning the best decisions for the agent, and applying the decisions on sample realizations of the CSOC's daily operation, the results show that the RL agent outperforms both approaches by generating (near-) optimal decisions that maintain a balanced LOE among the CSOC locations. Furthermore, the scalability experiments highlight the practicality of adapting the method to a large number of CSOC locations

Abciximab‐Associated Thrombocytopenia After Previous Tirofiban‐Related Thrombocytopenia

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90080/1/phco.26.3.423.pd

Derived categories of Burniat surfaces and exceptional collections

We construct an exceptional collection $\Upsilon$ of maximal possible length 6 on any of the Burniat surfaces with $K_X^2=6$, a 4-dimensional family of surfaces of general type with $p_g=q=0$. We also calculate the DG algebra of endomorphisms of this collection and show that the subcategory generated by this collection is the same for all Burniat surfaces. The semiorthogonal complement $\mathcal A$ of $\Upsilon$ is an "almost phantom" category: it has trivial Hochschild homology, and K_0(\mathcal A)=\bZ_2^6.Comment: 15 pages, 1 figure; further remarks expande

CHIP(-/-)-Mouse Liver: Adiponectin-AMPK-FOXO-Activation Overrides CYP2E1-Elicited JNK1-Activation, Delaying Onset of NASH: Therapeutic Implications.

Genetic ablation of C-terminus of Hsc70-interacting protein (CHIP) E3 ubiquitin-ligase impairs hepatic cytochrome P450 CYP2E1 degradation. Consequent CYP2E1 gain of function accelerates reactive O2 species (ROS) production, triggering oxidative/proteotoxic stress associated with sustained activation of c-Jun NH2-terminal kinase (JNK)-signaling cascades, pro-inflammatory effectors/cytokines, insulin resistance, progressive hepatocellular ballooning and microvesicular steatosis. Despite this, little evidence of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) was found in CHIP(-/-)-mice over the first 8-9-months of life. We herein document that this lack of tissue injury is largely due to the concurrent up-regulation and/or activation of the adiponectin-5'-AMP-activated protein kinase (AMPK)-forkhead box O (FOXO)-signaling axis stemming from at the least three synergistic features: Up-regulated expression of adipose tissue adiponectin and its hepatic adipoR1/adipoR2 receptors, stabilization of hepatic AMPKα1-isoform, identified herein for the first time as a CHIP-ubiquitination substrate (unlike its AMPKα2-isoform), as well as nuclear stabilization of FOXOs, well-known CHIP-ubiquitination targets. Such beneficial predominance of the adiponectin-AMPK-FOXO-signaling axis over the sustained JNK-elevation and injurious insulin resistance in CHIP(-/-)-livers apparently counteracts/delays rapid progression of the hepatic microvesicular steatosis to the characteristic macrovesicular steatosis observed in clinical NASH and/or rodent NASH-models

Pseudoacromegaly: A Differential Diagnostic Problem for Acromegaly With a Genetic Solution.

Acromegaly is usually not a difficult condition to diagnose once the possibility of this disease has been raised. However, a few conditions present with some aspects of acromegaly or gigantism but without growth hormone (GH) excess. Such cases are described as "pseudoacromegaly" or "acromegaloidism". Here we describe a female patient investigated for GH excess at 10 years of age for tall stature since infancy (height and weight > +3 standard deviations) and typical acromegalic features, including large hands/feet, large jaw, tongue, hoarse deep voice, and headache. Results of radiography of the sella turcica and GH response at an oral glucose tolerance test and insulin-arginine- thyrotrophin-luteinizing hormone-releasing hormone test were normal. Ethinylestradiol and medroxyprogesterone were given for 2 years; this successfully stopped further height increase. Although the patient's growth rate plateaued, coarsening of the facial features and acral enlargement also led to investigations for suspicion of acromegaly at 23 and 36 years of age, both with negative results. On referral at the age of 49 years, she had weight gain, sweating, sleep apnea, headaches, joint pain, and enlarged tongue. Endocrine assessment again showing normal GH axis was followed by genetic testing with a macrocephaly/overgrowth syndrome panel. A denovo mutation in the NSD1 gene (c.6605G>C; p.Cys2202Ser) was demonstrated. Mutations affecting the same cysteine residue have been identified in patients with Sotos syndrome. In summary, Sotos syndrome and other overgrowth syndromes can mimic the clinical manifestations of acromegaly or gigantism. Genetic assessment could be helpful in these cases