Simulation analysis of the consequences of shifting the balance of health care: a system dynamics approach

Objectives: The shift in the balance of health care, bringing services 'closer to home', is a well-established trend. This study sought to provide insight into the consequences of this trend, in particular the stimulation of demand, by exploring the underlying feedback structure. Methods: We constructed a simulation model using the system dynamics method, which is specifically designed for the analysis of feedback structure. The model was calibrated to two cases of the shift in cardiac catheterization services in the UK. Data sources included archival data, observations and interviews with senior health care professionals. Key model outputs were the basic trends displayed by waiting lists, average waiting times, cumulative patient referrals, cumulative patient activity and cumulative overall costs. Results: Demand was stimulated in both cases via several different mechanisms. We revealed the roles for clinical guidelines and capacity changes, and the typical responses to imbalances between supply and demand. Our analysis also demonstrated the potential benefits of changing the goals that drive activity by seeking a waiting list goal rather than a waiting time goal. Conclusions: Appreciating the wider consequences of shifting the balance of care is essential if services are to be improved overall. The underlying feedback mechanisms of both intended and unintended effects need to be understood. Using a systemic approach, more effective policies may be designed through coordinated programmes rather than isolated initiatives, which may have only a limited impact

Impacts of a flaring star-forming disc and stellar radial mixing on the vertical metallicity gradient

Using idealized N-body simulations of a Milky Way-sized disc galaxy, we qualitatively study how the metallicity distributions of the thin disc star particles are modified by the formation of the bar and spiral arm structures. The thin disc in our numerical experiments initially has a tight negative radial metallicity gradient and a constant vertical scaleheight. We show that the radial mixing of stars drives a positive vertical metallicity gradient in the thin disc. On the other hand, if the initial thin disc is flared, with vertical scaleheight increasing with galactocentric radius, the metal-poor stars, originally in the outer disc, become dominant in regions above the disc plane at every radii. This process can drive a negative vertical metallicity gradient, which is consistent with the current observed trend. This model mimics a scenario where the star-forming thin disc was flared in the outer region at earlier epochs. Our numerical experiment with an initial flared disc predicts that the negative vertical metallicity gradient of the mono-age relatively young thin disc population should be steeper in the inner disc, and the radial metallicity gradient of the mono-age population should be shallower at greater heights above the disc plane. We also predict that the metallicity distribution function of mono-age young thin disc populations above the disc plane would be more positively skewed in the inner disc compared to the outer disc

Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs

Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone. We propose a new inference-time approach to enforcing syntactic and semantic constraints on the outputs of LLMs, called sequential Monte Carlo (SMC) steering. The key idea is to specify language generation tasks as posterior inference problems in a class of discrete probabilistic sequence models, and replace standard decoding with sequential Monte Carlo inference. For a computational cost similar to that of beam search, SMC can steer LLMs to solve diverse tasks, including infilling, generation under syntactic constraints, and prompt intersection. To facilitate experimentation with SMC steering, we present a probabilistic programming library, LLaMPPL (https://github.com/probcomp/hfppl), for concisely specifying new generation tasks as language model probabilistic programs, and automating steering of LLaMA-family Transformers.Comment: Minor typo fixe

LAS: a software platform to support oncological data management

The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data qualit

Renormalization Group Study of the soliton mass on the (lambda Phi^4)_{1+1} lattice model

We compute, on the $(\lambda \Phi^4)_{1+1}$ model on the lattice, the soliton mass by means of two very different numerical methods. First, we make use of a ``creation operator'' formalism, measuring the decay of a certain correlation function. On the other hand we measure the shift of the vacuum energy between the symmetric and the antiperiodic systems. The obtained results are fully compatible. We compute the continuum limit of the mass from the perturbative Renormalization Group equations. Special attention is paid to ensure that we are working on the scaling region, where physical quantities remain unchanged along any Renormalization Group Trajectory. We compare the continuum value of the soliton mass with its perturbative value up to one loop calculation. Both quantities show a quite satisfactory agreement. The first is slightly bigger than the perturbative one; this may be due to the contributions of higher order corrections.Comment: 19 pages, preprint DFTUZ/93/0

Rethinking place and the social work office in the delivery of children's social work services

Limited attention has been given to the concept of place in social work research and practice. This paper draws on the national evaluation of social work practices (SWPs) in England undertaken between 2009 and 2012. SWPs were pilot organisations providing independent social work services for children in out-of-home care in five sites. One factor distinguishing some of these pilots was their attention to place. The evaluation employed a mixed methods approach and we use data from interviews with 121 children and young people in out-of-home care, 19 birth parents and 31 interviews with SWP staff which explored their views and experiences of the SWP offices. Children and young people were alert to the stigma which could attach to social work premises and appreciated offices which were planned and furnished to appear less institutional and more ‘normal’. Daily interactions with staff which conveyed a sense of recognition and value to service users also contributed to a view of some SWP offices as accessible and welcoming places. Both children and parents appreciated offices that provided fun activities that positioned them as active rather than passive. Staff valued opportunities for influencing planning decisions about offices and place was seen to confer a value on them as well as on service users. However, not all the SWPs were able to achieve these aspects of place, and engaging children and families in place was less likely when the service user population was widely dispersed. Recognising the importance of place and how place is constructed through relationships between people as well as through the physical environment appeared to be key to creating offices that combated the stigma attached to out-of-home care. Those leading and managing children’s services should explore ways of involving local communities in planning social work offices and turn attention to making these offices accessible, welcoming, places

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

How does language inform our downstream thinking? In particular, how do humans make meaning from language -- and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose \textit{rational meaning construction}, a computational framework for language-informed thinking that combines neural models of language with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a \textit{probabilistic language of thought} (PLoT) -- a general-purpose symbolic substrate for probabilistic, generative world modeling. Our architecture integrates two powerful computational tools that have not previously come together: we model thinking with \textit{probabilistic programs}, an expressive representation for flexible commonsense reasoning; and we model meaning construction with \textit{large language models} (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language. We illustrate our framework in action through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning about agents and their plans. In each, we show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings, while Bayesian inference with the generated programs supports coherent and robust commonsense reasoning. We extend our framework to integrate cognitively-motivated symbolic modules to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves

Dual tumor suppressing and promoting function of Notch1 signaling in human prostate cancer.

Adenocarcinomas of the prostate arise as multifocal heterogeneous lesions as the likely result of genetic and epigenetic alterations and deranged cell-cell communication. Notch signaling is an important form of intercellular communication with a role in growth/differentiation control and tumorigenesis. Contrasting reports exist in the literature on the role of this pathway in prostate cancer (PCa) development. We show here that i) compared to normal prostate tissue, Notch1 expression is significantly reduced in a substantial fraction of human PCas while it is unaffected or even increased in others; ii) acute Notch activation both inhibits and induces process networks associated with prostatic neoplasms; iii) down-modulation of Notch1 expression and activity in immortalized normal prostate epithelial cells increases their proliferation potential, while increased Notch1 activity in PCa cells suppresses growth and tumorigenicity through a Smad3-dependent mechanism involving p21WAF1/CIP1; iv) prostate cancer cells resistant to Notch growth inhibitory effects retain Notch1-induced upregulation of pro-oncogenic genes, like EPAS1 and CXCL6, also overexpressed in human PCas with high Notch1 levels. Taken together, these results reconcile conflicting data on the role of Notch1 in prostate cancer