Coupled-cluster calculations for the ground- and excited-states of the spin-half XXZ model

The coupled-cluster method is applied to the spin-1/2 antiferromagnetic XXZ model on a square lattice by employing an approximation which contains two-body long-range correlations and high-order four-body local correlations. Improvement is found for the ground-state energy, sublattice magnetization, and the critical anisotropy when comparing with the approximation including the two-body correlations alone. We also obtain the full excitation spectrum which is in good agreement with the quantum Monte Carlo results and the high-order spin-wave theory.Comment: 20 pages, 6 figure

Excited states of the quasi-one-dimensional hexagonal quantum antiferromagnets

We investigate the excited states of the quasi-one-dimensional quantum antiferromagnets on hexagonal lattices, including the longitudinal modes based on the magnon-density waves. A model Hamiltonian with a uniaxial single-ion anisotropy is first studied by a spin-wave theory based on the one-boson method; the ground state thus obtained is employed for the study of the longitudinal modes. The full energy spectra of both the transverse modes (i.e., magnons) and the longitudinal modes are obtained as functions of the nearest-neighbor coupling and the anisotropy constants. We have found two longitudinal modes due to the non-collinear nature of the triangular antiferromagnetic order, similar to that of the phenomenological field theory approach by Affleck. The excitation energy gaps due to the anisotropy and the energy gaps of the longitudinal modes without anisotropy are then investigated. We then compare our results for the longitudinal energy gaps at the magnetic wavevectors with the experimental results for several antiferromagnetic compounds with both integer and non-integer spin quantum numbers, and we find good agreement after the higher-order contributions are included in our calculations.Comment: 7 pages, 5 figure

Polyethylenimine and its derivates: investigation of in vivo fate, subcellular trafficking and development of novel vector systems

In this dissertation several aspects of polymer based gene delivery were investigated. First, key issues in subcellular processing of electrostatic polymer/nucleic acid complexes were investigated and new insights into mechanisms involved in these processes were gained. Secondly, a targeted gene delivery system was developed for the specific transfection of ovarian carcinoma cells. The resulting vector exhibited a high specificity for target cells combined with low unspecific transfection and toxicity. Furthermore, a novel type of gene delivery system was synthesized. This vector exhibited a high in vitro transfection efficiency and a very low in vitro toxicity as well as favourable in vivo properties, such as reduced toxicities. Another aspect that was studied in depth was the investigation of the stability of several electrostatic vectors in vitro and when applied intravenously

The effects of the COVID-19 pandemic on microbiology-immunology publications: Bibliometric analysis and visualization

Aim: The aim of this study is to visualize the most cited publications, the most frequently used keywords, and the topics studied in the field of "Microbiology -Immunology" before and during the pandemic and to reveal the differences between the two periods. Material and Methods: Studies registered in the Scopus database and published in the field of "Microbiology-Immunology" in 2019 and 2022 were included in the study. Data analysis was performed using Microsoft Excel and VOSviewer program. In the keyword analysis, the most recently published and the top 2000 most cited publications in 2019 and 2022 were evaluated.Results: The most frequently used keywords in the most recent publications in 2019 were "Medicago truncatula", "malaria" and "immunotherapy", while the most cited keywords were "inflammation","microbiome" and "immunotherapy". In 2019, it was determined that most studies were on immunotherapy. In 2022, the top three most frequently used keywords in the most recently published publications were "malaria", "neuroinflammation" and "inflammation", while the most cited publications were "COVID-19", "SARS-CoV-2" and "vaccination". As a result of the keyword analysis, it was determined that the most frequently published topics and the most cited topics were different from each other in the analysis of current studies in 2022.Discussion: Since our study reveals the changes in the literature related to our field, we think that it will be a guide in planning new studies. We believe that periodic repetition of bibliometric analyses and keyword mapping studies will contribute to the quantitative and qualitative development of scientific productivity in our field

Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer

Technological advances in biomarkers and imaging tests are creating new avenues to advance precision health for early detection of cancer. These advances have resulted in multiple layers of information that can be used to make clinical decisions, but how to best use these multiple sources of information is a challenging engineering problem due to the high cost and imperfect sensitivity and specificity of these tests. Questions that need to be addressed include which diagnostic tests to choose and how to best integrate them, in order to optimally balance the competing goals of early disease detection and minimal cost and harm from unnecessary testing. To study these research questions, we present new optimization-based models and data-driven analytic methods in three parts to improve early detection of prostate cancer (PCa). In the first part, we develop and validate predictive models to assess individual PCa risk using known clinical risk factors. Because not all men with newly-diagnosed PCa received imaging at diagnosis, we use an established method to correct for verification bias to evaluate the accuracy of published imaging guidelines. In addition to the published guidelines, we implement advanced classification modeling techniques to develop accurate classification rules identifying which patients should receive imaging. We propose a new algorithm for a classification model that considers information of patients with unverified disease and the high cost of misclassifying a metastatic patient. We summarize our development and implementation of state-wide, evidence-based imaging criteria that weigh the benefits and harms of radiological imaging for detection of metastatic PCa. In the second part of this thesis, we combine optimization and machine learning approaches into a robust optimization framework to design imaging guidelines that can account for imperfect calibration of predictions. We investigate efficient and effective ways to combine multiple medical diagnostic tests where the result of one test may be used to predict the outcome of another. We analyze the properties of the proposed optimization models from the perspectives of multiple stakeholders, and we present the results of fast approximation methods that we show can be used to solve large-scale models. In the third and final part of this thesis, we investigate the optimal design of composite multi-biomarker tests to achieve early detection of prostate cancer. Biomarker tests vary significantly in cost, and cause false positive and false negative results, leading to serious health implications for patients. Since no single biomarker on its own is considered satisfactory, we utilize simulation and statistical methods to develop the optimal diagnosis procedure for early detection of PCa consisting of a sequence of biomarker tests, balancing the benefits of early detection, such as increased survival, with the harms of testing, such as unnecessary prostate biopsies. In this dissertation, we identify new principles and methods to guide the design of early detection protocols for PCa using new diagnostic technologies. We provide important clinical evidence that can be used to improve health outcomes of patients while reducing wasteful application of diagnostic tests to patients for whom they are not effective. Moreover, some of the findings of this dissertation have been implemented directly into clinical practice in the state of Michigan. The models and methodologies we present in this thesis are not limited to PCa, and can be applied to a broad range of chronic diseases for which diagnostic tests are available.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143976/1/smerdan_1.pd

Polyethylenimine and its derivates: investigation of in vivo fate, subcellular trafficking and development of novel vector systems

In this dissertation several aspects of polymer based gene delivery were investigated. First, key issues in subcellular processing of electrostatic polymer/nucleic acid complexes were investigated and new insights into mechanisms involved in these processes were gained. Secondly, a targeted gene delivery system was developed for the specific transfection of ovarian carcinoma cells. The resulting vector exhibited a high specificity for target cells combined with low unspecific transfection and toxicity. Furthermore, a novel type of gene delivery system was synthesized. This vector exhibited a high in vitro transfection efficiency and a very low in vitro toxicity as well as favourable in vivo properties, such as reduced toxicities. Another aspect that was studied in depth was the investigation of the stability of several electrostatic vectors in vitro and when applied intravenously

On the Solutions Fractional Riccati Differential Equation with Modified Riemann-Liouville Derivative

Fractional variational iteration method (FVIM) is performed to give an approximate analytical solution of nonlinear fractional Riccati differential equation. Fractional derivatives are described in the Riemann-Liouville derivative. A new application of fractional variational iteration method (FVIM) was extended to derive analytical solutions in the form of a series for these equations. The behavior of the solutions and the effects of different values of fractional order are indicated graphically. The results obtained by the FVIM reveal that the method is very reliable, convenient, and effective method for nonlinear differential equations with modified Riemann-Liouville derivativ