Black-Scholes model under subordination

In this paper we consider a new mathematical extension of the Black-Scholes model in which the stochastic time and stock share price evolution is described by two independent random processes. The parent process is Brownian, and the directing process is inverse to the totally skewed, strictly \alpha-stable process. The subordinated process represents the Brownian motion indexed by an independent, continuous and increasing process. This allows us to introduce the long-term memory effects in the classical Black-Scholes model.Comment: 8 page

Assessing an ontology for the representation of clinical protocols in decision support systems

In order to assess the expressiveness of the CompGuide ontology for Clinical Practice Guidelines, a study was conducted with fourteen students of the Integrated Masters in Biomedical Engineering from the University of Minho in Portugal to whom it was proposed the representation of multiple guidelines according to the ontology. They were then asked to evaluate the ontology through a questionnaire and written reports. Although the results seem promising, there is the need for significant improvements mainly in: the representation of medication prescriptions, the tasks used to retrieve information from the patient, the diversity of actions offered by the ontology, the expressiveness of conditions regarding the state of a patient, and temporal constraints.FCT - Fundação para a Ciência e a Tecnologiainfo:eu-repo/semantics/publishedVersio

American Society of Hematology 2020 guidelines for sickle cell disease: Prevention, diagnosis, and treatment of cerebrovascular disease in children and adults

BACKGROUND: Central nervous system (CNS) complications are among the most common, devastating sequelae of sickle cell disease (SCD) occurring throughout the lifespan.OBJECTIVE: These evidence-based guidelines of the American Society of Hematology are intended to support the SCD community in decisions about prevention, diagnosis, and treatment of the most common neurological morbidities in SCD.METHODS: The Mayo Evidence-Based Practice Research Program supported the guideline development process, including updating or performing systematic evidence reviews. The panel used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, including GRADE evidence-to-decision frameworks, to assess evidence and make recommendations.RESULTS: The panel placed a higher value on maintaining cognitive function than on being alive with significantly less than baseline cognitive function. The panel developed 19 recommendations with evidence-based strategies to prevent, diagnose, and treat CNS complications of SCD in low-middle- and high-income settings.CONCLUSIONS: Three of 19 recommendations immediately impact clinical care. These recommendations include: use of transcranial Doppler ultrasound screening and hydroxyurea for primary stroke prevention in children with hemoglobin SS (HbSS) and hemoglobin Sβ0 (HbSβ0) thalassemia living in low-middle-income settings; surveillance for developmental delay, cognitive impairments, and neurodevelopmental disorders in children; and use of magnetic resonance imaging of the brain without sedation to detect silent cerebral infarcts at least once in early-school-age children and once in adults with HbSS or HbSβ0 thalassemia. Individuals with SCD, their family members, and clinicians should become aware of and implement these recommendations to reduce the burden of CNS complications in children and adults with SCD.</p

Metabolic drift in the aging brain.

Brain function is highly dependent upon controlled energy metabolism whose loss heralds cognitive impairments. This is particularly notable in the aged individuals and in age-related neurodegenerative diseases. However, how metabolic homeostasis is disrupted in the aging brain is still poorly understood. Here we performed global, metabolomic and proteomic analyses across different anatomical regions of mouse brain at different stages of its adult lifespan. Interestingly, while severe proteomic imbalance was absent, global-untargeted metabolomics revealed an energymetabolic drift or significant imbalance in core metabolite levels in aged mouse brains. Metabolic imbalance was characterized by compromised cellular energy status (NAD decline, increased AMP/ATP, purine/pyrimidine accumulation) and significantly altered oxidative phosphorylation and nucleotide biosynthesis and degradation. The central energy metabolic drift suggests a failure of the cellular machinery to restore metabostasis (metabolite homeostasis) in the aged brain and therefore an inability to respond properly to external stimuli, likely driving the alterations in signaling activity and thus in neuronal function and communication

Dark Matter Model Selection and the ATIC/PPB-BETS anomaly

We argue that we may be able to sort out dark matter models in which electrons are generated through the annihilation and/or decay of dark matter, by using a fact that the initial energy spectrum is reflected in the cosmic-ray electron flux observed at the Earth even after propagation through the galactic magnetic field. To illustrate our idea we focus on three representative initial spectra: (i)monochromatic (ii)flat and (iii)double-peak ones. We find that those three cases result in significantly different energy spectra, which may be probed by the Fermi satellite in operation or an up-coming cosmic-ray detector such as CALET.Comment: 19 pages, 8 figure

EGIA–evolutionary optimisation of gene regulatory networks, an integrative approach

Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored, to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement