Screening and Assessment in TANF/Welfare-to-Work: Ten Important Questions TANF Agencies and Their Partners Should Consider

Changes to the welfare system brought about by the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), and state and local welfare reform efforts, carry serious implications for Temporary Assistance to Needy Families (TNF) recipients with disabilities and barriers to employment. Specifically, work participation and time limit requirements are two key provisions of the federal welfare law which provided a new sense of urgency encouraging states to develop strategies to assist clients with their transistions from welfare to work. As a first step in this process, TANF agencies are considering strategies to identify the barriers that are inhibiting or prohibiting this transition. PRWORA offers unprecedented flexibility to develop such strategies and design programs and services to assist with the transition from welfare to work. This paper is merely a first step in considering some of the many challenges associated with identifying unobserved barriers to employment

Estimating Public and Private Expenditures on Occupational Training in the United States

[Excerpt] Retraining and upgrading the skills of incumbent workers and providing training to new labor force entrants, dislocated workers, and unemployed persons can help increase the efficiency and effectiveness of the workforce. Funding for occupational training comes from many sources — the federal government, state and local governments, private employers, philanthropic foundations, and individual workers themselves. This report examines occupational training to present a preliminary picture of the total spending on job training in the United States

A finite state projection algorithm for the stationary solution of the chemical master equation

The chemical master equation (CME) is frequently used in systems biology to quantify the effects of stochastic fluctuations that arise due to biomolecular species with low copy numbers. The CME is a system of ordinary differential equations that describes the evolution of probability density for each population vector in the state-space of the stochastic reaction dynamics. For many examples of interest, this state-space is infinite, making it difficult to obtain exact solutions of the CME. To deal with this problem, the Finite State Projection (FSP) algorithm was developed by Munsky and Khammash (Jour. Chem. Phys. 2006), to provide approximate solutions to the CME by truncating the state-space. The FSP works well for finite time-periods but it cannot be used for estimating the stationary solutions of CMEs, which are often of interest in systems biology. The aim of this paper is to develop a version of FSP which we refer to as the stationary FSP (sFSP) that allows one to obtain accurate approximations of the stationary solutions of a CME by solving a finite linear-algebraic system that yields the stationary distribution of a continuous-time Markov chain over the truncated state-space. We derive bounds for the approximation error incurred by sFSP and we establish that under certain stability conditions, these errors can be made arbitrarily small by appropriately expanding the truncated state-space. We provide several examples to illustrate our sFSP method and demonstrate its efficiency in estimating the stationary distributions. In particular, we show that using a quantised tensor train (QTT) implementation of our sFSP method, problems admitting more than 100 million states can be efficiently solved.Comment: 8 figure

Placental 11b-hydroxysteroid dehydrogenase expression and birth weight in Hamilton County, TN

Infants born below 2,500 grams are classified as low birth w eight. Reduced birth weight has been shown to increase the risk of infant mortality and chronic adulthood diseases. In 2007, Hamilton Country reported 12.0% of live births to be low birth weight, compared to the state average of 9.4%. An excess in utero exposure to cortisol has been linked to restricted fetal growth. Placental production of 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) inactivates cortisol before passage into the fetus. This study tests the hypothesis that increased placental 11β-HSD2 expression has a positive correlation with an individualized birth weight centile. A Spearman\u27s rank correlation reported a significant correlation between these two variables (p = 0.024). Additionally, birth weight was significantly different between underweight and obese mothers, married vs. single mothers and black vs. white mothers. These results reinforce the importance of proper 11β-HSD2 expression for optimal fetal growth

Optogenetic actuator - ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics.

Combining single-cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single-cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK-RAF and the ERK-RSK2-SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2-mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2-dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2-mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors

Parameter inference for stochastic single-cell dynamics from lineage tree data

Background With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle. Results In this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Sequential Monte Carlo for approximating the parameter likelihood function and Markov Chain Monte Carlo for parameter exploration. We demonstrate our inference framework on two simple examples in which the lineage tree information is crucial: one in which the cell phenotype can only switch at cell division and another where the cell state fluctuates slowly over timescales that extend well beyond the cell-cycle duration. Conclusion There exist several examples of biological processes, such as stem cell fate decisions or epigenetically controlled phase variation in bacteria, where the cell ancestry is expected to contain important information about the underlying system dynamics. Parameter inference methods that discard this information are expected to perform poorly for such type of processes. Our method provides a simple and computationally efficient way to take into account single-cell lineage tree data for the purpose of parameter inference and serves as a starting point for the development of more sophisticated and powerful approaches in the future