Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management

Operational risk was one of the most important risks in the 2008 global financial crisis. This is due to limitations of the applied models in explaining and estimating this type of risk from highly qualitative information related to failures in the operations of financial organizations. A review of research literature on this area indicates an increase in the development of models for the estimation of the operational value at risk. However, there is a lack of models that use qualitative information for estimating this type of risk. Motivated by this finding, we propose a Flexible Inverse Adaptive Fuzzy Inference Model that integrates both a novel Montecarlo sampling method for the linguistic input variables of frequency and severity that allow the characterization of a risk event, the impact of risk management matrices to estimate the loss distribution and the associated operational value at risk. The methodology follows a loss distribution approach as defined by Basel II. A benefit of the proposed model is that it works with highly qualitative risk data and it also connects the risk measurement (operational value at risk) with risk management, based on risk management matrices. This way, we mitigate limitations related to a lack of available operational risk event data when assessing operational risk. We evaluate the experimental results obtained through the proposed model by using the Index of Agreement indicator. The results provide a flexible loss distribution under different risk profiles or risk management matrices that explain the evolution of operational risk in real time

A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative datainnd also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management

Affinity proteomics within rare diseases: a BIO‐NMD study for blood biomarkers of muscular dystrophies

Despite the recent progress in the broad‐scaled analysis of proteins in body fluids, there is still a lack in protein profiling approaches for biomarkers of rare diseases. Scarcity of samples is the main obstacle hindering attempts to apply discovery driven protein profiling in rare diseases. We addressed this challenge by combining samples collected within the BIO‐NMD consortium from four geographically dispersed clinical sites to identify protein markers associated with muscular dystrophy using an antibody bead array platform with 384 antibodies. Based on concordance in statistical significance and confirmatory results obtained from analysis of both serum and plasma, we identified eleven proteins associated with muscular dystrophy, among which four proteins were elevated in blood from muscular dystrophy patients: carbonic anhydrase III (CA3) and myosin light chain 3 (MYL3), both specifically expressed in slow‐twitch muscle fibers and mitochondrial malate dehydrogenase 2 (MDH2) and electron transfer flavoprotein A (ETFA). Using age‐matched sub‐cohorts, 9 protein profiles correlating with disease progression and severity were identified, which hold promise for the development of new clinical tools for management of dystrophinopathies

An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management

The file attached to this record is the author's final peer reviewed version.Operational risk refers to deficiencies in processes, systems, people or external events, which may generate losses for an organization. The Basel Committee on Banking Supervision has defined different possibilities for the measurement of operational risk, although financial institutions are allowed to develop their own models to quantify operational risk. The advanced measurement approach, which is a risk-sensitive method for measuring operational risk, is the financial institutions preferred approach, among the available ones, in the expectation of having to hold less regulatory capital for covering operational risk with this approach than with alternative approaches. The advanced measurement approach includes the loss distribution approach as one way to assess operational risk. The loss distribution approach models loss distributions for business-line-risk combinations, with the regulatory capital being calculated as the 99,9% operational value at risk, a percentile of the distribution for the next year annual loss. One of the most important issues when estimating operational value at risk is related to the structure (type of distribution) and shape (long tail) of the loss distribution. The estimation of the loss distribution, in many cases, does not allow to integrate risk management and the evolution of risk; consequently, the assessment of the effects of risk impact management on loss distribution can take a long time. For this reason, this paper proposes a flexible integrated inverse adaptive fuzzy inference model, which is characterized by a Monte-Carlo behavior, that integrates the estimation of loss distribution and different risk profiles. This new model allows to see how the management of risk of an organization can evolve over time and it effects on the loss distribution used to estimate the operational value at risk. The experimental study results, reported in this paper, show the flexibility of the model in identifying (1) the structure and shape of the fuzzy input sets that represent the frequency and severity of risk; and (2) the risk profile of an organization. Therefore, the proposed model allows organizations or financial entities to assess the evolution of their risk impact management and its effect on loss distribution and operational value at risk in real time

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: Building a Treatabolome

Background: Rare diseases are individually rare but globally affect around 6% of the population, and in over 70% of cases are genetically determined. Their rarity translates into a delayed diagnosis, with 25% of patients waiting 5 to 30 years for one. It is essential to raise awareness of patients and clinicians of existing gene and variant-specific therapeutics at the time of diagnosis to avoid that treatment delays add up to the diagnostic odyssey of rare diseases' patients and their families. Aims: This paper aims to provide guidance and give detailed instructions on how to write homogeneous systematic reviews of rare diseases' treatments in a manner that allows the capture of the results in a computer-accessible form. The published results need to comply with the FAIR guiding principles for scientific data management and stewardship to facilitate the extraction of datasets that are easily transposable into machine-actionable information. The ultimate purpose is the creation of a database of rare disease treatments ("Treatabolome") at gene and variant levels as part of the H2020 research project Solve-RD. Results: Each systematic review follows a written protocol to address one or more rare diseases in which the authors are experts. The bibliographic search strategy requires detailed documentation to allow its replication. Data capture forms should be built to facilitate the filling of a data capture spreadsheet and to record the application of the inclusion and exclusion criteria to each search result. A PRISMA flowchart is required to provide an overview of the processes of search and selection of papers. A separate table condenses the data collected during the Systematic Review, appraised according to their level of evidence. Conclusions: This paper provides a template that includes the instructions for writing FAIR-compliant systematic reviews of rare diseases' treatments that enables the assembly of a Treatabolome database that complement existing diagnostic and management support tools with treatment awareness data

A new technique for precisely and accurately measuring lumbar spine bone mineral density in mice using clinical dual energy X-ray absorptiometry (DXA)

Dual Energy X-ray Absorptiometry (DXA) is effective in measuring bone mineral density (BMD) in mice for early detection of osteoporosis. However, scanners designed for use with small animals (i.e. PIXImus) are very expensive. Used human DXA machines are cheaper to obtain, but analysis of scans from these instruments is operator-dependent. Obtaining reliable data depends on having a single operator analyze the scans in a blinded fashion. Scan quality is improved by excising the bone prior to scanning, which does not allow serial measurements. This study describes a novel method of analyzing lumbar spine BMD in mice using whole body DXA. This non-invasive technique has a high degree of precision and reproducibility, with good correlation between multiple observers. Inter-observer variability (0.063 ± 0.00317 g/cm2 [mean ± SD], 5.05 [% coefficient of variation (CV)], repeat scan variability (0.063 ± 0.00364 g/cm2 [mean ± SD], 5.94 [%CV]) were very low compared to variability between different animals (0.063 ± 0.00588 g/cm2 [mean ± SD], 9.64 [%CV]) and variability seen in same animal over time (0.011 ± 0.00885 g/cm2 [mean ± SD], 80.68 [%CV]). The measurement error is thus smaller than the biological variation. Accuracy was determined by comparing average peak BMD from two scans per mouse in-vivo (0.066 g/cm2) versus excised spine (0.065 g/cm2). Furthermore, correlation between bone ash weights and whole body lumbar spine BMD measurements (p < 0.0001) was highly significant. This technique thus shows a high degree of precision and accuracy, even with multiple observers, for measuring BMD in mice using a DXA machine designed for clinical use

Mechanical Contributions of the Cortical and Trabecular Compartments Contribute to Differences in Age-Related Changes in Vertebral Body Strength in Men and Women Assessed by QCT-Based Finite Element Analysis

The biomechanical mechanisms underlying sex-specific differences in age-related vertebral fracture rates are ill defined. To gain insight into this issue, we used finite element analysis of clinical computed tomography (CT) scans of the vertebral bodies of L3 and T10 of young and old men and women to assess age- and sex-related differences in the strength of the whole vertebra, the trabecular compartment, and the peripheral compartment (the outer 2 mm of vertebral bone, including the thin cortical shell). We sought to determine whether structural and geometric changes with age differ in men and women, making women more susceptible to vertebral fractures. As expected, we found that vertebral strength decreased with age 2-fold more in women than in men. The strength of the trabecular compartment declined significantly with age for both sexes, whereas the strength of the peripheral compartment decreased with age in women but was largely maintained in men. The proportion of mechanical strength attributable to the peripheral compartment increased with age in both sexes and at both vertebral levels. Taken together, these results indicate that men and women lose vertebral bone differently with age, particularly in the peripheral (cortical) compartment. This differential bone loss explains, in part, a greater decline in bone strength in women and may contribute to the higher incidence of vertebral fractures among women than men. © 2011 American Society for Bone and Mineral Research

P2X7 purinoceptor alterations in dystrophic mdx mouse muscles: Relationship to pathology and potential target for treatment.

Open AccessDuchenne muscular dystrophy (DMD) is a lethal inherited muscle disorder. Pathological characteristics of DMD skeletal muscles include, among others, abnormal Ca2 homeostasis and cell signalling. Here, in the mdx mouse model of DMD, we demonstrate significant P2X7 receptor abnormalities in isolated primary muscle cells and cell lines and in dystrophic muscles in vivo. P2X7 mRNA expression in dystrophic muscles was significantly up-regulated but without alterations of specific splice variant patterns. P2X7 protein was also up-regulated and this was associated with altered function of P2X7 receptors producing increased responsiveness of cytoplasmic Ca2 and extracellular signal-regulated kinase (ERK) phosphorylation to purinergic stimulation and altered sensitivity to NAD. Ca2 influx and ERK signalling were stimulated by ATP and BzATP, inhibited by specific P2X7 antagonists and insensitive to ivermectin, confirming P2X7 receptor involvement. Despite the presence of pannexin-1, prolonged P2X7 activation did not trigger cell permeabilization to propidium iodide or Lucifer yellow. In dystrophic mice, in vivo treatment with the P2X7 antagonist Coomassie Brilliant Blue reduced the number of degeneration–regeneration cycles in mdx skeletal muscles. Altered P2X7 expression and function is thus an important feature in dystrophic mdx muscle and treatments aiming to inhibit P2X7 receptor might slow the progression of this disease

Distribution of bone density in the proximal femur and its association with hip fracture risk in older men: The osteoporotic fractures in men (MrOS) study

This prospective case-cohort study aimed to map the distribution of bone density in the proximal femur and examine its association with hip fracture. We analyzed baseline quantitative computed tomography (QCT) scans in 250 men aged 65 years or older, which comprised a randomly-selected subcohort of 210 men and 40 cases of first hip fracture during a mean follow-up period of 5.5 years. We quantified cortical, trabecular, and integral volumetric bone mineral density (vBMD), and cortical thickness (CtTh) in four quadrants of cross-sections along the length of the femoral neck (FN), intertrochanter (IT), and trochanter (TR). In most quadrants, vBMDs and CtTh were significantly (p  0.05) better than TH aBMD. With an area under the receiver operating characteristic curve (AUC) of 0.901 (95% CI, 0.852–0.950), the regression model combining TH aBMD, age, and trabecular vBMD predicted hip fracture significantly (p < 0.05) better than TH aBMD alone or TH aBMD plus age. These findings confirm that both cortical and trabecular bone contribute to hip fracture risk and highlight trabecular vBMD at the FN and TR as an independent risk factor