Glutamine-Derived Aspartate Biosynthesis in Cancer Cells: Role of Mitochondrial Transporters and New Therapeutic Perspectives

Aspartate has a central role in cancer cell metabolism. Aspartate cytosolic availability is crucial for protein and nucleotide biosynthesis as well as for redox homeostasis. Since tumor cells display poor aspartate uptake from the external environment, most of the cellular pool of aspar-tate derives from mitochondrial catabolism of glutamine. At least four transporters are involved in this metabolic pathway: the glutamine (SLC1A5_var), the aspartate/glutamate (AGC), the as-partate/phosphate (uncoupling protein 2, UCP2), and the glutamate (GC) carriers, the last three belonging to the mitochondrial carrier family (MCF). The loss of one of these transporters causes a paucity of cytosolic aspartate and an arrest of cell proliferation in many different cancer types. The aim of this review is to clarify why different cancers have varying dependencies on metabolite transporters to support cytosolic glutamine-derived aspartate availability. Dissecting the precise metabolic routes that glutamine undergoes in specific tumor types is of upmost importance as it promises to unveil the best metabolic target for therapeutic intervention

Picking on the family: disrupting android malware triage by forcing misclassification

Machine learning classification algorithms are widely applied to different malware analysis problems because of their proven abilities to learn from examples and perform relatively well with little human input. Use cases include the labelling of malicious samples according to families during triage of suspected malware. However, automated algorithms are vulnerable to attacks. An attacker could carefully manipulate the sample to force the algorithm to produce a particular output. In this paper we discuss one such attack on Android malware classifiers. We design and implement a prototype tool, called IagoDroid, that takes as input a malware sample and a target family, and modifies the sample to cause it to be classified as belonging to this family while preserving its original semantics. Our technique relies on a search process that generates variants of the original sample without modifying their semantics. We tested IagoDroid against RevealDroid, a recent, open source, Android malware classifier based on a variety of static features. IagoDroid successfully forces misclassification for 28 of the 29 representative malware families present in the DREBIN dataset. Remarkably, it does so by modifying just a single feature of the original malware. On average, it finds the first evasive sample in the first search iteration, and converges to a 100% evasive population within 4 iterations. Finally, we introduce RevealDroid*, a more robust classifier that implements several techniques proposed in other adversarial learning domains. Our experiments suggest that RevealDroid* can correctly detect up to 99% of the variants generated by IagoDroid

KRAS-regulated glutamine metabolism requires UCP2-mediated aspartate transport to support pancreatic cancer growth

The oncogenic KRAS mutation has a critical role in the initiation of human pancreatic ductal adenocarcinoma (PDAC) since it rewires glutamine metabolism to increase reduced nicotinamide adenine dinucleotide phosphate (NADPH) production, balancing cellular redox homeostasis with macromolecular synthesis1,2. Mitochondrial glutamine-derived aspartate must be transported into the cytosol to generate metabolic precursors for NADPH production2. The mitochondrial transporter responsible for this aspartate efflux has remained elusive. Here, we show that mitochondrial uncoupling protein 2 (UCP2) catalyses this transport and promotes tumour growth. UCP2-silenced KRASmut cell lines display decreased glutaminolysis, lower NADPH/NADP+ and glutathione/glutathione disulfide ratios and higher reactive oxygen species levels compared to wild-type counterparts. UCP2 silencing reduces glutaminolysis also in KRASWT PDAC cells but does not affect their redox homeostasis or proliferation rates. In vitro and in vivo, UCP2 silencing strongly suppresses KRASmut PDAC cell growth. Collectively, these results demonstrate that UCP2 plays a vital role in PDAC, since its aspartate transport activity connects the mitochondrial and cytosolic reactions necessary for KRASmut rewired glutamine metabolism2, and thus it should be considered a key metabolic target for the treatment of this refractory tumour

Il ruolo della struttura semantica e della composizione morfologica nell'accuratezza di decodifica:dislessici evolutivi e normolettori a confronto.

Nello specifico, il nostro lavoro di ricerca si propone di valutare l’influenza degli indizi contestuali sull’accuratezza di decodifica (corretto posizionamento dell’accento) del dislessico evolutivo e del normolettore confrontando due principali condizioni: assenza e presenza di disponibilità contestuale. In aggiunta, nell’ottica di effettuare una specifica indagine linguistica, l’intento è anche quello di analizzare l’influenza della composizione sillabico-accentuale, sull’accuratezza di decodifica (corretto posizionamento dell’accento) dei due gruppi di soggetti (Mulatti e Job, 2003; Marcolini e Burani, 2003; Burani, Barca e Ellis, 2006; Marcolini, Donato, Stella e Burani, 2006; Barca, Ellis e Burani, 2007). A tal proposito, sappiamo che, nella maggior parte delle parole italiane, l’accento è collocato sulla vocale della penultima sillaba (accentazione regolare: ballàre); più rara è invece la condizione che vede l’accento collocato sulla vocale della terzultima sillaba (accentazione irregolare: cèlebre)

Fuzzy modelling for a rotary dryer

Abstract In this research a fuzzy model is developed for a rotary dryer. It is applied to the pilot plant rotary dryer located in the Control Engineering Laboratory at Oulu University. Firstly, a literature review looking at the current situation of fuzzy modelling and comparison of different methods is done. One modelling method is then applied to the building of the model from data. The rule parameters are determined on the basis of clusters created by Kohonen learning rule method and the initial model is optimised by the trial and error method. The resulting model behaviour is examined with simulation and, the results achieved are compared with other models