(WP 2005-03) Openness, Centralized Wage Bargaining, and Inflation

This paper develops a model of an open economy containing both sectors in which wages are market-determined and sectors with wage-setting arrangements. A portion of the latter group of sectors coordinate their wages, taking into account that their collective actions influence the equilibrium inflation outcome in an environment in which the central bank engages in discretionary monetary policymaking. Key predictions forthcoming from this model are (1) increased centralization of wage setting initially causes inflation to increase at low degrees of wage centralization but then, as wage centralization increases, results in an inflation dropoff; (2) a greater degree of centralized wage setting reduces the inflation-restraining effect of greater central bank independence; and (3) increased openness is more likely to reduce inflation in nations with less centralized wage bargaining. Analysis of data for seventeen nations for the period 1970-1999 provides generally strong and robust empirical support for all three of these predictions

Openness, Centralized Wage Bargaining, and Inflation

This paper develops a model of an open economy containing both sectors in which wages are market-determined and sectors with wage-setting arrangements. A portion of the latter group of sectors coordinate their wages, taking into account that their collective actions influence the equilibrium inflation outcome in an environment in which the central bank engages in discretionary monetary policymaking. Key predictions forthcoming from this model are (1) increased centralization of wage setting initially causes inflation to increase at low degrees of wage centralization but then, as wage centralization increases, results in an inflation dropoff; (2) a greater degree of centralized wage setting reduces the inflation-restraining effect of greater central bank independence; and (3) increased openness is more likely to reduce inflation in nations with less centralized wage bargaining. Analysis of data for seventeen nations for the period 1970-1999 provides generally strong and robust empirical support for all three of these predictions.Openness, Centralized wage setting, inflation

Optimal Quantum Circuits for General Two-Qubit Gates

In order to demonstrate non-trivial quantum computations experimentally, such as the synthesis of arbitrary entangled states, it will be useful to understand how to decompose a desired quantum computation into the shortest possible sequence of one-qubit and two-qubit gates. We contribute to this effort by providing a method to construct an optimal quantum circuit for a general two-qubit gate that requires at most 3 CNOT gates and 15 elementary one-qubit gates. Moreover, if the desired two-qubit gate corresponds to a purely real unitary transformation, we provide a construction that requires at most 2 CNOTs and 12 one-qubit gates. We then prove that these constructions are optimal with respect to the family of CNOT, y-rotation, z-rotation, and phase gates.Comment: 6 pages, 8 figures, new title, final journal versio

First-in-man evaluation of 124I-PGN650: A PET tracer for detecting phosphatidylserine as a biomarker of the solid tumor microenvironment

Purpose: PGN650 is a F(ab′) 2 antibody fragment that targets phosphatidylserine (PS), a marker normally absent that becomes exposed on tumor cells and tumor vasculature in response to oxidative stress and increases in response to therapy. PGN650 was labeled with 124 I to create a positron emission tomography (PET) agent as an in vivo biomarker for tumor microenvironment and response to therapy. In this phase 0 study, we evaluated the pharmacokinetics, safety, radiation dosimetry, and tumor targeting of this tracer in a cohort of patients with cancer. Methods: Eleven patients with known solid tumors received approximately 140 MBq (3.8 mCi) 124 I-PGN650 intravenously and underwent positron emission tomography–computed tomography (PET/CT) approximately 1 hour, 3 hours, and either 24 hours or 48 hours later to establish tracer kinetics for the purpose of calculating radiation dosimetry (from integration of the organ time-activity curves and OLINDA/EXM using the adult male and female models). Results: Known tumor foci demonstrated mildly increased uptake, with the highest activity at the latest imaging time. There were no unexpected adverse events. The liver was the organ receiving the highest radiation dose (0.77 mGy/MBq); the effective dose was 0.41 mSv/MBq. Conclusion: Although 124 I-PGN650 is safe for human PET imaging, the tumor targeting with this agent in patients was less than previously observed in animal studies

The tautological ring of Mg,n via Pandharipande-Pixton-Zvonkine r-spin relations

We use relations in the tautological ring of the moduli spaces Mg,n derived by Pandharipande, Pixton, and Zvonkine from the Givental formula for the r-spin Witten class in order to obtain some restrictions on the dimensions of the tautological rings of the open moduli spacesMg,n. In particular, we give a new proof for the result of Looijenga (for n = 1) and Buryak et al. (for n > 2) that dimRg-1(Mg,n) ≤ n. We also give a new proof of the result of Looijenga (for n = 1) and Ionel (for arbitrary n > 1) that Ri(Mg,n) = 0 for i > g and give some estimates for the dimension of Ri(Mg,n) for i ≤ g - 2

Ethical thinking machines in surgery and the requirement for clinical leadership

No abstract available

The development and application of an oncology Therapy-Related Symptom Checklist for Adults (TRSC) and Children (TRSC-C) and e-health applications.

BACKGROUND: Studies found that treatment symptoms of concern to oncology/hematology patients were greatly under-identified in medical records. On average, 11.0 symptoms were reported of concern to patients compared to 1.5 symptoms identified in their medical records. A solution to this problem is use of an electronic symptom checklist that can be easily accessed by patients prior to clinical consultations. PURPOSE: Describe the oncology Therapy-Related Symptom Checklists for Adults (TRSC) and Children (TRSC-C), which are validated bases for e-Health symptom documentation and management. The TRSC has 25 items/symptoms; the TRSC-C has 30 items/symptoms. These items capture up to 80% of the variance of patient symptoms. Measurement properties and applications with outpatients are presented. E-Health applications are indicated. METHODS: The TRSC was developed for adults (N = 282) then modified for children (N = 385). Statistical analyses have been done using correlational, epidemiologic, and qualitative methods. Extensive validation of measurement properties has been reported. RESULTS: Research has found high levels of patient/clinician satisfaction, no increase in clinic costs, and strong correlations of TRSC/TRSC-C with medical outcomes. A recently published sequential cohort trial with adult outpatients at a Mayo Clinic community cancer center found TRSC use produced a 7.2% higher patient quality of life, 116% more symptoms identified/managed, and higher functional status. DISCUSSION, IMPLICATIONS, AND FOLLOW-UP: An electronic system has been built to collect TRSC symptoms, reassure patients, and enhance patient-clinician communications. This report discusses system design and efforts made to provide an electronic system comfortable to patients. Methods used by clinicians to promote comfort and patient engagement were examined and incorporated into system design. These methods included (a) conversational data collection as opposed to survey style or standardized questionnaires, (b) short response phrases indicating understanding of the reported symptom, (c) use of open-ended questions to reduce long lists of symptoms, (d) directed questions that ask for confirmation of expected symptoms, (e) review of symptoms at designated stages, and (d) alerting patients when the computer has informed clinicians about patient-reported symptoms. CONCLUSIONS: An e-Health symptom checklist (TRSC/TRSC-C) can facilitate identification, monitoring, and management of symptoms; enhance patient-clinician communications; and contribute to improved patient outcomes

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

PURPOSE: We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. METHODS: TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV RESULTS: Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV CONCLUSIONS: We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial

Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (OR=0.44, confidence interval [CI]=0.25-0.78), BMI (OR=0.94,CI=0.89-0.99) and diabetes (OR=2.33,CI=1.18-4.60). Patients with diabetes were almost three times more likely to return to the operating room (OR=2.78,CI=1.31-5.88). Patients with a history of smoking were four times more likely to experience postoperative infection (OR=4.20,CI=1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC=0.86), a complication within 12 months (AUC=0.91), return to the operating room (AUC=0.88) and infection (AUC=0.97). Age, BMI, procedure side, gender and a diagnosis of Parkinson’s disease were influential features. Conclusions: Multiple significant complication risk factors were identified and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery