Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support.

Biometrics and biometric-enabled decision support systems (DSS) have become a mandatory part of complex dynamic systems such as security checkpoints, personal health monitoring systems, autonomous robots, and epidemiological surveillance. Risk, trust, and bias (R-T-B) are emerging measures of performance of such systems. The existing studies on the R-T-B impact on system performance mostly ignore the complementary nature of R-T-B and their causal relationships, for instance, risk of trust, risk of bias, and risk of trust over biases. This paper offers a complete taxonomy of the R-T-B causal performance regulators for the biometric-enabled DSS. The proposed novel taxonomy links the R-T-B assessment to the causal inference mechanism for reasoning in decision making. Practical details of the R-T-B assessment in the DSS are demonstrated using the experiments of assessing the trust in synthetic biometric and the risk of bias in face biometrics. The paper also outlines the emerging applications of the proposed approach beyond biometrics, including decision support for epidemiological surveillance such as for COVID-19 pandemics.

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

PURPOSE: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. METHODS: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. RESULTS: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. CONCLUSIONS: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

Antidiabetic activity of Vatairea macrocarpa extract in rats.

In the present paper the anti-diabetic effects of stem-bark extract (ethanol 70%) of Vatairea macrocarpa, a traditional diabetes mellitus treatment widely used in Brazil, are reported. The extract was administered orally at a dose of 250 or 500 mg/kg, for 22 days, to normal and streptozotocin-diabetic rats. In extract treated (500 mg/kg) diabetic rats serum and urinary glucose, urinary urea, food and fluid intake were decreased, while body weight gain was increased, all of which indicate an improvement in diabetic state (p<0.05). No effects of the extract were observed in non-diabetic rats. In extract treated (500 mg/kg) diabetic group HOMA-R (homeostasis model for assessment of insulin resistance) was lower at the end of 22 days, as compared to diabetic non treated control group. Insulin was the reference substance used in the experiments. In an oral glucose tolerance test, the time to reach maximal glycemia was greater in diabetic 500 mg/kg treated group than in control group. These anti-diabetic effects could be related to an improved insulin resistance, although a possible effect on pancreatic B-cell function cannot be excluded. Thus, our data of sub-chronic experiments suggest that long-term use of V. macrocarpa stem-bark extract may be helpful in treating diabetic conditions.