A Clustered Overflow Configuration of Inpatient Beds in Hospitals

Problem Definition: The shortage of inpatient beds is a major cause of delays and cancellations in many hospitals. It may also lead to patients being admitted to inappropriate wards, whereby resulting in a lower quality of care and a longer length of stay. Academic/Practical Relevance: Investment in additional beds is not always feasible. Instead, new and creative solutions for a more efficient use of existing resources must be sought. Methodology: We propose a new configuration of inpatient beds which we call the clustered overflow configuration. In this configuration, patients who are denied admission to their primary wards as a result of beds being fully occupied are admitted to overflow wards, with each designated to serve overflows from a certain subset of specialties and providing the same quality of care as in primary wards. We propose two different formulations for partitioning and bed allocation in the proposed configuration: one minimizing the sum of average daily costs of turning patients away and nursing teams, and another minimizing the numbers turned away subject to nursing cost falling below a given threshold. We heuristically solve instances from both formulations. Results: Applying the models to real data shows that the configurations obtained from our models compare very well with the other configurations proposed in the literature, provided that patients' willingness to wait is relatively short. Managerial Implications: The proposed configuration provides the combined advantages of the dedicated configuration, wherein patients are only admitted to their primary wards, and the exible configuration, in which all specialties share a single ward. On the other hand, it restricts the adverse impacts of pooling and minimizes cross-training costs through appropriate partitioning and bed allocation. As such, it serves as a viable alternative to existing inpatient configurations

Neural 3D Video Synthesis

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance fields that represents scene dynamics using a set of compact latent codes. To exploit the fact that changes between adjacent frames of a video are typically small and locally consistent, we propose two novel strategies for efficient training of our neural network: 1) An efficient hierarchical training scheme, and 2) an importance sampling strategy that selects the next rays for training based on the temporal variation of the input videos. In combination, these two strategies significantly boost the training speed, lead to fast convergence of the training process, and enable high quality results. Our learned representation is highly compact and able to represent a 10 second 30 FPS multi-view video recording by 18 cameras with a model size of just 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for highly complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the current state of the art. We include additional video and information at: https://neural-3d-video.github.io/Comment: Project website: https://neural-3d-video.github.io

Adenosine-mono-phosphate-activated protein kinase-independent effects of metformin in T cells

The anti-diabetic drug metformin regulates T-cell responses to immune activation and is proposed to function by regulating the energy-stress-sensing adenosine-monophosphate-activated protein kinase (AMPK). However, the molecular details of how metformin controls T cell immune responses have not been studied nor is there any direct evidence that metformin acts on T cells via AMPK. Here, we report that metformin regulates cell growth and proliferation of antigen-activated T cells by modulating the metabolic reprogramming that is required for effector T cell differentiation. Metformin thus inhibits the mammalian target of rapamycin complex I signalling pathway and prevents the expression of the transcription factors c-Myc and hypoxia-inducible factor 1 alpha. However, the inhibitory effects of metformin on T cells did not depend on the expression of AMPK in T cells. Accordingly, experiments with metformin inform about the importance of metabolic reprogramming for T cell immune responses but do not inform about the importance of AMPK

The Conformal Manifold of Chern-Simons Matter Theories

We determine perturbatively the conformal manifold of N=2 Chern-Simons matter theories with the aim of checking in the three dimensional case the general prescription based on global symmetry breaking, recently introduced. We discuss in details few remarkable cases like the N=6 ABJM theory and its less supersymmetric generalizations with/without flavors. In all cases we find perfect agreement with the predictions of global symmetry breaking prescription.Comment: 1+17 pages, 1 figure, references adde

Million km² large-extent snowdrift-permitting snowpack predictions

Global Water Future

A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis

<p>Abstract</p> <p>Objectives</p> <p>A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility.</p> <p>Method</p> <p>Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery.</p> <p>Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.</p> <p>As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission.</p> <p>Results</p> <p>We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times.</p> <p>Conclusions</p> <p>One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.</p

Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach

We introduce a novel approach to model heat transport in solids, based on the Green-Kubo theory of linear response. It naturally bridges the Boltzmann kinetic approach in crystals and the Allen-Feldman model in glasses, leveraging interatomic force constants and normal-mode linewidths computed at mechanical equilibrium. At variance with molecular dynamics, our approach naturally and easily accounts for quantum mechanical effects in energy transport. Our methodology is carefully validated against results for crystalline and amorphous silicon from equilibrium molecular dynamics and, in the former case, from the Boltzmann transport equation

CYberinfrastructure for COmparative effectiveness REsearch (CYCORE): improving data from cancer clinical trials

Improved approaches and methodologies are needed to conduct comparative effectiveness research (CER) in oncology. While cancer therapies continue to emerge at a rapid pace, the review, synthesis, and dissemination of evidence-based interventions across clinical trials lag in comparison. Rigorous and systematic testing of competing therapies has been clouded by age-old problems: poor patient adherence, inability to objectively measure the environmental influences on health, lack of knowledge about patients’ lifestyle behaviors that may affect cancer’s progression and recurrence, and limited ability to compile and interpret the wide range of variables that must be considered in the cancer treatment. This lack of data integration limits the potential for patients and clinicians to engage in fully informed decision-making regarding cancer prevention, treatment, and survivorship care, and the translation of research results into mainstream medical care. Particularly important, as noted in a 2009 report on CER to the President and Congress, the limited focus on health behavior-change interventions was a major hindrance in this research landscape (DHHS 2009). This paper describes an initiative to improve CER for cancer by addressing several of these limitations. The Cyberinfrastructure for Comparative Effectiveness Research (CYCORE) project, informed by the National Science Foundation’s 2007 report “Cyberinfrastructure Vision for 21st Century Discovery” has, as its central aim, the creation of a prototype for a user-friendly, open-source cyberinfrastructure (CI) that supports acquisition, storage, visualization, analysis, and sharing of data important for cancer-related CER. Although still under development, the process of gathering requirements for CYCORE has revealed new ways in which CI design can significantly improve the collection and analysis of a wide variety of data types, and has resulted in new and important partnerships among cancer researchers engaged in advancing health-related CI

An element through the looking glass: Exploring the Au-C, Au-H and Au-O energy landscape

Gold, the archetypal “noble metal”, used to be considered of little interest in catalysis. It is now clear that this was a misconception, and a multitude of gold-catalysed transformations has been reported. However, one consequence of the long-held view of gold as inert metal is that its organometallic chemistry contains many “unknowns”, and catalytic cycles devised to explain gold's reactivity draw largely on analogies with other transition metals. How realistic are such mechanistic assumptions? In the last few years a number of key compound classes have been discovered that can provide some answers. This Perspective attempts to summarise these developments, with particular emphasis on recently discovered gold(III) complexes with bonds to hydrogen, oxygen, alkenes and CO ligands