Explainable AI using expressive Boolean formulas

We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions. The Boolean formula defines a rule with tunable complexity (or interpretability), according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, thus providing higher expressivity compared to more rigid rule-based and tree-based approaches. The classifier is trained using native local optimization techniques, efficiently searching the space of feasible formulas. Shallow rules can be determined by fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers, potentially powered by special purpose hardware or quantum devices. We combine the expressivity and efficiency of the native local optimizer with the fast operation of these devices by executing non-local moves that optimize over subtrees of the full Boolean formula. We provide extensive numerical benchmarking results featuring several baselines on well-known public datasets. Based on the results, we find that the native local rule classifier is generally competitive with the other classifiers. The addition of non-local moves achieves similar results with fewer iterations, and therefore using specialized or quantum hardware could lead to a speedup by fast proposal of non-local moves.Comment: 28 pages, 16 figures, 4 table

Development of a tandem affinity phosphoproteomic method with motif selectivity and its application in analysis of signal transduction networks

Phosphorylation is an important post-translational modification that is involved in regulating many signaling pathways. Of particular interest are the growth factor mediated Ras and phosphoinositide 3-kinase (PI3K) signaling pathways which, if misregulated, can contribute to the progression of cancer. Phosphoproteomic methods have been developed to study regulation of signaling pathways; however, due to the low stoichiometry of phosphorylation, understanding these pathways is still a challenge. In this study, we have developed a multi-dimensional method incorporating electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) with tandem IMAC-TiO2 enrichment for subsequent phosphopeptide identification by LC/MS/MS. We applied this method to PDGF-stimulated NIH 3T3 cells to provide over 11,000 unique phosphopeptide identifications. Upon motif analysis, IMAC was found to enrich for basophilic kinase substrates while the subsequent TiO2 step enriched for acidophilic kinase substrates, suggesting that both enrichment methods are necessary to capture the full complement of kinase substrates. Biological functions that were over-represented at each PDGF stimulation time point, together with the phosphorylation dynamics of several phosphopeptides containing known kinase phosphorylation sites illustrate the feasibility of this approach in quantitative phosphoproteomic studies

Polarization aberrations in next-generation giant segmented mirror telescopes (GSMTs) I. Effect on the coronagraphic performance

Next-generation large segmented mirror telescopes are expected to perform direct imaging and characterization of Earth-like rocky planets, which requires contrast limits of $10^{-7}$ to $10^{-8}$ at wavelengths from I to J band. One critical aspect affecting the raw on-sky contrast are polarization aberrations arising from the reflection from the telescope's mirror surfaces and instrument optics. We simulate the polarization aberrations and estimate their effect on the achievable contrast for three next-generation ground-based large segmented mirror telescopes. We performed ray-tracing in Zemax and computed the polarization aberrations and Jones pupil maps using the polarization ray-tracing algorithm. The impact of these aberrations on the contrast is estimated by propagating the Jones pupil maps through a set of idealized coronagraphs using hcipy, a physical optics-based simulation framework. The optical modeling of the giant segmented mirror telescopes (GSMTs) shows that polarization aberrations create significant leakage through a coronagraphic system. The dominant aberration is retardance defocus, which originates from the steep angles on the primary and secondary mirrors. The retardance defocus limits the contrast to $10^{-5}$ to $10^{-4}$ at 1 $\lambda/D$ at visible wavelengths, and $10^{-5}$ to $10^{-6}$ at infrared wavelengths. The simulations also show that the coating plays a major role in determining the strength of the aberrations. Polarization aberrations will need to be considered during the design of high-contrast imaging instruments for the next generation of extremely large telescopes. This can be achieved either through compensation optics, robust coronagraphs, specialized coatings, calibration, and data analysis approaches or by incorporating polarimetry with high-contrast imaging to measure these effects.Comment: 18 pages, 12 figures, Accepted in Astronomy & Astrophysics manuscript no. aa45651-2

Evaluation of Field Sobriety Tests for Identifying Drivers Under the Influence of Cannabis: A Randomized Clinical Trial

IMPORTANCE: With increasing medicinal and recreational cannabis legalization, there is a public health need for effective and unbiased evaluations for determining whether a driver is impaired due to Δ9-tetrahydrocannabinol (THC) exposure. Field sobriety tests (FSTs) are a key component of the gold standard law enforcement officer-based evaluations, yet controlled studies are inconclusive regarding their efficacy in detecting whether a person is under the influence of THC. OBJECTIVE: To examine the classification accuracy of FSTs with respect to cannabis exposure and driving impairment (as determined via a driving simulation). DESIGN, SETTING, AND PARTICIPANTS: This double-blind, placebo-controlled parallel randomized clinical trial was conducted from February 2017 to June 2019 at the Center for Medicinal Cannabis Research, University of California, San Diego. Participants were aged 21 to 55 years and had used cannabis in the past month. Data were analyzed from August 2021 to April 2023. INTERVENTION: Participants were randomized 1:1:1 to placebo (0.02% THC), 5.9% THC cannabis, or 13.4% THC cannabis smoked ad libitum. MAIN OUTCOME AND MEASURES: The primary end point was law enforcement officer determination of FST impairment at 4 time points after smoking. Additional measures included officer estimation as to whether participants were in the THC or placebo group as well as driving simulator data. Officers did not observe driving performance. RESULTS: The study included 184 participants (117 [63.6%] male; mean [SD] age, 30 [8.3] years) who had used cannabis a mean (SD) of 16.7 (9.8) days in the past 30 days; 121 received THC and 63, placebo. Officers classified 98 participants (81.0%) in the THC group and 31 (49.2%) in the placebo group as FST impaired (difference, 31.8 percentage points; 95% CI, 16.4-47.2 percentage points; P \u3c .001) at 70 minutes after smoking. The THC group performed significantly worse than the placebo group on 8 of 27 individual FST components (29.6%) and all FST summary scores. However, the placebo group did not complete a median of 8 (IQR, 5-11) FST components as instructed. Of 128 participants classified as FST impaired, officers suspected 127 (99.2%) as having received THC. Driving simulator performance was significantly associated with results of select FSTs (eg, ≥2 clues on One Leg Stand was associated with impairment on the simulator: odds ratio, 3.09; 95% CI, 1.63-5.88; P \u3c .001). CONCLUSIONS AND RELEVANCE: This randomized clinical trial found that when administered by highly trained officers, FSTs differentiated between individuals receiving THC vs placebo and driving abilities were associated with results of some FSTs. However, the high rate at which the participants receiving placebo failed to adequately perform FSTs and the high frequency that poor FST performance was suspected to be due to THC-related impairment suggest that FSTs, absent other indicators, may be insufficient to denote THC-specific impairment in drivers. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02849587

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility.

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

The Value of Intraoperative Parathyroid Hormone Monitoring in Localized Primary Hyperparathyroidism: A Cost Analysis

Minimally invasive parathyroidectomy (MIP) is the preferred approach to primary hyperparathyroidism (PHPT) when a single adenoma can be localized preoperatively. The added value of intraoperative parathyroid hormone (IOPTH) monitoring remains debated because its ability to prevent failed parathyroidectomy due to unrecognized multiple gland disease (MGD) must be balanced against assay-related costs. We used a decision tree and cost analysis model to examine IOPTH monitoring in localized PHPT. Literature review identified 17 studies involving 4,280 unique patients, permitting estimation of base case costs and probabilities. Sensitivity analyses were performed to evaluate the uncertainty of the assumptions associated with IOPTH monitoring and surgical outcomes. IOPTH cost, MGD rate, and reoperation cost were varied to evaluate potential cost savings from IOPTH. The base case assumption was that in well-localized PHPT, IOPTH monitoring would increase the success rate of MIP from 96.3 to 98.8%. The cost of IOPTH varied with operating room time used. IOPTH reduced overall treatment costs only when total assay-related costs fell below $110 per case. Inaccurate localization and high reoperation cost both independently increased the value of IOPTH monitoring. The IOPTH strategy was cost saving when the rate of unrecognized MGD exceeded 6$12,000 (compared with initial MIP cost of $3733). Setting the positive predictive value of IOPTH at 100% and reducing the false-negative rate to 0% did not substantially alter these findings. Institution-specific factors influence the value of IOPTH. In this model, IOPTH increased the cure rate marginally while incurring approximately 4% additional cost