Papillary and nonpapillary renal cell tumours can be differentiated according to their genetic constitution. In this study, their incidence in end stage kidney disease has been investigated histologically. Nonpapillary renal cell carcinoma was observed in 22 cases (51.2%) whereas papillary renal cell tumours were diagnosed in 21 (48.8%) of the 43 patients with end stage kidney disease. The incidence of papillary renal cell tumours in end stage kidney disease is significantly higher (chi 2 = 31.9; P < 0.001) than in the general population (4.8%). Haemodialysis patients with nonpapillary and papillary renal cell tumours did not show significant differences in age, sex or size of tumour. However, patients with papillary renal cell tumours had received longer duration of haemodialysis than patients with nonpapillary renal cell carcinomas. These data suggest that not only different genetic events but also different aetiological factors are involved in the development of the two types of tumour in end stage kidney disease.

No abstract

The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.

Enterprises which are distributed in space and/or which are composed as a temporary joint venture of legally different units recently often called virtual (extended) enterprises. Planning, design and operation (management) goals and requirements of such firms are generally different from those of single, centralized enterprises. The basic feature of an extended (virtual) enterprise is that the co-operating units of it keep their independence during the life-cycle of the co-operation-what is well regulated by the rules of the given conglomerate. It has to be accepted-on the other hand-that several basic functionalities and goals are the same for all types of distributed, large, complex organizations, which are the targets of our recent study.The evolution of web-based manufacturing design/planning and operation system philosophies can be followed through the works presented in this paper. We intend to give software solutions for design, planning and operation (management) of complex, networked organizations represented as nodes of networks. In the first part of the paper, solutions are given to manage complex logistics flows of distributed SMEs, giving more sophisticated solutions than the commonly used supply-chain management (SCM) packages available in the market. The second problem we solve is a complex, web-based solution to manage large, expensive, multi-site, multi-company projects using any type of Enterprise Resource Planning (ERP) and flow management solutions. Our goal is to integrate as many available solutions as possible and to make only the appropriate frameworks including decision-support systems where necessary. The first part of the work means the establishment and application of a web server at each node of the co-operating network, while the second approach uses only one, joint web server and each node communicates with it through the network. These architectures are easy to be integrated if needed, i.e. logistic flows and project management can be solved together. #

No abstract

No abstract

End-stage renal disease (ESRD) and acquired cystic renal disease (ACRD) are associated with high risk of development of renal cell tumors (RCT) displaying unusual phenotype and genotype. The underlying molecular mechanism is not yet known. To explore the molecular microenvironment, we have established the expression profile of ESRD/ACRD kidneys. RNA extracted from normal and ESRD/ACRD kidneys and distinct types of RCT was subjected to Affymetrix HG U133 micro array analysis. A gene expression signature indicated cancer-related biological processes in the remodeling of ESRD/ACRD kidneys. Quantitative RT-PCR studies confirmed a specific gene signature including a functional group of inflammatory cytokines and also cytokeratins associated with stem cell characteristics of epithelial cells. Several of the signature genes including the SCEL were expressed in ESRD/ACRD-associated papillary RCT as well. Immunohistological analysis confirmed the expression of CXCL8 and its receptor CXCR2 as well as the expression of SCEL in hyperplastic tubular, cystic, and papillary structures and RCTs in ESRD/ACRD kidney. Our data indicates that ESRD/ACRD is a novel disease and the inflammatory microenvironment altered plasticity, and stem cell characteristics of epithelial cells may be associated with the high risk of tumor development.

With the ubiquity and anonymity of the Internet, the spread of hate speech has been a growing concern for many years now. The language used for the purpose of dehumanizing, defaming or threatening individuals and marginalized groups not only threatens the mental health of its targets, as well as their democratic access to the Internet, but also the fabric of our society. Because of this, much effort has been devoted to manual moderation. The amount of data generated each day, particularly on social media platforms such as Facebook and twitter, however makes this a Sisyphean task. This has led to an increased demand for automatic methods of hate speech detection. Here, to contribute towards solving the task of hate speech detection, we worked with a simple ensemble of transformer models on a twitter-based hate speech benchmark. Using this method, we attained a weighted F1score of 0.8426, which we managed to further improve by leveraging more training data, achieving a weighted F1-score of 0.8504. Thus markedly outperforming the best performing system in the literature.