Antigen and Memory CD8 T Cells: Were They Both Right?

<p/> <p>Picture yourself as a researcher in immunology. To begin your project, you ask a question: Do CD8 T cells require antigen to maintain a memory response? This question is of prime importance to numerous medical fields. In chronologic order, you digest the literature, but unfortunately, you hit a major stumbling block in the 1990s. The crux of the problem is that which so often happens in science: two well-recognized, capable groups emerge with diametrically opposed conclusions, leaving you pondering which set of wellcontrolled data to believe. Fortunately, years later, a surprising group of articles sheds light on this mystery and subtly reconciles these two positions.</p

Mitigation Strategies for Acute Radiation Exposure during Space Flight

While there are many potential risks in a Moon or Mars mission, one of the most important and unpredictable is that of crew radiation exposure. The two forms of radiation that impact a mission far from the protective environment of low-earth orbit, are solar particle events (SPE) and galactic cosmic radiation (GCR). The effects of GCR occur as a long-term cumulative dose that results increased longer-term medical risks such as malignancy and neurological degeneration. Unfortunately, relatively little has been published on the medical management of an acute SPE that could potentially endanger the mission and harm the crew. Reanalysis of the largest SPE in August 1972 revealed that the dose rate was significantly higher than previously stated in the literature. The peak dose rate was 9 cGy h(sup -1) which exceeds the low dose-rate criteria for 25 hrs (National Council on Radiation Protection) and 16 hrs (United Nations Scientific Committee on the Effects of Atomic Radiation). The bone marrow dose accumulated was 0.8 Gy, which exceeded the 25 and 16 hour criteria and would pose a serious medical risk. Current spacesuits would not provide shielding from the damaging effects for an SPE as large as the 1972 event, as increased shielding from 1-5 grams per square centimeters would do little to shield the bone marrow from exposure. Medical management options for an acute radiation event are discussed based on recommendations from the Department of Homeland Security, Centers for Disease Control and evidence-based scientific literature. The discussion will also consider how to define acute exposure radiation safety limits with respect to exploration-class missions, and to determine the level of care necessary for a crew that may be exposed to an SPE similar to August 1972

Pre-Conditioners and Relations between Different Measures of Conditioning for Conic Linear Systems

In recent years, new and powerful research into "condition numbers" for convex optimization has been developed, aimed at capturing the intuitive notion of problem behavior. This research has been shown to be important in studying the efficiency of algorithms, including interior-point algorithms, for convex optimization as well as other behavioral characteristics of these problems such as problem geometry, deformation under data perturbation, etc. This paper studies measures of conditioning for a conic linear system of the form (FPd): Ax = b, x E Cx, whose data is d = (A, b). We present a new measure of conditioning, denoted pd, and we show implications of lid for problem geometry and algorithm complexity, and demonstrate that the value of = id is independent of the specific data representation of (FPd). We then prove certain relations among a variety of condition measures for (FPd), including ld, pad, Xd, and C(d). We discuss some drawbacks of using the condition number C(d) as the sole measure of conditioning of a conic linear system, and we then introduce the notion of a "pre-conditioner" for (FPd) which results in an equivalent formulation (FPj) of (FPd) with a better condition number C(d). We characterize the best such pre-conditioner and provide an algorithm for constructing an equivalent data instance d whose condition number C(d) is within a known factor of the best possible

Assignment of Swimmers to Dual Meet Events

Every fall, thousands of high school swimming coaches across the country begin the arduous process of preparing their athletes for competition. With a grueling practice schedule and a dedicated group of athletes, a coach can hone the squad into a cohesive unit, poised for any competition. However, oftentimes all preparation is in vain, as coaches assign swimmers to events with a lineup that is far from optimal. This paper provides a model that may help a high school (or other level) swim team coach make these assignments. Following state and national guidelines for swim meets, we describe a binary integer model that determines an overall assignment that maximizes the total number of points scored by the squad based on the times for swimmers on the squad and for the expected opponent

New Methods for Resolving Conflicting Requests with Examples from Medical Residency Scheduling

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138246/1/poms12728.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138246/2/poms12728-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138246/3/poms12728_am.pd

The Impact Grants Initiative: Community-Participatory Grantmaking Modeled on Venture Philanthropy

· The Jewish Community Federation and Endowment Fund (JCF) launched the Impact Grants Initiative (IGI), a model of grant making based on venture philanthropy, but offering high engagement opportunities for previously unaffiliated local donors and community leaders. · Before adopting the IGI model, the JCF used a community-participatory grantmaking approach that had become stale in engaging its donors, community leaders, and professional staff. Younger existing and potential donors were developing interests in documented outcomes, metrics, and impact, and those interests did not align with JCF’s grantmaking approach. · IGI builds on the concepts of venture philanthropy, combining theories and techniques used in venture capital and corporate business with philanthropic practice, provides a platform to highly engage donors, and results in strategic and tactical community investments. It focuses extensively on generating measurable results that are realistic and attainable and allows for the periodic reallocation of resources based on evolving priorities and goals. · Participation in IGI grant rounds has more than doubled the number of community members engaged in philanthropy through the JCF

Simplex Algorithm for Countable-state Discounted Markov Decision Processes

Submitted to Operations Research; preliminary version.We consider discounted Markov Decision Processes (MDPs) with countably-infinite state spaces, finite action spaces, and unbounded rewards. Typical examples of such MDPs are inventory management and queueing control problems in which there is no specific limit on the size of inventory or queue. Existing solution methods obtain a sequence of policies that converges to optimality in value but may not improve monotonically, i.e., a policy in the sequence may be worse than preceding policies. Our proposed approach considers countably-infinite linear programming (CILP) formulations of the MDPs (a CILP is defined as a linear program (LP) with countably-infinite numbers of variables and constraints). Under standard assumptions for analyzing MDPs with countably-infinite state spaces and unbounded rewards, we extend the major theoretical extreme point and duality results to the resulting CILPs. Under an additional technical assumption which is satisfied by several applications of interest, we present a simplex-type algorithm that is implementable in the sense that each of its iterations requires only a finite amount of data and computation. We show that the algorithm finds a sequence of policies which improves monotonically and converges to optimality in value. Unlike existing simplex-type algorithms for CILPs, our proposed algorithm solves a class of CILPs in which each constraint may contain an infinite number of variables and each variable may appear in an infinite number of constraints. A numerical illustration for inventory management problems is also presented.National Science Foundation grant CMMI-1333260A research grant from the University of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/109413/1/CountableStateMDP-MAE.pdfDescription of CountableStateMDP-MAE.pdf : Main article (preliminary version

A new column-generation-based algorithm for VMAT treatment plan optimization

We study the treatment plan optimization problem for volumetric modulated arc therapy (VMAT). We propose a new column-generation-based algorithm that takes into account bounds on the gantry speed and dose rate, as well as an upper bound on the rate of change of the gantry speed, in addition to MLC constraints. The algorithm iteratively adds one aperture at each control point along the treatment arc. In each iteration, a restricted problem optimizing intensities at previously selected apertures is solved, and its solution is used to formulate a pricing problem, which selects an aperture at another control point that is compatible with previously selected apertures and leads to the largest rate of improvement in the objective function value of the restricted problem. Once a complete set of apertures is obtained, their intensities are optimized and the gantry speeds and dose rates are adjusted to minimize treatment time while satisfying all machine restrictions. Comparisons of treatment plans obtained by our algorithm to idealized IMRT plans of 177 beams on five clinical prostate cancer cases demonstrate high quality with respect to clinical dose–volume criteria. For all cases, our algorithm yields treatment plans that can be delivered in around 2 min. Implementation on a graphic processing unit enables us to finish the optimization of a VMAT plan in 25–55 s.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98593/1/0031-9155_57_14_4569.pd

Condition number complexity of an elementary algorithm for computing a reliable solution of a conic linear system

A conic linear system is a system of the form¶¶(FP d )Ax = b ¶ x ∈ C X ,¶¶where A:X ? Y is a linear operator between n - and m -dimensional linear spaces X and Y , b ∈ Y , and C X ⊂X is a closed convex cone. The data for the system is d =( A,b ). This system is “well-posed” to the extent that (small) changes in the data d =( A,b ) do not alter the status of the system (the system remains feasible or not). Renegar defined the “distance to ill-posedness,”ρ( d ), to be the smallest change in the data Δ d =(Δ A ,Δ b ) needed to create a data instance d +Δ d that is “ill-posed,” i.e., that lies in the intersection of the closures of the sets of feasible and infeasible instances d ′ =( A ′ , b ′ ) of (FP (·) ). Renegar also defined the condition number ?( d ) of the data instance d as the scale-invariant reciprocal of ρ( d ) : ?( d )= .¶In this paper we develop an elementary algorithm that computes a solution of (FP d ) when it is feasible, or demonstrates that (FP d ) has no solution by computing a solution of the alternative system. The algorithm is based on a generalization of von Neumann’s algorithm for solving linear inequalities. The number of iterations of the algorithm is essentially bounded by¶¶ O (  ?( d ) 2 ln(?( d )))¶¶where the constant depends only on the properties of the cone C X and is independent of data d . Each iteration of the algorithm performs a small number of matrix-vector and vector-vector multiplications (that take full advantage of the sparsity of the original data) plus a small number of other operations involving the cone C X . The algorithm is “elementary” in the sense that it performs only a few relatively simple computations at each iteration.¶The solution of the system (FP d ) generated by the algorithm has the property of being “reliable” in the sense that the distance from to the boundary of the cone C X , dist( ,∂ C X ), and the size of the solution, ∥ ∥, satisfy the following inequalities:¶¶∥ ∥≤ c 1 ?( d ),dist( ,∂ C X )≥ c 2 , and ≤ c 3 ?( d ),¶¶where c 1 , c 2 , c 3 are constants that depend only on properties of the cone C X and are independent of the data d (with analogous results for the alternative system when the system (FP d ) is infeasible).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42344/1/10107-88-3-451_00880451.pd

Detection of Soluble Angiotensin-Converting Enzyme 2 in Heart Failure Insights Into the Endogenous Counter-Regulatory Pathway of the Renin-Angiotensin-Aldosterone System

ObjectivesWe sought to determine whether circulating soluble angiotensin-converting enzyme 2 (sACE2) is increased in the plasma of patients with heart failure (HF).BackgroundAngiotensin-converting enzyme 2 (ACE2) is an integral membrane protein that antagonizes the actions of angiotensin II and prevents the development of HF in animal models. However, because of the need for invasive cardiac tissue sampling, little is known about whether ACE2 is involved in the pathophysiology of HF in humans.MethodsWe developed a sensitive and specific assay to measure sACE2 activity in human plasma and screened a heterogeneous group of patients suspected of having clinical HF.ResultsIncreasing sACE2 plasma activity strongly correlated with a clinical diagnosis of HF (p = 0.0002), worsening left ventricular ejection fraction (p < 0.0001), and increasing B-type natriuretic peptide levels (p < 0.0001). Similar to B-type natriuretic peptide, sACE2 activity reflected the severity of HF, with increasing levels associated with worsening New York Heart Association functional class (p < 0.0001). These associations were independent of other disease states and medication use. We found that sACE2 activity was increased in patients with both ischemic and nonischemic cardiomyopathies and also in patients with clinical HF but a preserved left ventricular ejection fraction.ConclusionsSoluble ACE2 activity is increased in patients with HF and correlates with disease severity, suggesting that a cardioprotective arm of the renin-angiotensin-aldosterone system is active in HF