Camouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se. Here, we show that when subjects break camouflage, they can also localize the camouflaged target accurately, even though they had received no specific training in localizing the target. The localization was significantly accurate when the subjects viewed the scene as briefly as 50 ms, but more so when the subjects were able to freely view the scenes. The accuracy and precision of target localization by expert subjects in the camouflage-breaking task were statistically indistinguishable from the accuracy and precision of target localization by naive subjects during a conventional visual search where the target ‘pops out’, i.e., is readily visible to the untrained eye. Together, these results indicate that when expert camouflage-breakers detect a camouflaged target, they can also localize it accurately.

Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects’ estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output.

Background We have investigated the efficacy of a new strategy to limit pathological retinal neovascularization (RNV) during ischemic retinopathy by targeting the cholesterol metabolizing enzyme acyl-coenzyme A: cholesterol transferase 1 (ACAT1). Dyslipidemia and cholesterol accumulation have been strongly implicated in promoting subretinal NV. However, little is known about the role of cholesterol metabolism in RNV. Here, we tested the effects of inhibiting ACAT1 on pathological RNV in the mouse model of oxygen-induced retinopathy (OIR). Methods In vivo studies used knockout mice that lack the receptor for LDL cholesterol (LDLR−/−) and wild-type mice. The wild-type mice were treated with a specific inhibitor of ACAT1, K604 (10 mg/kg, i.p) or vehicle (PBS) during OIR. In vitro studies used human microglia exposed to oxygen–glucose deprivation (OGD) and treated with the ACAT1 inhibitor (1 μM) or PBS. Results Analysis of OIR retinas showed that increased expression of inflammatory mediators and pathological RNV were associated with significant increases in expression of the LDLR, increased accumulation of neutral lipids, and formation of toxic levels of cholesterol ester (CE). Deletion of the LDLR completely blocked OIR-induced RNV and significantly reduced the AVA. The OIR-induced increase in CE formation was accompanied by significant increases in expression of ACAT1, VEGF and inflammatory factors (TREM1 and MCSF) (p < 0.05). ACAT1 was co-localized with TREM1, MCSF, and macrophage/microglia makers (F4/80 and Iba1) in areas of RNV. Treatment with K604 prevented retinal accumulation of neutral lipids and CE formation, inhibited RNV, and decreased the AVA as compared to controls (p < 0.05). The treatment also blocked upregulation of LDLR, ACAT1, TREM1, MCSF, and inflammatory cytokines but did not alter VEGF expression. K604 treatment of microglia cells also blocked the effects of OGD in increasing expression of ACAT1, TREM1, and MCSF without altering VEGF expression. Conclusions OIR-induced RNV is closely associated with increases in lipid accumulation and CE formation along with increased expression of LDLR, ACAT1, TREM1, and MCSF. Inhibiting ACAT1 blocked these effects and limited RNV independently of alterations in VEGF expression. This pathway offers a novel strategy to limit vascular injury during ischemic retinopathy. Graphical Abstract

When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using ‘mental shortcuts’, or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment (‘anchored position’) based on available external information (‘anchoring information’) and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography. We provided practicing radiologists (N = 27 across two studies) a random number that we told them was the estimate of a previous radiologist of the probability that a mammogram they were about to see was positive for breast cancer. We then showed them the actual mammogram. We found that the radiologists’ own estimates of cancer in the mammogram reflected the random information they were provided and ignored the actual evidence in the mammogram. However, when the heuristic information was not provided, the same radiologists detected breast cancer in the same set of mammograms highly accurately, indicating that the effect was solely attributable to the availability of heuristic information. Thus, the effects of the AAA heuristic can sometimes be so strong as to override the actual clinical evidence in diagnostic tasks.

Background: We have investigated the efficacy of a new strategy to limit pathological retinal neovascularization (RNV) during ischemic retinopathy. Our previous studies in a mouse model of oxygen-induced retinopathy (OIR) showed that blockade of a receptor of the immunoglobulin superfamily, triggering receptor expressed on myeloid cells 1 (TREM1) significantly inhibited RNV and reduced expansion of the avascular area (AVA). Here we investigated the role of the cholesterol metabolizing enzyme acyl-coenzyme A: cholesterol transferase 1 (ACAT1) in this process.Methods: In vivo studies used the mouse model of OIR using LDLR-/- mice and wild-type mice treated with a specific inhibitor of ACAT1 (10 mg/Kg, i.p) or vehicle (PBS). In vitro studies used human THP1 macrophages maintained in hypoxia (1% O2) or normoxia (21% O2) for 16 hrs and treated with the ACAT1 inhibitor (10μg/ml) or PBS.Results: Analysis of OIR retinas showed that increased expression of inflammatory mediators and pathological RNV were associated with significant increases in expression of the LDL receptor (LDLR), increased accumulation of neutral lipids, and formation of toxic levels of cholesterol ester (CE). Deletion of the LDLR completely blocked OIR-induced RNV and significantly reduced the AVA. The OIR-induced increase in CE formation was accompanied by significant increases in expression of ACAT1, VEGF and inflammatory factors (TREM1 and MCSF) (p<0.05). ACAT1 was co-localized with TREM1, MCSF, and macrophage/microglia makers (F4/80 and Iba1) in areas of RNV. Treatment with K604 prevented retinal accumulation of neutral lipids and CE formation, inhibited RNV, and decreased the AVA as compared to controls (p<0.05). The treatment also blocked upregulation of LDLR, ACAT1, TREM1, MCSF, and inflammatory cytokines but did not alter VEGF expression. K604 treatment of THP1 macrophages also blocked the effects of hypoxia in increasing expression of ACAT1, TREM1, and MCSF without altering VEGF expression. Conclusions: OIR-induced RNV is closely associated with increases in lipid accumulation and CE formation along with increased expression of LDLR, ACAT1, TREM1, and MCSF. Inhibiting ACAT1 blocked these effects and limited RNV independently of alterations in VEGF expression. This pathway offers a novel strategy to limit vascular injury during ischemic retinopathy.