Data Envelopment Analysis Models with Ratio Data: A revisit

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The performance evaluation of for-profit and not-for-profit organisations is a unique tool to support the continuous improvement process. Data envelopment analysis (DEA) is literally known as an impeccable technique for efficiency measurement. However, the lack of the ability to attend to ratio measures is an ongoing challenge in DEA. The convexity axiom embedded in standard DEA models cannot be fully satisfied where the data set includes ratio measures and the results obtained from such models may not be correct and reliable. There is atypical approach to deal with the problem of ratio measures in DEA, in particular when numerators and denominators of ratio data are available. In this paper, we show that the current solutions may also fail to preserve the principal properties of DEA as well as to instigate some other flaws. We also make modifications to explicitly overcome the flaws and measure the performance of a set of operating units for the input-and output orientations regardless of assumed technology.Finally, a case study in the education sector is presented to illustrate the strengths and limitations of the proposed approach

An extended multiple criteria data envelopment analysis model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Several researchers have adapted the data envelopment analysis (DEA) models to deal with two inter-related problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries

Selecting data envelopment analysis models: A data-driven application to EU countries

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data envelopment analysis (DEA) is a non-parametric data-driven approach for evaluating the efficiency of a set of homogeneous decision-making units (DMUs) with multiple inputs and multiple outputs. The number of performance factors (inputs and outputs) plays a crucial role when applying DEA to real-world applications. In other words, if the number of performance factors is significantly greater than the number of DMUs, it is highly possible to arrive at a large portion of efficient DMUs, which practically may become problematic due to the lack of ample discrimination among DMUs. The current research aims to develop an array of selecting DEA models to narrow down the performance factors based upon a rule of thumb. To this end, we show that the input- and output-oriented selecting DEA models may select different factors and then present the integrated models to identify a set of common factors for both orientations. In addition to efficiency evaluation at the individual level, we study structural efficiency with a single production unit at the industry level. Finally, a case study on the EU countries is presented to give insight into business innovation, social economy and growth with regard to the efficiency of the EU countries and entire EU

An interval efficiency analysis with dual‑role factors

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data envelopment analysis (DEA) is a data-driven and benchmarking tool for evaluating the relative efficiency of production units with multiple outputs and inputs. Conventional DEA models are based on a production system by converting inputs to outputs using input-transformation-output processes. However, in some situations, it is inescapable to think of some assessment factors, referred to as dual-role factors, which can play simultaneously input and output roles in DEA. The observed data are often assumed to be precise although it needs to consider uncertainty as an inherent part of most real-world applications. Dealing with imprecise data is a perpetual challenge in DEA that can be treated by presenting the interval data. This paper develops an imprecise DEA approach with dual-role factors based on revised production possibility sets. The resulting models are a pair of mixed binary linear programming problems that yield the possible relative efficiencies in the form of intervals. In addition, a procedure is presented to assign the optimal designation to a dual-role factor and specify whether the dual-role factor is a nondiscretionary input or output. Given the interval efficiencies, the production units are categorized into the efficient and inefficient sets. Beyond the dichotomized classification, a practical ranking approach is also adopted to achieve incremental discrimination through evaluation analysis. Finally, an application to third-party reverse logistics providers is studied to illustrate the efficacy and applicability of the proposed approach

Canine Models of Inherited Musculoskeletal and Neurodegenerative Diseases

Mouse models of human disease remain the bread and butter of modern biology and therapeutic discovery. Nonetheless, more often than not mouse models do not reproduce the pathophysiology of the human conditions they are designed to mimic. Naturally occurring large animal models have predominantly been found in companion animals or livestock because of their emotional or economic value to modern society and, unlike mice, often recapitulate the human disease state. In particular, numerous models have been discovered in dogs and have a fundamental role in bridging proof of concept studies in mice to human clinical trials. The present article is a review that highlights current canine models of human diseases, including Alzheimer\u27s disease, degenerative myelopathy, neuronal ceroid lipofuscinosis, globoid cell leukodystrophy, Duchenne muscular dystrophy, mucopolysaccharidosis, and fucosidosis. The goal of the review is to discuss canine and human neurodegenerative pathophysiologic similarities, introduce the animal models, and shed light on the ability of canine models to facilitate current and future treatment trials

Real-time MR tracking of AAV gene therapy with betagal-responsive MR probe in a murine model of GM1-gangliosidosis

Transformative results of adeno-associated virus (AAV) gene therapy in patients with spinal muscular atrophy and Leber\u27s congenital amaurosis led to approval of the first two AAV products in the United States to treat these diseases. These extraordinary results led to a dramatic increase in the number and type of AAV gene-therapy programs. However, the field lacks non-invasive means to assess levels and duration of therapeutic protein function in patients. Here, we describe a new magnetic resonance imaging (MRI) technology for real-time reporting of gene-therapy products in the living animal in the form of an MRI probe that is activated in the presence of therapeutic protein expression. For the first time, we show reliable tracking of enzyme expression after a now in-human clinical trial AAV gene therapy (ClinicalTrials.gov: NTC03952637) encoding lysosomal acid beta-galactosidase (betagal) using a self-immolative betagal-responsive MRI probe. MRI enhancement in AAV-treated enzyme-deficient mice (GLB-1(-/-)) correlates with betagal activity in central nervous system and peripheral organs after intracranial or intravenous AAV gene therapy, respectively. With \u3e 1,800 gene therapies in phase I/II clinical trials (ClinicalTrials.gov), development of a non-invasive method to track gene expression over time in patients is crucial to the future of the gene-therapy field

Comparative route of administration studies using therapeutic siRNAs show widespread gene modulation in Dorset sheep

siRNAs comprise a class of drugs that can be programmed to silence any target gene. Chemical engineering efforts resulted in development of divalent siRNAs (di-siRNAs), which support robust and long-term efficacy in rodent and nonhuman primate brains upon direct cerebrospinal fluid (CSF) administration. Oligonucleotide distribution in the CNS is nonuniform, limiting clinical applications. The contribution of CSF infusion placement and dosing regimen on relative accumulation, specifically in the context of large animals, is not well characterized. To our knowledge, we report the first systemic, comparative study investigating the effects of 3 routes of administration - intrastriatal (i.s.), i.c.v., and intrathecal catheter to the cisterna magna (ITC) - and 2 dosing regimens - single and repetitive via an implanted reservoir device - on di-siRNA distribution and accumulation in the CNS of Dorset sheep. CSF injections (i.c.v. and ITC) resulted in similar distribution and accumulation across brain regions. Repeated dosing increased homogeneity, with greater relative deep brain accumulation. Conversely, i.s. administration supported region-specific delivery. These results suggest that dosing regimen, not CSF infusion placement, may equalize siRNA accumulation and efficacy throughout the brain. These findings inform the planning and execution of preclinical and clinical studies using siRNA therapeutics in the CNS

Diabetes Alters Intracellular Calcium Transients in Cardiac Endothelial Cells

Diabetic cardiomyopathy (DCM) is a diabetic complication, which results in myocardial dysfunction independent of other etiological factors. Abnormal intracellular calcium ([Ca2+]i) homeostasis has been implicated in DCM and may precede clinical manifestation. Studies in cardiomyocytes have shown that diabetes results in impaired [Ca2+]i homeostasis due to altered sarcoplasmic reticulum Ca2+ ATPase (SERCA) and sodium-calcium exchanger (NCX) activity. Importantly, altered calcium homeostasis may also be involved in diabetes-associated endothelial dysfunction, including impaired endothelium-dependent relaxation and a diminished capacity to generate nitric oxide (NO), elevated cell adhesion molecules, and decreased angiogenic growth factors. However, the effect of diabetes on Ca2+ regulatory mechanisms in cardiac endothelial cells (CECs) remains unknown. The objective of this study was to determine the effect of diabetes on [Ca2+]i homeostasis in CECs in the rat model (streptozotocin-induced) of DCM. DCM-associated cardiac fibrosis was confirmed using picrosirius red staining of the myocardium. CECs isolated from the myocardium of diabetic and wild-type rats were loaded with Fura-2, and UTP-evoked [Ca2+]i transients were compared under various combinations of SERCA, sarcoplasmic reticulum Ca2+ ATPase (PMCA) and NCX inhibitors. Diabetes resulted in significant alterations in SERCA and NCX activities in CECs during [Ca2+]i sequestration and efflux, respectively, while no difference in PMCA activity between diabetic and wild-type cells was observed. These results improve our understanding of how diabetes affects calcium regulation in CECs, and may contribute to the development of new therapies for DCM treatment