Innovative multidimensional models in a high-throughput-format for different cell types of endocrine origin

The adrenal gland provides an important function by integrating neuronal, immune, vascular, metabolic and endocrine signals under a common organ capsule. It is the central organ of the stress response system and has been implicated in numerous stress-related disorders. While for other diseases, regeneration of healthy organ tissue has been aimed at such approaches are lacking for endocrine diseases - with the exception of type-I-diabetes. Moreover, tumor formation is very common, however, appropriate high-throughput applications reflecting the high heterogeneity and furthermore relevant 3D-structures in vitro are still widely lacking. Recently, we have initiated the development of standardized multidimensional models of a variety of endocrine cell/tissue sources in a new multiwell-format. Firstly, we confirmed common applicability for pancreatic pseudo-islets. Next, we translated applicability for spheroid establishment to adrenocortical cell lines as well as patient material to establish spheroids from malignant, but also benign adrenal tumors. We aimed furthermore at the development of bovine derived adrenal organoids and were able to establish steroidogenic active organoids containing both, cells of cortical and medullary origin. Overall, we hope to open new avenues for basic research, endocrine cancer and adrenal tissue-replacement therapies as we demonstrate potential for innovative mechanistic insights and personalized medicine in endocrine (tumor)-biology.

Clinicopathological Features Combined With Immune Infiltration Could Well Distinguish Outcomes in Stage II and Stage III Colorectal Cancer: A Retrospective Study

BackgroundThe Immunoscore predicts prognosis in patients with colorectal cancer (CRC). However, a few studies have incorporated the Immunoscore into the construction of comprehensive prognostic models in CRC, especially stage II CRC. We aimed to construct and validate multidimensional models integrating clinicopathological characteristics and the Immunoscore to predict the prognosis of patients with stage II–III CRC.MethodsPatients (n = 254) diagnosed with stage II–III CRC from 2009 to 2016 were used to generate Cox models for predicting disease-free survival (DFS) and overall survival (OS). The variables included basic clinical indicators, blood inflammatory markers, preoperative tumor biomarkers, mismatch repair status, and the Immunoscore (CD3+ and CD8+ T-cell densities). Univariate and multivariate Cox proportional regressions were used to construct the prognostic models for DFS and OS. We validated the predictive accuracy and ability of the prognostic models in our cohort of 254 patients.ResultsWe constructed two predictive prognostic models with C-index values of 0.6941 for DFS and 0.7138 for OS in patients with stage II–III CRC. The Immunoscore was the most informative predictor of DFS (11.92%), followed by pN stage, carcinoembryonic antigen (CEA), and vascular infiltration. For OS, the Immunoscore was the most informative predictor (8.59%), followed by pN stage, age, CA125, and CEA. Based on the prognostic models, nomograms were developed to predict the 3- and 5-year DFS and OS rates. Patients were divided into three risk groups (low, intermediate, and high) according to the risk scores obtained from the nomogram, and significant differences were observed in the recurrence and survival of the different risk groups (p < 0.0001). Calibration curve and time-dependent receiver operating characteristic (ROC) analysis showed good accuracy of our models. Furthermore, the decision curve analysis indicated that our nomograms had better net benefit than pathological TNM (pTNM) stage within a wide threshold probability. Especially, we developed a website based on our prognostic models to predict the risks of recurrence and death of patients with stage II–III CRC.ConclusionsMultidimensional models including the clinicopathological characteristics and the Immunoscore were constructed and validated, with good accuracy and convenience, to evaluate the risks of recurrence and death of stage II–III CRC patients.

Measuring leadership an assessment of the Multifactor Leadership Questionnaire

Although the most used measure of transformational leadership, the Multifactor Leadership Questionnaire (MLQ), has been the subject of intense scrutiny among leadership scholars, little interest has been shown in analyzing the relationship between its underlying constructs and / or their measures. The present study identifies a formative factor structure for most MLQ first-order factors, replacing the usual reflective model. We demonstrate the value of this structure using data from two different samples. First, we applied the MLQ to a sample of 129 police officers from the Catalan Police workforce. Second, we ran an online survey with 300 US citizens. We argue that three second-order factors (transformational, transactional, and laissez faire) should be used as emergent aggregate multidimensional models to describe three different leadership styles, challenging the ubiquitous multidimensional latent models favored in the extant literature. We then propose that transformational/charismatic leadership should be treated as a multidimensional emergent profile model, replacing the leadership development order of precedence, which is dominant in modern leadership research.

Forecasting Efficiency of Acid Impact on the Basis of Construction of Mathematical Models Taking into Account the Technology and Used Composition

Acid treatment is one of the most studied methods of stimulation of inflow in carbonate reservoirs. Nevertheless, enterprises are faced with the problem of reducing the planned production rates of wells after such events, which is due to geological and technological reasons. Taking them into account when planning impact and predicting effectiveness is possible by analyzing actual production materials using mathematical statistics methods. In this paper, using the example of a oil field in the southern part of Perm region, the main factors that influence the efficiency of acid treatments are identified, a number of multidimensional models are obtained that take into account their mutual influence and allow determining the result of a geological and technical measure.

Evaluating the Instantiation of Narcissism Components in Contemporary Measures of Psychopathy

Narcissistic traits figure prominently in classical conceptualizations of psychopathy and bear substantial empirical overlap with components of psychopathy. Yet the degree to which various widely used self-report measures of psychopathy include relevant narcissistic content has not been evaluated, especially in relation to new, multidimensional models of narcissism. Using a large undergraduate sample ( N = 432) and self- and informant-ratings of narcissism, the present study examined narcissism’s instantiation in several commonly used self-report psychopathy measures. A modern conceptualization of narcissism at three levels of hierarchical organization (i.e., Trifurcated Model of Narcissism) was applied to comprehensively evaluate narcissism’s instantiation in these psychopathy measures. Although narcissistic traits related to self-centered antagonism were well-instantiated across psychopathy self-report measures, narcissistic traits related to agentic extraversion were represented more modestly by most (i.e., Triarchic Psychopathy Measure, Self-Report Psychopathy Scale: Version III, Levenson Self-Report Psychopathy Scale), but not all (i.e., Elemental Psychopathy Assessment [EPA]) psychopathy measures. Only the EPA contained substantial representation of all narcissism components. Given the theoretical and empirical overlap between psychopathy and narcissism, it behooves researchers to be aware of the degree to which psychopathy measures capture narcissistic content.

Use of Multidimensional Models to Investigate Boundary Shear Stress through Meandering River Channels

Three-dimensional hydraulics were simulated through a wide range of synthetically generated meandering river channels to determine how channel curvature and width would correlate with the maximum boundary shear stress. Multidimensional models were applied, similar to a computational flume to simulate a wide range of 72 meandering channels, developed from sine-generated curves. Cannel sinuosity ranged from 1.1 to 3.0 and included five consecutive meander bends. Longitudinal slopes of the various channels spanned four orders of magnitude, while bankfull discharges spanned three orders of magnitude. Using results from one-half of the simulation sets, an empirical correlation was found to predict the maximum boundary shear stress as a function of dimensionless ratios of channel curvature and width. The remaining simulation sets were used for verification. Multidimensional models were used to simulate channel hydraulics to efficiently investigate a wide range of channel sinuosity, width/depth ratios, bankfull discharges, and valley slopes. When simulating such a wide range of channel conditions, multidimensional models offer a more efficiency method of generating consistent datasets than either field studies or physical modeling. This paper demonstrates how multidimensional models can be used to identify important hydraulic relationships that are otherwise difficult to determine.

Knowledge Graph OLAP

A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.