Gap scaling at Berezinskii-Kosterlitz-Thouless quantum critical points in one-dimensional Hubbard and Heisenberg models

We discuss how to locate critical points in the Berezinskii-Kosterlitz-Thouless (BKT) universality class by means of gap-scaling analyses. While accurately determining such points using gap extrapolation procedures is usually challenging and inaccurate due to the exponentially small value of the gap in the vicinity of the critical point, we show that a generic gap-scaling analysis, including the effects of logarithmic corrections, provides very accurate estimates of BKT transition points in a variety of spin and fermionic models. As a first example, we show how the scaling procedure, combined with density-matrix-renormalization-group simulations, performs extremely well in a non-integrable spin-$3/2$ XXZ model, which is known to exhibit strong finite-size effects. We then analyze the extended Hubbard model, whose BKT transition has been debated, finding results that are consistent with previous studies based on the scaling of the Luttinger-liquid parameter. Finally, we investigate an anisotropic extended Hubbard model, for which we present the first estimates of the BKT transition line based on large-scale density-matrix-renormalization-group simulations. Our work demonstrates how gap-scaling analyses can help to locate accurately and efficiently BKT critical points, without relying on model-dependent scaling assumptions.Comment: 8 pages, 7 figure

Barcoding Genetically Distinct Plasmodium falciparum Strains for Comparative Assessment of Fitness and Antimalarial Drug Resistance

The repeated emergence of antimalarial drug resistance in Plasmodium falciparum, including to the current frontline antimalarial artemisinin, is a perennial problem for malaria control. Next-generation sequencing has greatly accelerated the identification of polymorphisms in resistance-associated genes but has also highlighted the need for more sensitive and accurate laboratory tools to profile current and future antimalarials and to quantify the impact of drug resistance acquisition on parasite fitness. The interplay of fitness and drug response is of fundamental importance in understanding why particular genetic backgrounds are better at driving the evolution of drug resistance in natural populations, but the impact of parasite fitness landscapes on the epidemiology of drug resistance has typically been laborious to accurately quantify in the lab, with assays being limited in accuracy and throughput. Here we present a scalable method to profile fitness and drug response of genetically distinct P. falciparum strains with well-described sensitivities to several antimalarials. We leverage CRISPR/Cas9 genome-editing and barcode sequencing to track unique barcodes integrated into a nonessential gene (pfrh3). We validate this approach in multiplex competitive growth assays of three strains with distinct geographical origins. Furthermore, we demonstrate that this method can be a powerful approach for tracking artemisinin response as it can identify an artemisinin resistant strain within a mix of multiple parasite lines, suggesting an approach for scaling the laborious ring-stage survival assay across libraries of barcoded parasite lines. Overall, we present a novel high-throughput method for multiplexed competitive growth assays to evaluate parasite fitness and drug response

Potential cost-savings due to the application of a center of excellence care model in rheumatoid arthritis in Colombia

To evaluate the economic efficiency of a Center of Excellence (CoE) care model for rheumatic diseases located in Bogotá-Colombia. Biomab CoE is based on an adaptation of Colombian clinical practice guideline for the management of rheumatoid arthritis (RA). Care standards are defined by the severity of the disease (DAS28), involving an interdisciplinary team and differential types and frequencies of health services for each level of severity [remission, low (LDA), moderate (MDA) and severe disease activity (SDA)]. A cost-analysis was conducted to analyze the health economic impact after the application of a CoE model in a cohort of RA patients followed during a year. Mean, minimum, and maximum treatment costs were calculated at different moments in time: baseline, follow-up at month six, and after a year. This was done by multiplying the number of patients at each cut-off by the estimated cost per stage of the disease, according to the recommendations of the Colombian Institute of Health Technology Assessment. Statistical analyses were performed using Microsoft Excel® and R. All estimated costs were expressed in United States dollars, using the average exchange rate from January to December of 2018, reported by Banco de la República de Colombia: US$1=$2,951.3 Colombian pesos(COP). As preliminary results, 968 patients were followed during a year. At the beginning of the follow-up, treating all patients in the CoE with an integral attention would cost COP$1,808,096,027 ($1,440,179,796-$3,601,084,711). Same number of patients treated at month six of follow-up would cost COP$1,377,186,140 ($1,127,818,822-$2,570,342,964), and COP$1,147,370,864 ($949,470,612-$2,090,941,567) after a year of follow-up. Treating these patients in a CoE means potential cost-savings of up to COP$660,725,163 annually. As patients are treated in the CoE for RA, their health outcomes improve from severe disease activity status to low disease activity and remission, saving costs to the Colombian health system

The Rap Net: a geodetic positioning network for Andalusia (South Spain)

In this paper we present a description of the Andalusian Positioning Network, RAP: its objectives; design; development; its problems and its present status. This geodetic network consists of 22 permanent GPS stations whose surveying will provide the data required to obtain relative positions of any place in Andalusia after applying some post-processing techniques and real-time differential corrections. Thus, high-precision geodetic coordinates referred to the WGS-84 system will be provided for anywhere in Andalusia. The station in the network can broadcast a differential correction via internet. Nine stations can also broadcast the RTK corrections via radio. The network has been designed to cover Andalusia and to provide real-time differential corrections in the whole area. The RAP network is referred to the EUREF system and is the new reference frame in Andalusia for the Institute of Cartography of Andalusia’s new cartography. It will also be used to settle photogrametric points or marks for the georeferencing of satellite images; to establish control points for reference networks in civil engineering or GIS applications; for numerous scientific and technological applications, such as precise geoid determination and tropospheric, ionospheric, and climatological studies, among others. Its millimetric precision will ensure success when integrating new projects with other cartographies

How expensive is treating patients in a center of excellence for rheumatoid arthritis in Colombia?

We aimed to estimate the cost of treating patients with rheumatoid arthritis (RA) in a Center of Excellence (CoE) for rheumatic diseases located in Bogotá, Colombia. We performed a cost analysis from the standard cost estimation of a CoE program for RA care. We estimated costs of consultations, laboratory and imaging tests, and pharmacological treatment from the measurement of the health care resource utilization of the CoE standard protocol according to the activity level of the disease (DAS28). Costing process was done following the recommendation of the Colombian Institute of Health Technology Assessment (IETS, in Spanish). Mean, minimum and maximum costs were reported annually for a type case depending on severity and classified as Remission, low disease activity (LDA), moderate disease activity (MDA) and severe disease activity -SDA- (with and without bDMARD). All costs were reported in American dollars, using the average exchange rate from January to December of 2018, reported by Banco de la República de Colombia: US$1 =$2,951.3 Colombian pesos. Mean total direct medical cost to treat a patient in remission is US$235.7 (213.0-336.4), in LDA is US325.2 (288.0-464.6) and in MDA is US$835.5 ($573.1-$2,187.1). There is a considerable increase in direct medical costs from a patient in SDA and SDA+Biologics: US$2,555.5 ($2,301.1-$3,890.9) to US$8,032.4 ($7,564.4-$8,400.3). The largest share of the cost was related to drugs, representing 39.9% for Remission, 53.6% for LDA, 75.2 for MDA, and in SDA and SDA+Biologics the proportion of what is spent on drugs for RA treatment is 88.5% and 96.7%, respectively. As the severity of the disease increases, the expenditure rate on drugs rises over the total of each activity level. With the introduction of the biological therapy, the treatment of RA is expensive, however, the CoE is an efficient way of care for RA

Predicting Many Properties of a Quantum System from Very Few Measurements

Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical shadow’, can be used to predict many different properties; order log(M) measurements suffice to accurately predict M different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables and the energy variance of many-body local Hamiltonians. The numerical results highlight the advantages of classical shadows relative to previously known methods

Visualizing Strange Metallic Correlations in the 2D Fermi-Hubbard Model with AI

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.Comment: 12 pages, 9 figures; updated in accord with the published versio

Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- A nd short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems