Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Modeling of Healthcare Capacity and Patient Needs to Intervene Against Human Transmitted Viral Disease

The Covid-19 pandemic has challenged the world to reduce the presence and consequence of a highly contagious and severe disease, in the absence of vaccines and effective medical treatments. Aggressive measures to isolate populations, and restrict movement, aim to reduce the number of Covid-19 cases, and to spread such cases over a longer period of time. A benefit of this strategy is to reduce the rates at which patients present at hospitals for care, so that hospitals are better able to accommodate the flow of Covid19 patients (i.e., so-called “flattening the curve” so that the number of cases does not exceed hospital capacity). This concept is the starting point for the proposed project, which aims to develop a comprehensive patient flow model to represent the interaction between the transmission of disease and the delivery of healthcare for afflicted patients. We recognize that in a crisis strategies exist to dynamically supplement hospital capacity (e.g., transforming spaces to add bed capacity, expanding scope of responsibility, acquiring resources, optimizing discharge, etc.), diverting patients or delaying nonurgent/emergent surgeries. Thus, we aim to reduce the consequences of viral disease through interventions that simultaneously affect the rates at which new cases occur and affect the provisioning of healthcare resources the needs to serve these cases. Zumberge funding would support preliminary research through data integration, model development, and preparation of proposals seeking external support. Modeling will emphasize improving the sensitivity of transmission models to interventions that limit the movement of people and impose their isolation, with emphasis on skilled nursing facilities, which have experienced much higher case rates and fatalities of COVID-19 than the population at large. Skilled nursing is also an important dimension of health-care delivery for COVID-19 patients, being able to accept discharged patients from acute-care hospitals and to care for patients with co-morbidities and chronic disease. A second emphasis of modeling is representing uncertainty in model parameters, which are not known with precision at the outset of an epidemic, yet can have enormous influence on the growth in number of cases. The Zumberge project relies on an interdisciplinary skill set in transportation, healthcare delivery, statistical modeling, and optimization. The PI has conducted research in all of these areas. By the end of the calendar year, the project team will be expanded to include healthcare economics and epidemiology for submission of a proposal for external funding from the Agency for Healthcare Research and Quality (AHRQ). The requested funding of $41,700 will support a doctoral student for one year. During that time, he will complete his dissertation proposal, qualifying exam, and submit a proposal to AHRQ for dissertation funding. An undergraduate student worker will also be supported, and additional funding will be used for data acquisition.
Inter Commodity Price Linkages in India: A Case of Foodgrains, Oilseeds and Edible Oils

The main objective of this paper is to analyze the nature of price inter-linkages among four commodity complexes, namely cereals, pulses, oil seeds and edible oils. The co-integration and errorcorrection analysis in the case of cereals led to the conclusion that there was a unique relationship among wheat, rice and spiked millet while great millet, maize, barley and finger millet did not belong to the cereals common market. Among pulses, lentil, green gram and Bengal gram constituted a single common market. In the case of oilseeds, only groundnut, mustard, sunflower and soybean belonged to the common market. In edible oils, groundnut, rape-seed-mustard and coconut formed a common market in the long run. In the late 1990’s, prices of oilseeds drifted away from the long-run equilibrium path because of a short period disturbance in the oilseeds market. However, the drift was not visible in the case of edible oils plausibly because of the presence of imported edible oils. The diversions were least for cereals and to some extent for pulses, evidently because of the cushion available in terms of procurement and minimum support price policy of Government of India in their cases.
Construction and Evaluation of Hemoglobin‐Based Capsules as Blood Substitutes

Hemoglobin‐based capsules for use as blood substitutes are successfully fabricated by covalent layer‐by‐layer assembly. Dialdehyde heparin (DHP) is used both as one of the wall components and a cross‐linker without employing other extraneous or toxic crosslinking agents. The biocompatibility of (Hb/DHP)6 microcapsules is evaluated through the 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide) (MTT) assay and cell experiments. The hemocompatibility of (Hb/DHP)6 microcapsules is characterized in terms of prothrombin time, thrombin time, activated partial thromboplastin time, and hemolysis rate. The oxygen‐carrying capacity of the microcapsules is demonstrated by converting the deoxy‐Hb state of the microcapsules into the oxy‐Hb state. All these results demonstrate that the hemoglobin‐based microcapsules exhibit oxygen‐carrying capacity as well as biocompatibility and hemocompatility, indicating that the as‐prepared capsules have great potential to function as blood substitutes.
Paper microfluidics for red wine tasting

A paper microfluidic chip was designed and fabricated to evaluate the taste of 10 different red wines using a set of chemical dyes. The digital camera of a smartphone captured the images, and its red-green-blue (RGB) pixel intensities were analyzed by principal component analysis (PCA). Using 8 dyes and 2 principal components (PCs), we were able to distinguish each wine by the grape variety and the oxidation status. Through comparing with the flavor map by human evaluation, PC1 seemed to represent the sweetness and PC2 the bodyness of red wine. This superior performance is attributed to: (1) careful selection of commercially available dyes through a series of linear correlation studies with the taste chemicals in red wines, (2) minimization of sample-to-sample variation by splitting a single sample into multiple wells on the paper microfluidics, and (3) filtration of particulate matter through paper fibers. The image processing and PCA procedure can eventually be implemented as a stand-alone smartphone application and can be adopted as an extremely low-cost, disposable, fully handheld, easy-to-use, yet sensitive and specific quality control method for appraising red wine or similar beverage products in resource-limited environments.
Monitoring and analysis of low-voltage network with smart grid architecture model by developing use cases

Abstract The objective of this paper is to show the characteristics of smart meters enabling to monitor and analyze the low-voltage (LV) network. This is achieved by developing use cases, where power quality and outage data are transferred from smart meters through distribution network to the control center. To visualize the monitoring process of LV network, the use cases are mapped into smart grid architecture model. The paper proposes a solution to analyze the LV network interruption and power quality problems (over-voltage, under-voltage, voltage sags, and swells). Thus, this paper provides a smart platform for monitoring LV network.
Validation and Safety Approval of a Dual-Mode Head Coil for pTx Applications In Vivo at 7 Tesla

Following regulatory approval of single-transmit MRI at 7 tesla, there is a rapidly growing interest in clinical MRI at this field strength. However, the wider use of diagnostic MRI at 7T will require imaging in parallel-transmit (pTx) mode to reduce B1+ inhomogeneity. Previous work introduced a dual-mode head coil that operates in both transmit modes and this study investigates the use of this coil for the pTx case. It also describes the safety procedure that was followed to ensure safe operation for human scanning using the real-time SAR supervision on a commercial scanner.
modelling framework for analysing the reproductive utput of individual plants grown in monoculture

We propose a framework for modelling plant reproductive data. Several statistical issues can arise in the analysis of reproductive data and this paper develops a framework for dealing with them. The relationship between reproductive output and plant biomass regularly follows a log–log allometric regression. Frequently, a number of plants do not reproduce and the corresponding zero reproductive values do not fall easily within this standard regression framework. Truncated regression allows zero values to be incorporated appropriately in the allometric relationship. We also propose a mixture-model method to deal with outlier values that do not follow the allometric relationship, for example large plants that have zero reproductive output values. Reproductive data from plants grown together in pots or in field plots may not be independent and this dependence should be dealt with in each of the above analyses. We illustrate our method using either generated data or data from an experiment examining reproductive output in Sinapis arvensis and provide the programming tools used.
The effect of numerical dissipation on the predictive accuracy of wall-modelled large-eddy simulation

The effect of numerical dissipation on the predictive accuracy of wall-modelled large-eddy simulation is investigated via systematic simulations of fully-developed turbulent channel flow. A total of 16 simulations are conducted using the open-source computational fluid dynamics software OpenFOAM®. Four densities of the computational mesh are considered, with four simulations performed on each, in turn varying in the amount of numerical dissipation introduced by the numerical scheme used for interpolating the convective fluxes. The results are compared to publicly-available data from direct numerical simulation of the same flow. Computed error profiles of all the considered flow quantities are shown to vary monotonically with the amount of dissipation introduced by the numerical schemes. As expected, increased dissipation leads to damping of high-frequency motions, which is clearly observed in the computed energy spectra. But it also results in increased energy of the large-scale motions, and a significant over-prediction of the turbulent kinetic energy in the inner region of the boundary layer. On the other hand, dissipation benefits the accuracy of the mean velocity profile, which in turn improves the prediction of the wall shear stress given by the wall model. Thus, in the current framework, the optimal choice for the dissipation of the numerical schemes may depend on the primary quantity of interest for the conducted simulation. With respect to the resolution of the grid, the change in the accuracy is much less predictable, and the optimal resolution depends on the considered quantity and the amount of dissipation introduced by the numerical schemes.
Deliverable D5.1 – Technical description of the holistic design flow in CONTINUUM

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Deliverable D5.1 – Technical description of the holistic design flow in CONTINUUM Rabab Bouziane, Erven Rohou, Florent Bruguier, Guillaume Devic, Abdoulaye Gamatié, Guilherme Leobas, Marcelo Novaes, David Novo, Pierre-Yves Péneau, Fernando Magno Quinto Pereira, et al.
Working through interpreters in old age psychiatry – A Systematic Review

Background: The literature on interpreting and interpretation services is limited in old age psychiatry, and few countries have quality standards when such services are utilised. The aim of this paper was to appraise the effect of the use of interpreters in psychogeriatrics. Method: We conducted a systematic search of the literature to meet the following objectives: To assess the impact of a language barrier in psychiatric assessment and management, to assess the effect of the use of interpreters on patient satisfaction and quality of care, to identify good practice and make recommendations for research and practice in the field of old age mental health. Exclusion and Inclusion criteria were applied to the research literature. Qualitative and quantitative papers were included. Final stage studies were assessed in accordance with quality ratings, independently by the authors. Results: Three original research studies met the final criteria stage, and identified obstacles to care pathways exist for BME patients, psychiatric distress through the use of an interpreter is possible and interpreter services are inadequate and unacceptable. Conclusion: Low number of overall papers fitting the review criteria highlights the need for research in this area. None of these papers were UK based, but provide a platform for areas of exploration within old age psychiatry in the UK. Given the factors of globalisation and migration it is inevitable that psychiatric services will urgently need to address translation and interpretation as embedded services provided to set quality standards.
Supplementary material to "Seasonal dispersal of fjord meltwaters as an important source of iron to coastal Antarctic phytoplankton"

Abstract. Glacial meltwater from the western Antarctic Ice Sheet is hypothesized to be an important source of cryospheric iron, fertilizing the Southern Ocean, yet its trace metal composition and factors which control its dispersal remain poorly constrained. Here we characterize meltwater iron sources in a heavily glaciated western Antarctic Peninsula (WAP) fjord. Using dissolved and particulate ratios of manganese-to-iron in meltwaters, porewaters, and seawater, we show that glacial melt and subglacial plumes contribute to the seasonal cycle of bioavailable iron within a fjord still relatively unaffected by climate change-induced glacial retreat. Organic ligands derived from the phytoplankton bloom and the glaciers bind dissolved iron and facilitate the solubilization of particulate iron downstream. Using a numerical model, we show that plumes generated by outflow from the subglacial hydrologic system, enriched in labile particulate trace metals derived from a chemically-modified crustal source, can supply the surface through vertical mixing, and that prolonged katabatic wind events enhance export of meltwater out of the fjord. Thus, we identify an important atmosphere-ice-ocean coupling intimately tied to coastal iron biogeochemistry and primary productivity along the WAP.
On Pearl’s Hierarchy and the Foundations of Causal Inference

Cause-and-effect relationships play a central role in how we perceive and make sense of the world around us, how we act upon it, and ultimately, how we under­ stand ourselves. Almost two decades ago, computer scientist Judea Pearl made a breakthrough in understanding causality by discovering and systematically study­ ing the “Ladder of Causation,” a framework that highlights the distinct roles of seeing, doing, and imagining. In honor of this landmark discovery, we name this the Pearl Causal Hierarchy (PCH). In this chapter, we develop a novel and comprehensive treatment of the PCH through two complementary lenses: one logical-probabilistic and another inferential-graphical. Following Pearl’s own pre­ sentation of the hierarchy, we begin by showing how the PCH organically emerges from a well-specified collection of causal mechanisms (a structural causal model, or SCM). We then turn to the logical lens. Our first result, the Causal Hierarchy Theorem (CHT), demonstrates that the three layers of the hierarchy almost always separate in a measure-theoretic sense. Roughly speaking, the CHT says that data at one layer virtually always underdetermines information at higher layers. As in most practical settings the scientist does not have access to the precise form of the underlying causal mechanisms—only to data generated by them with respect to some of the PCH’s layers—this motivates us to study inferences within the PCH through the graphical lens. Specifically, we explore a set of methods known as causal inference that enable inferences bridging the PCH’s layers given a partial
Disability Insurance and Labor Supply : The Role of Health Insurance ∗

In this paper, I study the relative importance of the different types of benefits offered by a suite of social insurance programs. Particularly, I focus on the two major disability insurance programs in the United States – the Social Security Disability Insurance (SSDI) and the Supplemental Security Income (SSI) program. Beneficiaries of these programs receive both cash and health insurance benefits via Medicare and Medicaid, respectively. Different from prior work which has predominantly focused on the role of cash benefits, I study how the health insurance benefits affect participation in these programs and in the labor market, in addition to and separately from the cash benefits. I estimate a conditional logit choice model of labor supply and program application decision using data from the Health and Retirement Study coupled with administrative F831 records which provide data on applications to the two programs. I find that individuals value the cash and the health insurance benefits differently for SSI and SSDI; further there is heterogeneity across education and gender. While the SSI benefits predict participation decisions for high school dropouts who are drawn to the program primarily by the cash benefits, the SSDI benefits predict participation decisions for high school graduates for whom the health insurance benefits drive participation. The magnitude of these results vary by gender. The relative disincentive effects of the two types of benefits on labor market participation is heterogeneous akin to their relative effects on program participation. Based on these findings, I suggest policies that reduce caseloads and costs while increasing work incentives, without depriving the needy of assistance.
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders

We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under some mild assumptions, the model is uniquely identified by a hybrid method. Our method leverages the advantages of constraint-based methods and independent noise-based methods to handle both confounded and unconfounded situations. The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results. The results, unfortunately, usually determine very few unconfounded direct causal relations, because whenever it is possible to have a confounder, it will indicate it. The second step of our procedure finds the unconfounded causal edges between observed variables among only those adjacent pairs informed by the FCI results. By making use of the so-called Triad condition, the third step is able to find confounders and their causal relations with other variables. Afterward, we apply ICA on a notably smaller set of graphs to identify remaining causal relationships if needed. Extensive experiments on simulated data and real-world data validate the correctness and effectiveness of the proposed method.
The rare X-ray flaring activity of the ultraluminous X-ray source NGC 4559 X7

Ultraluminous X-ray sources are considered amongst the most extremely accreting objects in the local Universe. The recent discoveries of pulsating neutron stars in ULXs strengthened the scenario of highly super-Eddington accretion mechanisms on stellar mass compact objects. In this work, we present the first long-term light curve of the source NGC 4559 X7 using all the available Swift/XRT, XMM-Newton, Chandra and NuSTAR data. Thanks to the high quality 2019 XMM-Newton and NuSTAR observations, we investigated in an unprecedented way the spectral and temporal properties of NGC 4559 X7. The source displayed flux variations of up to an order of magnitude and an unusual flaring activity. We modelled the spectra from NGC 4559 X7 with a combination of two thermal components, testing also the addition of a further high energy cut-off powerlaw. We observed a spectral hardening associated with a luminosity increase during the flares, and a spectral softening in the epochs far from the flares. Narrow absorption and emission lines were also found in the RGS spectra, suggesting the presence of an outflow. Furthermore, we measured hard and (weak) soft lags with magnitudes of a few hundreds of seconds whose origin is possibly be due to the accretion flow. We interpret the source properties in terms of a super-Eddington accretion scenario assuming the compact object is either a light stellar mass black hole or a neutron star.
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning

We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method, MedSelect, consists of a trainable deep learning selector that uses image embeddings obtained from contrastive pretraining for determining which images to label, and a non-parametric selector that uses cosine similarity to classify unseen images. We demonstrate that MedSelect learns an effective selection strategy outperforming baseline selection strategies across seen and unseen medical conditions for chest X-ray interpretation. We also perform an analysis of the selections performed by MedSelect comparing the distribution of latent embeddings and clinical features, and find significant differences compared to the strongest performing baseline. We believe that our method may be broadly applicable across medical imaging settings where labels are expensive to acquire.
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

Most modeling approaches lie in either of the two categories: physics‐based or data‐driven. Recently, a third approach which is a combination of these deterministic and statistical models is emerging for scientific applications. To leverage these developments, our aim in this perspective paper is centered around exploring numerous principle concepts to address the challenges of (i) trustworthiness and generalizability in developing data‐driven models to shed light on understanding the fundamental trade‐offs in their accuracy and efficiency and (ii) seamless integration of interface learning and multifidelity coupling approaches that transfer and represent information between different entities, particularly when different scales are governed by different physics, each operating on a different level of abstraction. Addressing these challenges could enable the revolution of digital twin technologies for scientific and engineering applications.