From Correctional Custody to Community: The Massachusetts Forensic Transition Program

Offenders with mental illness who are serving correctional sentences are released to the community.Without support systems linking their transition to community-based programs following release from prison, the services necessary for their community reintegration are often fragmented and attenuated. Nearly two thirds of all inmates return to prison, and offenders with mental illness face major challenges during reintegration and have an even more difficult time living in the community without specialized, informed services. This article describes a Massachusetts program designed to bridge the transition of offenders with mental illness from incarceration to the community.The authors review historical and recent trends that support the need for such a program along with a description of the demographics of the population served and the challenges faced during program implementation. They also offer recommendations for enhancing public safety and providing efficient service to offenders with mental illness

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Gene networks and liar paradoxes

Network motifs are small patterns of connections, found over-represented in gene regulatory networks. An example is the negative feedback loop (e.g. factor A represses itself). This opposes its own state so that when ‘on’ it tends towards ‘off’ – and vice versa. Here, we argue that such self-opposition, if considered dimensionlessly, is analogous to the liar paradox: ‘This statement is false’. When ‘true’ it implies ‘false’ – and vice versa. Such logical constructs have provided philosophical consternation for over 2000 years. Extending the analogy, other network topologies give strikingly varying outputs over different dimensions. For example, the motif ‘A activates B and A. B inhibits A’ can give switches or oscillators with time only, or can lead to Turing-type patterns with both space and time (spots, stripes or waves). It is argued here that the dimensionless form reduces to a variant of ‘The following statement is true. The preceding statement is false’. Thus, merely having a static topological description of a gene network can lead to a liar paradox. Network diagrams are only snapshots of dynamic biological processes and apparent paradoxes can reveal important biological mechanisms that are far from paradoxical when considered explicitly in time and space

Towards computational prediction of microRNA function and activity

While it has been established that microRNAs (miRNAs) play key roles throughout development and are dysregulated in many human pathologies, the specific processes and pathways regulated by individual miRNAs are mostly unknown. Here, we use computational target predictions in order to automatically infer the processes affected by human miRNAs. Our approach improves upon standard statistical tools by addressing specific characteristics of miRNA regulation. Our analysis is based on a novel compendium of experimentally verified miRNA-pathway and miRNA-process associations that we constructed, which can be a useful resource by itself. Our method also predicts novel miRNA-regulated pathways, refines the annotation of miRNAs for which only crude functions are known, and assigns differential functions to miRNAs with closely related sequences. Applying our approach to groups of co-expressed genes allows us to identify miRNAs and genomic miRNA clusters with functional importance in specific stages of early human development. A full list of the predicted mRNA functions is available at http://acgt.cs.tau.ac.il/fame/

Investment decision-making under economic policy uncertainty

It is widely established that economic policy uncertainty (EPU) affects investment decisions and performance, yet research in this area has overlooked the direct property investment market. This article seeks to rectify this and proposes a multistage multilevel analytical framework to offer new insights and a richness of findings. Using a news-based measure of EPU in the United Kingdom, and controlling for economic conditions, a national-level analysis reveals some evidence of Granger-Causality between EPU and total returns, indicating that pricing is responsive to uncertainty. These findings suggest that EPU is an important risk factor for direct property investments, with pricing implications. Differences in data and performance measure are important, however, with income returns unresponsive. A micro-level investigation begins to reveal some of the asset-pricing decisions underpinning the national results, indicating investors’ concerns for income streams are consistently high, regardless of varying EPU. Pricing can also cause changes in EPU, such as in the retail and industrial markets (increasingly linked through logistics) reflecting sector-specific stakeholder groups and newsworthy issues. This evidence highlights how important it is for policy-makers to understand the complex and bi-directional relationship, that indecision can undermine investment confidence and cause investment market volatility, in turn raising EPU

Transgenerational plasticity and selection shape the adaptive potential of sticklebacks to salinity change

In marine climate change research, salinity shifts have been widely overlooked. While widespread desalination effects are expected in higher latitudes, salinity is predicted to increase closer to the equator. Here, we use the steep salinity gradient of the Baltic Sea as a space‐for time design to address effects of salinity change on populations. Additionally, genetic diversity, a prerequisite for adaptive responses, is reduced in Baltic compared to Atlantic populations. On the one hand, adaptive transgenerational plasticity (TGP) might buffer the effects of environmental change, which may be of particular importance under reduced genetic variation. On the other hand, physiological trade‐offs due to environmental stress may hamper parental provisioning to offspring thereby intensifying the impact of climate change across generations (non‐adaptive TGP). Here, we studied both hypothesis of adaptive and non‐adaptive TGP in the three‐spined stickleback (Gasterosteus aculeatus) fish model along the strong salinity gradient of the Baltic Sea in a space‐for‐time experiment. Each population tolerated desalination well, which was not altered by parental exposure to low salinity. Despite a common marine ancestor, populations locally adapted to low salinity lost their ability to cope with fully marine conditions, resulting in lower survival and reduced relative fitness. Negative transgenerational effects were evident in early life stages, but disappeared after selection via mortality occurred during the first 12‐30 days post hatch. Modeling various strengths of selection, we showed that non‐adaptive transgenerational plasticity accelerated evolution by increasing directional selection within the offspring generation. Qualitatively, when genetic diversity is large, we predict that such effects will facilitate rapid adaptation and population persistence, while below a certain threshold populations suffer a higher risk of local extinction. Overall, our results suggest that transgenerational plasticity and selection are not independent of each other and thereby highlight a current gap in TGP studies

Insulin-Producing Cells Generated from Dedifferentiated Human Pancreatic Beta Cells Expanded In Vitro

Expansion of beta cells from the limited number of adult human islet donors is an attractive prospect for increasing cell availability for cell therapy of diabetes. However, attempts at expanding human islet cells in tissue culture result in loss of beta-cell phenotype. Using a lineage-tracing approach we provided evidence for massive proliferation of beta-cell-derived (BCD) cells within these cultures. Expansion involves dedifferentiation resembling epithelial-mesenchymal transition (EMT). Epigenetic analyses indicate that key beta-cell genes maintain open chromatin structure in expanded BCD cells, although they are not transcribed. Here we investigated whether BCD cells can be redifferentiated into beta-like cells.Redifferentiation conditions were screened by following activation of an insulin-DsRed2 reporter gene. Redifferentiated cells were characterized for gene expression, insulin content and secretion assays, and presence of secretory vesicles by electron microscopy. BCD cells were induced to redifferentiate by a combination of soluble factors. The redifferentiated cells expressed beta-cell genes, stored insulin in typical secretory vesicles, and released it in response to glucose. The redifferentiation process involved mesenchymal-epithelial transition, as judged by changes in gene expression. Moreover, inhibition of the EMT effector SLUG (SNAI2) using shRNA resulted in stimulation of redifferentiation. Lineage-traced cells also gave rise at a low rate to cells expressing other islet hormones, suggesting transition of BCD cells through an islet progenitor-like stage during redifferentiation.These findings demonstrate for the first time that expanded dedifferentiated beta cells can be induced to redifferentiate in culture. The findings suggest that ex-vivo expansion of adult human islet cells is a promising approach for generation of insulin-producing cells for transplantation, as well as basic research, toxicology studies, and drug screening

Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector

Results of a search for H → τ τ decays are presented, based on the full set of proton-proton collision data recorded by the ATLAS experiment at the LHC during 2011 and 2012. The data correspond to integrated luminosities of 4.5 fb−1 and 20.3 fb−1 at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV respectively. All combinations of leptonic (τ → `νν¯ with ` = e, µ) and hadronic (τ → hadrons ν) tau decays are considered. An excess of events over the expected background from other Standard Model processes is found with an observed (expected) significance of 4.5 (3.4) standard deviations. This excess provides evidence for the direct coupling of the recently discovered Higgs boson to fermions. The measured signal strength, normalised to the Standard Model expectation, of µ = 1.43 +0.43 −0.37 is consistent with the predicted Yukawa coupling strength in the Standard Model

Interacting with Fictions:The Role of Pretend Play in Theory of Mind Acquisition

Pretend play is generally considered to be a developmental landmark in Theory of Mind acquisition. The aim of the present paper is to offer a new account of the role of pretend play in Theory of Mind development. To this end I combine Hutto and Gallagher’s account of social cognition development with Matravers’ recent argument that the cognitive processes involved in engagement with narratives are neutral regarding fictionality. The key contribution of my account is an analysis of pretend play as interaction with fictions. I argue that my account offers a better explanation of existing empirical data on the development of children’s pretend play and Theory of Mind than the competing theories from Leslie, Perner and Harris