Differential Evolution with Enhanced Abilities for Adaptation Applied to Heterogeneous Numerical Optimization

This article presents an exploration of Differential Evolution (DE) algorithm with enhanced adaptability. The main purpose of this study is to identify how this search method can cope with changes of the number of variables of a hard design test, unaided. The results clearly show that this method successfully solves the explored functions

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Text Content Features for Hybrid Recommendations : Pre-trained Language Models for Better Recommendations

Nowadays, with the ever growing availability of options in many areas of our lives, it is crucial to have good ways to navigate your choices. This is why recommendation engines’ role is growing more important. Recommenders are often based on user-item interaction. In many areas like news and podcasts, however, by the time there is enough interaction data for an item, the item has already become irrelevant. This is why incorporating content features is desirable, as the content does not depend on the popularity or novelty of an item. Very often, there is text describing an item, so text features are good candidates for features within recommender systems. Within Natural Language Processing (NLP), pre-trained language models based on the Transformer architecture have brought a revolution in recent years, achieving state-of-the-art performance on many language tasks. Because of this, it is natural to explore how such models can play a role within recommendation systems. The scope of this work is on the intersection between NLP and recommendation systems where we investigate what are the effects of adding BERT-based encodings of titles and descriptions of movies and books to a recommender system. The results show that even in off-the-shelf BERT-models there is a considerable amount of information on movie and book similarity. It also shows that BERT based representations could be used in a recommender system for user recommendation to combine the best of collaborative and content representations. In this thesis, it is shown that adding deep pre-trained language model representations could improve a recommender system’s capability to predict good items for users with up to 0.43 AUC-ROC score for a shallow model, and 0.017 AUC-ROC score for a deeper model. It is also shown that SBERT can be fine-tuned to encode item similarity with up to 0.03 nDCG and up to 0.05 nDCG@10 score improvement. Med den ständigt växande tillgängligheten av val i många delar av våra liv har det blivit viktigt att enkelt kunna navigera kring olika alternativ. Det är därför rekommendationssystems har blivit viktigare. Rekommendationssystem baseras ofta på interaktion-historiken mellan användare och artikel. När tillräckligt mycket data inom nyheter och podcast har hunnits samlats in för att utföra en rekommendation så har artikeln hunnit bli irrelevant. Det är därför det är önskvärt att införa innehållsfunktioner till rekommenderaren, då innehållet inte är beroende av popularitet eller nymodigheten av artikeln. Väldigt ofta finns det text som beskriver en artikel vilket har lett till textfunktioner blivit bra kandidater som funktion för rekommendationssystem. Inom Naturlig Språkbehandling (NLP), har förtränande språkmodeller baserad på transformator arkitekturen revolutionerat området de senaste åren. Den nya arkitekturen har uppnått toppmoderna resultat på flertal språkuppgifter. Tack vare detta, har det blivit naturligt att utforska hur sådana modeller kan fungera inom rekommendationssystem. Det här arbetet är mellan två områden, NLP och rekommendationssystem. Arbetet utforskar effekten av att lägga till BERT-baserade kodningar av titel och beskrivning av filmer, samt böcker till ett rekommendationssystem. Resultaten visar att även i förpackade BERT modeller finns det mycket av information om likheter mellan film och böcker. Resultaten visar även att BERT representationer kan användas i rekommendationssystem för användarrekommendationer, i kombination med kollaborativa och artikel baserade representationer. Uppsatsen visar att lägga till förtränade djupspråkmodell representationer kan förbättra rekommendationssystemens förmåga att förutsäga bra artiklar för användare. Förbättringarna är upp till 0.43 AUC-ROC poäng för en grundmodell, samt 0.017 AUC-ROC poäng för en djupmodell. Uppsatsen visar även att SBERT kan bli finjusterad för att koda artikel likhet med upp till 0.03 nDCG och upp till 0.05 nDCG@10 poängs förbättring

Impairment of NKG2D-mediated tumor immunity by TGF-β

Transforming growth factor-β (TGF-β) suppresses innate and adaptive immune responses via multiple mechanisms. TGF-β also importantly contributes to the formation of an immunosuppressive tumor microenvironment thereby promoting tumor growth. Amongst others, TGF-β impairs tumor recognition by cytotoxic lymphocytes via NKG2D. NKG2D is a homodimeric C-type lectin-like receptor expressed on virtually all human NK cells and cytotoxic T cells, and stimulates their effector functions upon engagement by NKG2D ligands (NKG2DL). While NKG2DL are mostly absent from healthy cells, their expression is induced by cellular stress and malignant transformation, and, accordingly, frequently detected on various tumor cells. Hence, the NKG2D axis is thought to play a decisive role in cancer immunosurveillance and, obviously, often is compromised in clinically apparent tumors. There is mounting evidence that TGF-β, produced by tumor cells and immune cells in the tumor microenvironment, plays a key role in blunting the NKG2D-mediated tumor surveillance. Here, we review the current knowledge on the impairment of NKG2D-mediated cancer immunity through TGF-β and discuss therapeutic approaches aiming at counteracting this major immune escape pathway. By reducing tumor-associated expression of NKG2DL and blinding cytotoxic lymphocytes through down-regulation of NKG2D, TGF-β is acting upon both sides of the NKG2D axis severely compromising NKG2D-mediated tumor rejection. Consequently, novel therapies targeting the TGF-β pathway are expected to reinvigorate NKG2D-mediated tumor elimination and thereby to improve the survival of cancer patients

The NKG2D ligand ULBP4 is not expressed by human monocytes.

The C-type lectin-like receptor NKG2D contributes to the immunosurveillance of virally infected and malignant cells by cytotoxic lymphocytes. A peculiar and puzzling feature of the NKG2D-based immunorecognition system is the high number of ligands for this single immunoreceptor. In humans, there are a total of eight NKG2D ligands (NKG2DL) comprising two members of the MIC (MICA, MICB) and six members of the ULBP family of glycoproteins (ULBP1 to ULBP6). While MICA has been extensively studied with regard to its biochemistry, cellular expression and function, very little is known about the NKG2DL ULBP4. This is, at least in part, due to its rather restricted expression by very few cell lines and tissues. Recently, constitutive ULBP4 expression by human monocytes was reported, questioning the view of tissue-restricted ULBP4 expression. Here, we scrutinized ULBP4 expression by human peripheral blood mononuclear cells and monocytes by analyzing ULBP4 transcripts and ULBP4 surface expression. In contrast to MICA, there was no ULBP4 expression detectable, neither by freshly isolated monocytes nor by PAMP-activated monocytes. However, a commercial antibody erroneously indicated surface ULBP4 on monocytes due to a non-ULBP4-specific binding activity, emphasizing the critical importance of validated reagents for life sciences. Collectively, our data show that ULBP4 is not expressed by monocytes, and likely also not by other peripheral blood immune cells, and therefore exhibits an expression pattern rather distinct from other human NKG2DL

Looking for added value of milk products of autochthonous sheep breeds

In order to determine the added value of the product, characterization of sheep milk and cheese samples from Sjenica sheep were assessed and compared to British milk sheep. The analyses were carried out according to standard chemical procedures. A comparative study showed that the milk from the Sjenica sheep was higher in fat and protein and had a lower casein/fat ratio. It also had higher total and somatic cell counts than British milk sheep. Sjenica cheese had significantly higher dry matter and fat content. However, both cheeses were classified as soft full-fat cheese with brine. The study showed that sheep's milk contains many essential components for cheesemaking and distinguishes sheep's cheese from other types of cheese, but standardisation of cheese production is still needed. To preserve the autochthonous sheep breeds of Serbia, promoting cheese production with added value is crucial and together with increased production could contribute to the sustainable development of rural area