A microscopic model for d-wave charge carrier pairing and non-Fermi-liquid behavior in a purely repulsive 2D electron system

We investigate a microscopic model for strongly correlated electrons with both on-site and nearest neighbor Coulomb repulsion on a 2D square lattice. This exhibits a state in which electrons undergo a ``somersault'' in their internal spin-space (spin-flux) as they traverse a closed loop in external coordinate space. When this spin-1/2 antiferromagnetic (AFM) insulator is doped, the ground state is a liquid of charged, bosonic meron-vortices, which for topological reasons are created in vortex-antivortex pairs. The magnetic exchange energy of the distorted AFM background leads to a logarithmic vortex-antivortex attraction which overcomes the direct Coulomb repulsion between holes localized on the vortex cores. This leads to the appearance of pre-formed charged pairs. We use the Configuration Interaction (CI) Method to study the quantum translational and rotational motion of various charged magnetic solitons and soliton pairs. The CI method systematically describes fluctuation and quantum tunneling corrections to the Hartree-Fock Approximation (HFA). We find that the lowest energy charged meron-antimeron pairs exhibit d-wave rotational symmetry, consistent with the symmetry of the cuprate superconducting order parameter. For a single hole in the 2D AFM plane, we find a precursor to spin-charge separation in which a conventional charged spin-polaron dissociates into a singly charged meron-antimeron pair. This model provides a unified microscopic basis for (i) non-Fermi-liquid transport properties, (ii) d-wave preformed charged carrier pairs, (iii) mid-infrared optical absorption, (iv) destruction of AFM long range order with doping and other magnetic properties, and (v) certain aspects of angled resolved photo-emission spectroscopy (ARPES).Comment: 14 pages, 17 figure

A numerical study of multi-soliton configurations in a doped antiferromagnetic Mott insulator

We evaluate from first principles the self-consistent Hartree-Fock energies for multi-soliton configurations in a doped, spin-1/2, antiferromagnetic Mott insulator on a two-dimensional square lattice. We find that nearest-neighbor Coulomb repulsion stabilizes a regime of charged meron-antimeron vortex soliton pairs over a region of doping from 0.05 to 0.4 holes per site for intermediate coupling 3 < U/t <8. This stabilization is mediated through the generation of ``spin-flux'' in the mean-field antiferromagnetic (AFM) background. Holes cloaked by a meron-vortex in the spin-flux AFM background are charged bosons. Our static Hartree-Fock calculations provide an upper bound on the energy of a finite density of charged vortices. This upper bound is lower than the energy of the corresponding charged stripe configurations. A finite density of charge carrying vortices is shown to produce a large number of unoccupied electronic levels in the Mott-Hubbard charge transfer gap. These levels lead to significant band tailing and a broad mid-infrared band in the optical absorption spectrum as observed experimentally. At very low doping (below 0.05) the doping charges create extremely tightly bound meron-antimeron pairs or even isolated conventional spin-polarons, whereas for very high doping (above 0.4) the spin background itself becomes unstable to formation of a conventional Fermi liquid and the spin-flux mean-field is energetically unfavorable. Our results point to the predominance of a quantum liquid of charged, bosonic, vortex solitons at intermediate coupling and intermediate doping concentrations.Comment: 12 pages, 25 figures; added references, modified/eliminated some figure

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses

Abstract Background Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided different situations and considerations for knowledge synthesis teams to consider when using artificial intelligence and AML for title and abstract screening. Methods We retrospectively evaluated the implementation and performance of AML across a set of ten historically completed systematic reviews. Based upon the findings from this work and in consideration of the barriers we have encountered and navigated during the past 24 months in using these tools prospectively in our research, we discussed and developed a series of practical recommendations for research teams to consider in seeking to implement AML tools for citation screening into their workflow. Results We developed a seven-step framework and provide guidance for when and how to integrate artificial intelligence and AML into the title and abstract screening process. Steps include: (1) Consulting with Knowledge user/Expert Panel; (2) Developing the search strategy; (3) Preparing your review team; (4) Preparing your database; (5) Building the initial training set; (6) Ongoing screening; and (7) Truncating screening. During Step 6 and/or 7, you may also choose to optimize your team, by shifting some members to other review stages (e.g., full-text screening, data extraction). Conclusion Artificial intelligence and, more specifically, AML are well-developed tools for title and abstract screening and can be integrated into the screening process in several ways. Regardless of the method chosen, transparent reporting of these methods is critical for future studies evaluating artificial intelligence and AML

Interpretation of Complexometric Titration Data: An Intercomparison of Methods for Estimating Models of Trace Metal Complexation by Natural Organic Ligands

With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model their most common type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they were familiar with to model synthetic titration data that are typical of those obtained by applying state-of-the-art electrochemical methods – anodic stripping voltammetry (ASV) and competitive ligand equilibration-adsorptive cathodic stripping voltammetry (CLE-ACSV) – to the analysis of natural waters. Herein, we compare their estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations ([Mf]), and evaluate the influence of the various methods and assumptions used on these results. The ASV-type titrations were designed to test each participant\u27s ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameters. For the four samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate ‘measurements’ of ambient [Mf]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one\u27s parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root–mean–squared relative error between the fitted observations and the simulated curves should be comparable to the expected precision of the analytical method and upon visual inspection the distribution of residuals should not be skewed. Modeling the synthetic, CLE-ACSV-type titration dataset, which comprises 5 titration curves generated at different analytical windows or levels of competing ligand added to the virtual sample, proved to be more challenging due to the random measurement error that was incorporated. Comparison of the submitted results was complicated by the participants\u27 differing interpretations of their task. Most adopted the provided ‘true’ instrumental sensitivity in modeling the CLE-ACSV curves, but several estimated sensitivities using internal calibration, exactly as is required for actual samples. Since most fitted sensitivities were biased low, systematic error in inferred ambient [Mf] and in estimated weak ligand (L2) concentrations resulted. The main distinction between the mathematical approaches taken by participants lies in the functional form of the speciation model equations, with their implicit definition of independent and dependent or manipulated variables. In ‘direct modeling’, the dependent variable is the measured [Mf] (or Ip) and the total metal concentration ([M]T) is considered independent. In other, much more widely used methods of analyzing titration data – classical linearization, best known as van den Berg/Ružić, and isotherm fitting by nonlinear regression, best known as the Langmuir or Gerringa methods – [Mf] is defined as independent and the dependent variable calculated from both [M]T and [Mf]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [Mf] revealed that the best results were obtained by the direct approach. Linear regression of transformed data yielded the largest bias and greatest variability, while non-linear isotherm fitting generated results with mean bias comparable to direct modeling, but also with greater variability. Participants that performed a unified analysis of ACSV titration curves at multiple detection windows for a sample improved their results regardless of the basic mathematical approach taken. Overall, the three most accurate sets of results were obtained using direct modeling of the unified multiwindow dataset, while the single most accurate set of results also included simultaneous calibration. We therefore recommend that where sample volume and time permit, titration experiments for all natural water samples be designed to include two or more detection windows, especially for coastal and estuarine waters. It is vital that more practical experimental designs for multi-window titrations be developed. Finally, while all mathematical approaches proved to be adequate for some datasets, matrix-based equilibrium models proved to be most naturally suited for the most challenging cases encountered in this work, i.e., experiments where the added ligand in ACSV became titrated. The ProMCC program (Omanović et al., this issue) as well as the Excel Add-in based KINETEQL Multiwindow Solver spreadsheet (Hudson, 2014) have this capability and have been made available for public use as a result of this intercomparison exercise

Versatile Materials for use as Chemically Sensitive

Interfaces in SAW-based Sensor Arrays colvF-q 067 3 --A ABSTRACT The primary research objective of the work described here is to design, synthesize, and characterize new materials for use as chemical sensor interfaces, integrate these materials, using appropriate transducers, into sensor arrays, and then develop appropriate mathematical algorithms for interpreting the array response. In this paper, we will discuss two new types of materials we have developed that are ideally suited for use as chemically sensitive interfaces for array-based chemical sensing applications, since they: (1) provide general specificity towards classes of functional groups rather than individual compounds; (2) are intermediate in structure between monolayers and polymers; (3) exhibit both endo-and exo-recognition. The fust class of materials is surface-confined dendrimers and the second is hyperbranched polymers