The EPH/Ephrin System in Bone and Soft Tissue Sarcomas’ Pathogenesis and Therapy: New Advancements and a Literature Review

Musculoskeletal sarcomas represent rare heterogenous malignancies of mesenchymal origin that can be divided in two distinct subtypes, bone and soft tissue sarcomas. Current treatment options combine the surgical excision of local tumors and multidrug chemotherapy to prevent metastatic widespread disease. Due to the grim prognosis that usually accompanies such tumors, researchers have attempted to shed light on the molecular pathways implicated in their pathogenesis in order to develop novel, innovative, personalized therapeutic strategies. Erythropoietin-producing human hepatocellular receptors (EPHs) are tyrosine-kinase transmembrane receptors that, along with their ligands, ephrins, participate in both tumor-suppressive or tumor-promoting signaling pathways in bone and soft tissue sarcomas. The EPH/ephrin axis orchestrates cancerous processes such as cell–cell and cell–substrate adhesion and enhances the remodeling of the intracellular cytoskeleton to stimulate the motility and invasiveness of sarcoma cells. The purpose of our study was to review published PubMed literature to extract results from in vitro, in vivo and clinical trials indicative of the role of EPH/ephrin signaling in bone and soft tissue sarcomas. Based on these reports, significant interactions between the EPH/ephrin signaling pathway and a plethora of normal and abnormal cascades contribute to molecular mechanisms enhancing malignancy during sarcoma progression. In addition, EPHs and ephrins are prospective candidates for diagnostic, monitoring and therapeutic purposes in the clinical setting against bone and soft tissue sarcomas.     

Occupancy‐based decision support system for building management: From automation to end‐user persuasion

In this paper, an intelligent occupancy‐based, automated building control system is initially introduced, which has the capability of reducing energy consumption while respecting the occupants' comfort and actions inside building areas. Data stemming from a multisensorial network is combined with implicitly extracted information, since no direct feedback is expected by the users, towards unsupervised monitoring and control of building operation. However, due to significantly higher costs of actuating equipment and the reluctance that some end users show in accepting fully automated solutions, a new hybrid approach is conceptualized and presented; the automated decision support system is supplemented with a persuasion mechanism aimed at increasing energy savings, through raising user awareness. The persuasive methodology employs a continuous feedback mechanism in order to select the optimal incentive strategy by taking into account user success rates, in terms of requested actions, as well as available mechanisms, and daily harassment.     

Estimation and evaluation of productivity change and its drivers in the English and Welsh water sector: a stochastic cost frontier approach

ABSTRACT

Estimating productivity change and its determinants is of great importance when measuring the performance of a firm. A true random effect cost model was employed to measure the cost efficiency of water companies in England and Wales from 1993 to 2016. Subsequently, total factor productivity change was estimated and decomposed into cost-efficiency change, technical change, scale efficiency change, output effect and residual price effect. The results indicate that the English and Welsh water industry increased its productivity over the period examined at a rate of about 2.1%, and it was influenced primarily by technical change. The price reviews appear to have a positive impact on the productivity of water-only companies during the whole period examined. In the case of water and sewerage companies, the 1994 and 1999 price reviews had a positive impact on productivity, whereas the last two price reviews had an adverse impact.     

Cost Efficiency of English and Welsh Water Companies: a Meta-Stochastic Frontier Analysis

Water Resources Management - In regulated industries it is important to identify appropriate performance benchmarks to incentivize companies’ performance. This study applies a stochastic...     

The Impact of Angiogenesis in the Most Common Salivary Gland Malignant Tumors

Salivary gland carcinomas (SGCs) represent a group of rare tumors, with complete surgical resection being the main treatment option. Therapeutic armory for cases of locally aggressive, recurrent, and/or metastatic SGCs, though, remains poor since they exhibit high rates of resistance to systematic therapy. Angiogenesis is considered one of the contemporary hallmarks of cancer and anti-angiogenic factors have already been approved for the treatment of several cancer types. This review aims to summarize, in a histotype-specific manner, the most current available data on the angiogenic factors implicated in SGC angiogenesis, in order to highlight the differences between the most common SGC histotypes and the factors that may have a potential role as therapeutic targets.     

EEG transient event detection and classification using association rules.

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.     

Trap‐State Suppression and Improved Charge Transport in PbS Quantum Dot Solar Cells with Synergistic Mixed‐Ligand Treatments

The power conversion efficiency of colloidal PbS‐quantum‐dot (QD)‐based solar cells is significantly hampered by lower‐than‐expected open circuit voltage (V_OC). The V_OC deficit is considerably higher in QD‐based solar cells compared to other types of existing solar cells due to in‐gap trap‐induced bulk recombination of photogenerated carriers. Here, this study reports a ligand exchange procedure based on a mixture of zinc iodide and 3‐mercaptopropyonic acid to reduce the V_OC deficit without compromising the high current density. This layer‐by‐layer solid state ligand exchange treatment enhances the photovoltaic performance from 6.62 to 9.92% with a significant improvement in V_OC from 0.58 to 0.66 V. This study further employs optoelectronic characterization, X‐ray photoelectron spectroscopy, and photoluminescence spectroscopy to understand the origin of V_OC improvement. The mixed‐ligand treatment reduces the sub‐bandgap traps and significantly reduces bulk recombination in the devices.     

Focused crawling for the hidden web

A constantly growing amount of high-quality information resides in databases and is guarded behind forms that users fill out and submit. The Hidden Web comprises all these information sources that conventional web crawlers are incapable of discovering. In order to excavate and make available meaningful data from the Hidden Web, previous work has focused on developing query generation techniques that aim at downloading all the content of a given Hidden Web site with the minimum cost. However, there are circumstances where only a specific part of such a site might be of interest. For example, a politics portal should not have to waste bandwidth or processing power to retrieve sports articles just because they are residing in databases also containing documents relevant to politics. In cases like this one, we need to make the best use of our resources in downloading only the portion of the Hidden Web site that we are interested in. We investigate how we can build a focused Hidden Web crawler that can autonomously extract topic-specific pages from the Hidden Web by searching only the subset that is related to the corresponding area. In this regard, we present an approach that progresses iteratively and analyzes the returned results in order to extract terms that capture the essence of the topic we are interested in. We propose a number of different crawling policies and we experimentally evaluate them with data from four popular sites. Our approach is able to download most of the content in search in all cases, using a significantly smaller number of queries compared to existing approaches.