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This paper proposes that data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics in performance evaluation and benchmarking. While computational algorithms have been developed to deal with large volumes of data (decision making units, inputs, and outputs) under the conventional DEA, valuable information hidden in big data that are represented by network structures should be extracted by DEA. These network structures, e.g., transportation and logistics systems, encompass a broader range of inter-linked metrics that cannot be modelled by conventional DEA. It is proposed that network DEA is related to the value dimension of big data. It is shown that network DEA is different from standard DEA, although it bears the name of DEA and some similarity with conventional DEA. Network DEA is big data enabled analytics (big DEA) when multiple (performance) metrics or attributes are linked through network structures. These network structures are too large or complex to be dealt with by conventional DEA. Unlike conventional DEA that are solved via linear programming, general network DEA corresponds to nonconvex optimization problems. This represents opportunities for developing techniques for solving non-linear network DEA models. Areas such as transportation and logistics system as well as supply chains have a great potential to use network DEA in big data modeling. [ABSTRACT FROM AUTHOR]

Performing a high-quality manufacturing audit can be time consuming and costly given the large number and scale of manufacturing firms and enterprises. Chinese economy has experienced some rapid and significant growth over the past 30 years, which is largely due to contributions from the development of manufacturing industry. Auditing tools are very much needed in auditing the Chinese manufacturing industry. Data envelopment analysis (DEA) has been used as an auditing tool in selecting audit objects that are treated as decision making units (DMUs). These DMUs are characterized by a set of performance measures. DEA then uses data on these performance measures to identify potential audit objects. However, conventional DEA treat each DMU or system as a “black-box” and does not take the operations of individual components within the “black-box” into consideration. For example, a large number of firms or enterprises exist in a manufacturing industry. When the conventional DEA is applied to the industries, the performance of the firms is often ignored. This paper proposes a new parallel DEA model where each input/output of the system is not the sum of those of all its components. Such a situation arises from the need of auditing firms or enterprises in Chinese manufacturing industries. For example, the cost margin of a particular industry may not equal to the sum of that of all its component firms within the industry because this metric is measured in percentage. The proposed approach is applied to the manufacturing industry of China. [ABSTRACT FROM AUTHOR]

Abstract: RET gene is crucial for the development of enteric nervous system, and dys-regulation of RET expression causes Hirschsprung disease. HOXB5 regulates RET transcription, and perturbations in transcriptional regulation by HOXB5 caused reduced RET expression and defective enteric nervous system development in mice. The mechanisms by which HOXB5 regulate RET transcription are unclear. Thus, unraveling the regulatory mechanisms of HOXB5 on RET transcription could lead to a better understanding of the etiology of Hirschsprung disease. In this study, we identified and confirmed HOXB5 binding to the multi-species conserved sequence (MCS+9.7) in the first intron of the RET gene. We developed a RET mini-gene reporter system, and showed that MCS+9.7 enhanced HOXB5 trans-activation from RET promoter in human neuroblastoma SK-N-SH cells and in chick embryos. The deletion of HOXB5 binding site interfered with HOXB5 trans-activation. Furthermore, transfection of HOXB5 induced endogenous RET transcription, enhanced the co-precipitation of TATA-box binding protein with the transcription start site of RET, and induced histone H3K4 trimethylation in chromatin regions upstream and downstream of RET transcription start site. In conclusion, (i) HOXB5 physically interacted with MCS+9.7 and enhanced RET transcription, (ii) HOXB5 altered chromatin conformation and histone modification of RET locus, which could facilitate the formation of transcription complex, and enhance RET transcription, (iii) expression of RET was mediated by a complex regulatory network of transcription factors functioning in a synergistic, additive and/or independent manners. Hence, dys-regulation of RET expression by HOXB5 could result in insufficient RET expression and Hirschsprung disease. [Copyright &y& Elsevier]

Selecting the most promising candidates to fill an open position can be a difficult task when there are many applicants. Each applicant achieves certain performance levels in various categories and the resulting information can be overwhelming. We demonstrate how data envelopment analysis (DEA) can be used as a fair screening and sorting tool to support the candidate selection and decision-making process. Each applicant is viewed as an entity with multiple achievements. Without any a priori preference or information on the multiple achievements, DEA identifies the non-dominated solutions, which, in our case, represent the “best” candidates. A DEA-aided recruiting process was developed that (1) determines the performance levels of the “best” candidates relative to other applicants; (2) evaluates the degree of excellence of “best” candidates’ performance; (3) forms consistent tradeoff information on multiple recruiting criteria among search committee members, and, then, (4) clusters the applicants. [Copyright &y& Elsevier]

In most practical applications of Data Envelopment Analysis (DEA), it is recognized that there is a gap between the theoretical maximum reduction in inputs. e.g., 1 - 0, versus what is actually achievable. In this paper we examine this phenomenon in the context of productivity measurement of highway maintenance crews. Here, maintenance supervisors and geotechnical engineers estimate the maximum achievable reduction in resources without impacting the outputs from the process. Furthermore, they put bounds on the extent of output erosion that can result from input reduction beyond this maximum level. We present a modified version of the standard Banker, Charnes and Cooper (BCC) DEA model that explicitly addresses this erosion phenomenon. The resulting projections provide maintenance managers with a measure of the impact on system performance under excessive resource reduction, and aid in setting guidelines for maintenance budgeting at the patrol level. [ABSTRACT FROM AUTHOR]

Sufficient numbers of decision-making units (DMUs) in comparison to the number performance measures that are classified as inputs and outputs has been a concern when applying data envelopment analysis (DEA) in real world settings. As the number of DMUs decreases (or the numbers of inputs and outputs increases) the discrimination power and accuracy of DEA with respect to the performance of DMUs decreases. The current study proposes a technique which increases the discrimination power of DEA and allows for increasing the numbers of inputs and outputs even if the number of DMUs is relatively small. An adjusted DEA methodology is proposed which is stronger and more accurate than the routine DEA methodology, and permits one to identify the reference units for each best-practice unit (more than the DMU itself) and to distinguish the performance and rank of DMUs relative to one another, thereby enhancing the discrimination power of DEA. The scores from the adjusted methodology are logically meaningful. The pros of the new approach are demonstrated by several examples and simulation experiments in controlled environments. The results show that the proposed technique significantly improves the discrimination power over that of the conventional DEA model when the number of DMUs decreases or the number of inputs and outputs increases. In addition, the proposed technique is used to evaluate the quality-of-life related to twelve input–output combinations for two sets of DMUs, namely fifteen USA cities and five international cities, where none of the rules of thumb are satisfied. [ABSTRACT FROM AUTHOR]

Health care spending usually contributes to a large part of a developed country's economy. In 2011, the U.S. consumed about 17.7% of its GDP on health care. As one of the most significant components of the health care industry, the hospital sector plays a key role to provide healthcare services. Healthcare services industry can be affected by many factors, of which economic downturn is a crucial one. As a result, it is worth investigating the condition and state of hospital management when economic downturn occurs. This paper aims to analyze how the Great Recession affects hospital performance in Pennsylvania during the period 2005–2012 by using data envelopment analysis (DEA). Specifically, we measure efficiency for hospitals in Pennsylvania, and use several DEA models to calculate the global Malmquist index (GMI). We find that: (1) 15.4% hospitals are always efficient while 36.9% hospitals are always inefficient for all years in 2005–2012; (2) The relative distance for a group of hospitals to the frontier is almost unchanged post-recession and pre-recession; (3) The average efficiency/GMI decreases by 2.43%/3.07% from pre-recession to post-recession. The analysis indicates that hospital performance slightly decreased due to the economic downturn in Pennsylvania. [ABSTRACT FROM AUTHOR]

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil resources into electric energy. In this paper, data envelopment analysis (DEA) is used to assess ecological efficiency where air emissions are used as undesirable outputs. Although conventional DEA does not require a priori information on the input and output weights, weight restrictions can be incorporated to reflect a user's preference over the performance metrics, or to refine the DEA results. Adding weight restrictions voids the fact that DEA scores are independent of the units of measurement. To incorporate weight constraints in ecological efficiency assessment, this paper develops a DEA model that is units-invariant when weight restrictions are imposed. Moreover, the proposed model is equivalent to the standard units-invariant DEA model when weight restrictions are not present. [ABSTRACT FROM AUTHOR]

In 2006, the Massachusetts legislature passed a landmark health care reform bill (the Reform) that has served as a model for the national health care reform. By aiming to provide 'access to affordable, quality, accountable health care,' the goals of this reform were to reduce the number of uninsured Massachusetts residents while containing the growth of health care costs and improving the quality of health care services. The current paper examines the impact of the Reform on the quality of care in addition to hospital costs simultaneously from a perspective of efficiency analysis. We develop an integer-valued non-radial Russell data envelopment analysis (DEA) model, which is unit variant and calculate the hospitals' efficiency directly. However, the proposed integer-valued model is non-linear. The current paper thus transforms this model into a parametric integer linear programming. We develop a method to derive its optimal solutions. We then use the new DEA model to calculate and compare the efficiency scores of hospitals in Massachusetts and Connecticut pre- and post-Reform. The analysis shows that the Reform has achieved its cost containment and quality improvement goals at the same time. These analyses provide potentially useful information to hospital regulators and government regulators, especially in light of the national interest on health care legislation. [ABSTRACT FROM AUTHOR]

Patterning of neural crest (NC) for the formation of specific structures along the anterio-posterior (A-P) body axis is governed by a combinatorial action of Hox genes, which are expressed in the neuroepithelium at the time of NC induction. Hoxb5 was expressed in NC at both induction and migratory stages, and our previous data suggested that Hoxb5 played a role in the NC development. However, the underlying mechanisms by which Hoxb5 regulates the early NC development are largely unknown. Current study showed that both the human and mouse Foxd3 promoters were bound and trans-activated by Hoxb5 in NC-derived neuroblastoma cells. The binding of Hoxb5 to Foxd3 promoter in vivo was further confirmed in the brain and neural tube of mouse embryos. Moreover, Wnt1-Cre mediated perturbation of Hoxb5 signaling at the dorsal neural tube in mouse embryos resulted in Foxd3 down-regulation. In ovo , Foxd3 alleviated the apoptosis of neural cells induced by perturbed Hoxb5 signaling, and Hoxb5 induced ectopic Foxd3 expression in the chick neural tube. This study demonstrated that Hoxb5 (an A-P patterning gene) regulated the NC development by directly inducing Foxd3 (a NC specifier and survival gene). [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a broadly used non-parametric technique for performance evaluation and data analytics. While conventional single-stage DEA models overlook the internal interactions of decision making units (DMUs), network DEA opens this black box to investigate the internal structure of DMUs. Practically, many network DEA models involve shared performance measures that are not easily divisible among individual components of a network. Based upon a two-stage network DEA model, the current study treats such performance measures as inseparable links, implying that no proportions are optimized and allocated to the two stages of the network. The shared and unsplittable links in the proposed two-stage DEA model manifest integrality while both ends of the link are maximized or minimized simultaneously, and this setting has not been modeled in any existing DEA studies. The shared and unsplittable links in our model can be considered intermediate measures, but they are different from the two existing types of dual-role intermediate measures, which are traditional intermediate measures and feedback measures. Our performance link is a new type of intermediate measure that is minimized or maximized in both stages of the network. The resulting network DEA model is highly non-linear. To address the non-linearity, a parametric linear model is adopted. The proposed approach is construed in four variants, and then illustrated using a set of 100 banks in the United States. [ABSTRACT FROM AUTHOR]

Abstract: Polycythemia vera (PV) is a myeloproliferative disorder involving hematopoietic stem cells. A recurrent somatic missense mutation in JAK2 (JAK2V617F) is thought to play a causal role in PV. Therefore, targeting Jak2 will likely provide a molecular mechanism-based therapy for PV. To facilitate the development of such new and specific therapeutics, a suitable and well-characterized preclinical animal model is essential. Although several mouse models of PV have been reported, the spatiotemporal kinetics of PV formation and progression has not been studied. To address this, we created a bone marrow transplant mouse model that co-expresses mutant Jak2 and luciferase 2 (Luc2) genes. Bioluminescent imaging (BLI) was used to visualize disease cells and analyze the kinetics of PV development in vivo. To better understand the molecular mechanism of PV, we generated mice carrying a kinase inactive mutant Jak2 (Jak2K882E), demonstrating that the PV disease was dependent on constitutive activation of the Jak2 kinase activity. We further showed that the Jak2V617F mutation caused increased stem cell renewal activity and impaired cell differentiation, which was at least in part due to deregulated transcriptional programming. The Jak2V617F–Luc2 PV mice will be a useful preclinical model to characterize novel JAK2 inhibitors for the treatment of PV. [Copyright &y& Elsevier]

This paper presents a methodology for dealing with performance evaluation settings where factors can simultaneously play both input and output roles. Model structures are developed for classifying Decision-Making Units (DMUs) into three groups according to whether such a factor is behaving like an output, an input, or is in equilibrium, neither wanting to lose or gain any of the factors. We connect these ideas to those involving increasing, decreasing and constant returns to scale. Examples of factors that play this dual-role are: trainees in organizations, such as nurses, medical students, and doctoral students; awards to scholars or university departments; certain revenue—generating transactions in banks, and so on. We apply the model to the analysis of a set of university departments. In some settings, a dual-role factor may be one that can be reallocated, such as would be the case when DMUs are managed by a central authority. We develop the appropriate model structures to permit such a reallocation. We present two such structures, with the first involving reallocation from an existing allocation, and the second, a zero-base allocation. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases where percentage data are present and where projected percentages must not exceed the requisite 100 % limit. Other examples include Likert scale data. Using existing integer DEA approaches as a backdrop, the current paper presents models for dealing with bounded and discrete data. Our proposed models address the issue of constraining DEA projections to fall within imposed bounds. It is shown that Likert scale data can be modeled using the proposed approach. The proposed DEA models are used to evaluate the energy efficiency of 29 provinces in China. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a methodology used to measure the relative efficiencies of peer decision-making units (DMUs). In the original model, it is assumed that in a multiple input, multiple output setting, all members of the input bundle affect the entire output bundle. There are many situations, however, where this assumption does not hold. In a manufacturing setting, for example, packaging resources (inputs) only influence the production of those products that require packaging. This is referred as partial input-to-output interactions where the DEA model is based on the view of a DMU as a business unit consisting of a set of independent subunits, such that efficiency of the DMU can be defined as a weighted average of the efficiencies of those subunits. The current paper presents an extension to that methodology to allow for efficiency measurement in situations where there exist multiple procedures or processes for generating given output bundles. The proposed model is then applied to the problem of evaluating the efficiencies of a set of steel fabrication plants. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a chronic autoimmune disease resulting in joint inflammation, pain, and eventual bone loss. Bone loss and remodeling caused by symmetric polyarthritis, the hallmark of RA, is readily detectable by bone mineral density (BMD) measurement using micro-CT. Abnormalities in these measurements over time reflect the underlying pathophysiology of the bone. To evaluate the efficacy of anti-rheumatic agents in animal models of arthritis, we developed a high throughput knee and ankle joint imaging assay to measure BMD as a translational biomarker. A bone sample holder was custom designed for micro-CT scanning, which significantly increased assay throughput. Batch processing 3-dimensional image reconstruction, followed by automated image cropping, significantly reduced image processing time. In addition, we developed a novel, automated image analysis method to measure BMD and bone volume of knee and ankle joints. These improvements significantly increased the throughput of ex vivo bone sample analysis, reducing data turnaround from 5 days to 24 hours for a study with 200 rat hind limbs. Taken together, our data demonstrate that BMD, as quantified by micro-CT, is a robust efficacy biomarker with a high degree of sensitivity. Our innovative approach toward evaluation of BMD using optimized image acquisition and novel image processing techniques in preclinical models of RA enables high throughput assessment of anti-rheumatic agents offering a powerful tool for drug discovery. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for the analysis of electromagnetic problems, namely radial basis function (RBF) mixed with Galerkin finite element method (FEM). The new method divides computational domain into a series of sub-domains and uses the point interpolation based on RBF to obtain the shape functions, respectively. Then, each separate domain is taken as elements of the Galerkin FEM to approximate the solutions of the entire computational area. Using this method, the coefficient matrix becomes sparse; and strict meshing is not necessary. The hybrid method also combines the advantages of RBF and FEM, such as easy to handle complex boundary conditions for FEM and high efficient fitting for RBF. Two electromagnetic problems are computed to verify the method. Meanwhile, two traditional methods are also investigated to prove the advantages of the proposed method as a comparison. [ABSTRACT FROM PUBLISHER]

The health care sector is one of the fastest growing sectors in the United States. Researchers are interested in conducting studies in the area of health economics in order to propose solutions to curb the rapid increase in health care spending and to improve the efficiency of the health care system in the United States. Specifically, hospital efficiency is one important research area in health economics. In this paper, data envelopment analysis (DEA) is used to assess hospital efficiency. An additive super-efficiency model is presented and applied to a sample of general acute care hospitals in Pennsylvania. In addition to the conventional choice of input and output variables, we include the survival rate as a quality measure of health outcome in the set of output variables. Thus our model takes both the quantity and the quality of the output into account. With the results obtained from our proposed DEA model, inefficiencies can be identified for hospitals to address without sacrificing the quality of care. [ABSTRACT FROM AUTHOR]

Abstract: γ-Secretase modulators (GSMs) are potentially disease-modifying treatments for Alzheimer’s disease. They selectively lower pathogenic Aβ42 levels by shifting the enzyme cleavage sites without inhibiting γ-secretase activity, possibly avoiding known adverse effects observed with complete inhibition of the enzyme complex. A cell-based HTS effort identified the sulfonamide 1 as a GSM lead. Lead optimization studies identified compound 25 with improved cell potency, PKDM properties, and it lowered Aβ42 levels in the cerebrospinal fluid (CSF) of Sprague-Dawley rats following oral administration. Further optimization of 25 to improve cellular potency is described. [Copyright &y& Elsevier]

Conventional applications of data envelopment analysis generally treat the Decision-Making Unit (DMU) as a black box in that the internal processes are not examined in detail. In some situations, such as the measurement of performance of a set of supply chains, the DMU can be viewed as exhibiting a network structure. A significant body of recent literature has examined a particular form of network structure, namely, where the DMU is a two-stage serial process in which the outputs from the first stage are intermediate variables that serve as inputs to the second stage. The current article extends this idea to include those situations where outputs from the second stage can be fed back as inputs to the first stage. Such feedback variables thus serve a dual role. Models are developed for examining performance in this feedback setting and are illustrated using an application involving the measurement of performance of a set of Chinese universities. [ABSTRACT FROM AUTHOR]

Simple Summary: This review explores the current trials using cellular immunotherapies in pediatric sarcoma and describes examples of promising new CAR T targets in sarcoma that are in preclinical development. We provide insights into the ways in which the immunosuppressive tumor immune microenvironment can impact on CAR T cell therapy, highlighting specific mechanisms by which the tumor microenvironment may limit CAR T efficacy. Appreciation of these mechanisms may lead to rational combinations of immunotherapies, for example, the combination of CAR T cells with checkpoint inhibitor drugs. We also describe innovations in CAR T cell generation and combination therapies that may pave the way to better clinical outcomes for these patients. Sarcomas are a diverse group of bone and soft tissue tumors that account for over 10% of childhood cancers. Outcomes are particularly poor for children with refractory, relapsed, or metastatic disease. Chimeric antigen receptor T (CAR T) cells are an exciting form of adoptive cell therapy that potentially offers new hope for these children. In early trials, promising outcomes have been achieved in some pediatric patients with sarcoma. However, many children do not derive benefit despite significant expression of the targeted tumor antigen. The success of CAR T cell therapy in sarcomas and other solid tumors is limited by the immunosuppressive tumor microenvironment (TME). In this review, we provide an update of the CAR T cell therapies that are currently being tested in pediatric sarcoma clinical trials, including those targeting tumors that express HER2, NY-ESO, GD2, EGFR, GPC3, B7-H3, and MAGE-A4. We also outline promising new CAR T cells that are in pre-clinical development. Finally, we discuss strategies that are being used to overcome tumor-mediated immunosuppression in solid tumors; these strategies have the potential to improve clinical outcomes of CAR T cell therapy for children with sarcoma. [ABSTRACT FROM AUTHOR]

Abstract: The bradykinin 1 (B1) receptor is upregulated during times of inflammation and is important for maintaining inflamed and chronic pain states. Blocking this receptor has been shown to reverse and/or ameliorate pain and inflammation in animal models. In this report, we describe a new class of B1 receptor antagonists that contain the piperidine acetic acid tetralin core. A structure–activity relationship for these analogs is described in this paper. The most potent compounds from this class have IC50s<20nM in a B1 receptor functional assay. One of these compounds, 13g, shows modest oral bioavailability in rats. [Copyright &y& Elsevier]

Simple Summary: Chimeric antigen receptors (CAR) are engineered molecules expressed on the cell surface that can recognise specific proteins and deliver an activation signal to the cells. Human T lymphocytes equipped with CAR, also called CAR-T cells, can target and kill tumour cells. This technology has been successfully used in treating some of the blood cancers in the last decade. Although the majority of research interest in CAR technology has been focused on CAR-T cells to date, the CAR design has also been used in other types of immune cells to fight against cancers. In this review, we discuss recent advances in CAR design beyond that used in conventional CAR-T cells and their novel indications to develop more potent CAR-based therapy for cancers. Chimeric antigen receptors (CAR) are genetically engineered receptors that can recognise specific antigens and subsequently activate downstream signalling. Human T cells engineered to express a CAR, also known as CAR-T cells, can target a specific tumour antigen on the cell surface to mediate a cytotoxic response against the tumour. CAR-T cell therapy has achieved remarkable success in treating hematologic malignancies, but not in solid tumours. Currently, extensive research is being carried out to make CAR-T cells a therapy for solid tumours. To date, most of the research interest in the field has focused on cytotoxic T lymphocytes as the carrier of CAR products. However, in addition to T cells, the CAR design can be introduced in other immune cells, such as natural killer (NK)/NKT cells, γδ T cells, mucosal-associated invariant T (MAIT) cells, dendritic cells (DC), macrophages, regulatory T cells (Treg), B cells, etc. Some of the CAR-engineered immune cells, such as CAR- γδ T and CAR-NK/NK-T cells, are directly involved in the anti-tumour response, demonstrated in preclinical studies and/or clinical trials. CAR-Tregs showed promising therapeutic potential in treating autoimmune diseases. In particular, B cells engineered with chimeric receptors can be used as a platform for long-term delivery of therapeutic proteins, such as recombinant antibodies or protein replacement, in an antigen-specific manner. CAR technology is one of the most powerful engineering platforms in immunotherapy, especially for the treatment of cancers. In this review, we will discuss the recent application of the CAR design in non-CAR-T cells and future opportunities in immunotherapy. [ABSTRACT FROM AUTHOR]