Target Distribution Research Articles

Progressively Knowledge Distillation via Re-parameterizing Diffusion Reverse Process

Knowledge distillation aims at transferring knowledge from the teacher model to the student one by aligning their distributions. Feature-level distillation often uses L2 distance or its variants as the loss function, based on the assumption that outputs follow normal distributions. This poses a significant challenge when distribution gaps are substantial since this loss function ignores the variance term. To address the problem, we propose to decompose the transfer objective into small parts and optimize it progressively. This process is inspired by diffusion models from which the noise distribution is mapped to the target distribution step by step. However, directly employing diffusion models is impractical in the distillation scenario due to its heavy reverse process. To overcome this challenge, we adopt the structural re-parameterization technique to generate multiple student features to approximate the teacher features sequentially. The multiple student features are combined linearly in inference time without extra cost. We present extensive experiments performed on various transfer scenarios, such as CNN-to-CNN and Transformer-to-CNN, that validate the effectiveness of our approach.

Abstract 5157: Development and evaluation of a high-throughput method for rapid detection of surface antigen expression in fixed cells

Abstract Cell surface proteins are increasingly considered in the development of novel anticancer agents, as demonstrated by recent clinical successes in the field of immuno-oncology and antibody-drug conjugates (ADCs). These conjugates specifically target antigens present on the surface of tumor cells, enabling the delivery of cytotoxic agents to specific tumor sites. During the preclinical development of such compounds, for example, when they are tested on a wide panel of cell lines, it is essential to have a rapid and reliable method for monitoring target expression and distribution. Complementing our 160 Cell line panel ProLiFiler™, we report here the development of a novel method named oncoFLOW-Profiler™ which includes steps of cell fixation, micronic storage, staining, and measurement by flow cytometry. This method is applied in a 96-well format, enabling screening of all cell lines in parallel and analysis of the expression of different targets in just one day. In this proof-of-concept study, we investigated the level of ERBB2 expression across the ProLiFiler panel of 160-cell lines in order to validate this methodology. We first compared the impact of fixation methods and sample storage on knowingly positive and negative control cell lines over a period of several months. ERBB2 measurements from the oncoFLOW-Profiler™ will be analyzed and validated by comparison with various OMICs datasets from the Cancer DataMiner platform (4HF Biotec), including RNA expression, mutational status, and protein expression levels. To determine the expression cutoff of ERBB2 amplified models, results will be analyzed according to gene copy number variation. Finally, the flow cytometry data generated will be investigated for use as a predictor of response to clinically approved ADCs targeting ERBB2: Kadcyla and Enhertu. Here we present a method that we have demonstrated is feasible for investigating cell surface receptor/antigens during preclinical phase of drug development. Our method has enabled us to successfully assess ERBB2 in biological samples and to identify models sensitive to ADCs targeting ERBB2. We will also discuss the results of ongoing experiments analyzing the applicability of this screen to intracellular targets and carbohydrate-based surface antigens, which cannot be analyzed either by transcriptomics or proteomics. Citation Format: Kaja Holtorf, Anne-Lise Peille, Daniel Feger, Vincent Vuaroqueaux, Jan Erik Ehlert, Nadine Obier. Development and evaluation of a high-throughput method for rapid detection of surface antigen expression in fixed cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5157.

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks

Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent on a prior information, resulting in a less stable and adaptable method. Data-driven deep learning-based reconstruction avoids the errors of light propagation models and the reliance on experience and a prior by learning the mapping relationship between the surface light distribution and the target directly from the dataset. However, the acquisition of the training dataset and the training of the network itself are time consuming, and the high dependence of the network performance on the training dataset results in a low generalization ability. The objective of this work is to develop a highly robust reconstruction framework to solve the existing problems. Approach. This paper proposes a physical model constrained neural networks-based reconstruction framework. In the framework, the neural networks are to generate a target distribution from surface measurements, while the physical model is used to calculate the surface light distribution based on this target distribution. The mean square error between the calculated surface light distribution and the surface measurements is then used as a loss function to optimize the neural network. To further reduce the dependence on a priori information, a movable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this movable region and the results are used as the basis for its next movement. Main Results. The performance of the proposed framework is evaluated with a series of simulations and in vivo experiment, including accuracy robustness of different target distributions, noise immunity, depth robustness, and spatial resolution. The results collectively demonstrate that the framework can reconstruct targets with a high accuracy, stability and versatility. Significance. The proposed framework has high accuracy and robustness, as well as good generalizability. Compared with traditional regularization-based reconstruction methods, it eliminates the need to manually delineate feasible regions and adjust regularization parameters. Compared with emerging deep learning assisted methods, it does not require any training dataset, thus saving a lot of time and resources and solving the problem of poor generalization and robustness of deep learning methods. Thus, the framework opens up a new perspective for the reconstruction of three-dimension optical imaging.

Biological New Targets Prediction & ADME Pharmacokinetics Profiling of Newly Synthesized E / Z Isomers of Methyl 2-phenyl-2-(2-phenylhydrazono) Acetate Derivatives

Objective: In order to predict the pharmacokinetic properties, and new potential biological targets, biological targets prediction, and absorption, distribution, metabolism and excretion (ADME) profiling of newly synthesized Entgegen / Zusammen (E / Z isomers) of Methyl 2-phenyl-2-(2-phenylhydrazono) acetate derivatives were carried out. Methods: The prediction of new biological targets, pharmacokinetic properties, membrane permeability, and bioavailability radar properties were carried out by using Swiss Targets Prediction and Swiss ADME tools, respectively. Results: All the eight screened compounds possessed excellent drug-likeness criteria and passed successfully from Pan-assay interference compounds filter. Additionally, all screened compounds fell within the acceptable range in the bioavailability radar, showing excellent descriptors with a slight exceeding of insaturation properties. Compounds displayed elevated permeability across the Blood Brain Barrier, while compounds exhibited highest Human Intestinal Absorption according to the Egan Egg model. Moreover, the screened compounds also exhibited good pharmacokinetic properties. All the compounds exhibited activity against various enzymes, such as lyase, kinase, protease, and Family AG protein-coupled receptor. Conclusion: This conducted study smartly reveals that in-silico based studies were considered to provide robustness towards a rational drug design and development approach; therefore, in this way, it helps for further investigation of drug design and drug discovery steps.

PDMP Monte Carlo methods for piecewise smooth densities

Abstract There has been substantial interest in developing Markov chain Monte Carlo algorithms based on piecewise deterministic Markov processes. However, existing algorithms can only be used if the target distribution of interest is differentiable everywhere. The key to adapting these algorithms so that they can sample from densities with discontinuities is to define appropriate dynamics for the process when it hits a discontinuity. We present a simple condition for the transition of the process at a discontinuity which can be used to extend any existing sampler for smooth densities, and give specific choices for this transition which work with popular algorithms such as the bouncy particle sampler, the coordinate sampler, and the zigzag process. Our theoretical results extend and make rigorous arguments that have been presented previously, for instance constructing samplers for continuous densities restricted to a bounded domain, and we present a version of the zigzag process that can work in such a scenario. Our novel approach to deriving the invariant distribution of a piecewise deterministic Markov process with boundaries may be of independent interest.

Cancelable templates for secure face verification based on deep learning and random projections

Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

Generative Quantum Machine Learning via Denoising Diffusion Probabilistic Models.

Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-quality samples in many computer vision tasks, as well as to incorporate flexible model architectures and a relatively simple training scheme. Quantum generative models, empowered by entanglement and superposition, have brought new insight to learning classical and quantum data. Inspired by the classical counterpart, we propose the quantum denoising diffusion probabilistic model (QuDDPM) to enable efficiently trainable generative learning of quantum data. QuDDPM adopts sufficient layers of circuits to guarantee expressivity, while it introduces multiple intermediate training tasks as interpolation between the target distribution and noise to avoid barren plateau and guarantee efficient training. We provide bounds on the learning error and demonstrate QuDDPM's capability in learning correlated quantum noise model, quantum many-body phases, and topological structure of quantum data. The results provide a paradigm for versatile and efficient quantum generative learning.

Optimizing freeform lenses for extended sources with algorithmic differentiable ray tracing and truncated hierarchical B-splines.

We propose a method for optimizing the geometry of a freeform lens to redirect the light emitted from an extended source into a desired irradiance distribution. We utilize a gradient-based optimization approach with MITSUBA 3, an algorithmic differentiable non-sequential ray tracer that allows us to obtain the gradients of the freeform surface parameters with respect to the produced irradiance distribution. To prevent the optimizer from getting trapped in local minima, we gradually increase the number of degrees of freedom of the surface by using Truncated Hierarchical B-splines (THB-splines) during optimization. The refinement locations are determined by analyzing the gradients of the surface vertices. We first design a freeform using a collimated beam (zero-etendue source) for a complex target distribution to demonstrate the method's effectiveness. Then, we demonstrate the ability of this approach to create a freeform that can project the light of an extended Lambertian source into a prescribed target distribution.

When lattice bases are Markov bases

Sampling the fibre comprising the solutions of a linear inverse problem for count data is an important practical problem. Connectivity of the sampler is guaranteed only if a Markov basis, defining a sufficiently rich variety of sampling directions, is available. Computation of a Markov basis is typically challenging, and the mixing properties of the resulting sampler can be poor. However, for some problems a suitably chosen lattice basis will be a Markov basis. We provide an easily checkable condition for the existence of such a lattice Markov basis, and demonstrate that associated hit-and-run samplers will mix rapidly for uniform target distributions.

Enhancing model‐based acoustic localisation using quantum annealing

AbstractModel‐based acoustic localisation estimates the locations of underwater objects by comparing sensor measurements with model predictions. To obtain high quality predictions, propagation models need to be run for a large set of environmental parameters. However, real‐time Model‐based acoustic localisation estimations using onboard computational resources are often limited. To address this, the authors propose a Quantum annealing (QA) algorithm for enhancing underwater acoustic localisation. A restricted Boltzmann machine (RBM) is trained to predict the probability distribution of underwater targets. Advantage of this approach is that part of the computation is moved to offline‐training. Moreover, the probability distribution can potentially be sampled efficiently using a quantum annealer possibly enabling real‐time accurate target estimations being made onboard.The RBM is applied to a simplified multi‐sensor horizontal localisation problem where a constant and linear acoustic propagation is assumed. Using simulated annealing the authors show that the RBM is able to learn probability distributions that resemble target locations. Preliminary results show that training and sampling the RBM can be done using QA hardware by D‐Wave Systems.However, there remains room for improvement especially in ranging predictions. Further research into possible benefits of QA RBMs is needed to provide theoretical and practical results of a speed‐up.

Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks

At present, the distribution network investment is faced with complicated operation strategies and various new investment elements. It's difficult to evaluate the impact of different investment measures on the final benefits due to the complicated physical modeling and potential coupling correlation, which contributes to that the traditional model-driven decision-making method may not work. This paper proposes a novel deep transfer learning based surrogate-assisted method to address the challenge. The key idea is to develop a surrogate model to replace the input-output correlation between the certain investment measure and its benefits, which can be further reformulated into mixed-integer linear constraints and embedded into the classical investment decision-making model for the target distribution network. As the model is trained through a designed two-stage adaptive transfer learning algorithm, which can extract the similar rules from other distribution networks, the dilemma of insufficient effective samples is alleviated, while a neural network constrained algorithm is designed to linear the surrogate model for simplifying the optimization. Numerical cases prove the effectiveness of the surrogate model on correlation mining and show the superiority of the surrogate-assisted method in achieving the precision investment for target distribution network.

Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifier trained on the limited data available for the target task. We derive the expected loss of an element in the class with respect to the target distribution for a specific generative model, propose a computable approximation of the loss, and demonstrate that the element of the proposed class that minimizes the approximated risk is able to exploit a natural bias–variance trade-off in task space in both simulated and real-data settings. We conclude by discussing further applications, limitations, and potential future research directions.

Pen-Based Swine Oral Fluid Samples Contain Both Environmental and Pig-Derived Targets.

Laboratory methods for detecting specific pathogens in oral fluids are widely reported, but there is little research on the oral fluid sampling process itself. In this study, a fluorescent tracer (diluted red food coloring) was used to test the transfer of a target directly from pigs or indirectly from the environment to pen-based oral fluid samples. Pens of ~30, ~60, and ~125 14-week-old pigs (32 pens/size) on commercial swine farms received one of two treatments: (1) pig exposure, i.e., ~3.5 mL of tracer solution sprayed into the mouth of 10% of the pigs in the pen; (2) environmental exposure, i.e., 20 mL of tracer solution was poured on the floor in the center of the pen. Oral fluids collected one day prior to treatment (baseline fluorescence control) and immediately after treatment were tested for fluorescence. Data were evaluated by receiver operating characteristic (ROC) analysis, with Youden's J statistic used to set a threshold. Pretreatment oral fluid samples with fluorescence responses above the ROC threshold were removed from further analysis (7 of 96 samples). Based on the ROC analyses, oral fluid samples from 78 of 89 pens (87.6%), contained red food coloring, including 43 of 47 (91.5%) pens receiving pig exposure and 35 of 42 (83.3%) pens receiving environmental exposure. Thus, oral fluid samples contain both pig-derived and environmental targets. This methodology provides a safe and quantifiable method to evaluate oral fluid sampling vis-à-vis pen behavior, pen size, sampling protocol, and target distribution in the pen.

Style-Guided Adversarial Teacher for Cross-Domain Object Detection

The teacher–student framework is widely employed for cross-domain object detection. However, it suffers from two problems. One is that large distribution discrepancies will cause critical performance drops. The other is that the samples that deviate from the overall distributions of both domains will greatly mislead the model. To solve these problems, we propose a style-guided adversarial teacher (SGAT) method for domain adaptation. Specifically, on the domain level, we generate target-like images based on source images to effectively narrow the gaps between domains. On the sample level, we denoise samples by estimating the probability density ratio of the ‘target-style’ and target distributions, which could filter out the unrelated samples and highlight the related ones. In this way, we could guarantee reliable samples. With these reliable samples, we learn the domain-invariant features through teacher–student mutual learning and adversarial learning. Extensive experiments verify the effectiveness of our method. In particular, we achieve 52.9% mAP on Clipart1k and 42.7% on Comic2k, which are 6.4% and 5.0% higher than the compared baselines.

Comprehensive Data-Driven Assessment of Non-Kinase Targets of Inhibitors of the Human Kinome.

Protein kinases (PKs) are involved in many intracellular signal transduction pathways through phosphorylation cascades and have become intensely investigated pharmaceutical targets over the past two decades. Inhibition of PKs using small-molecular inhibitors is a premier strategy for the treatment of diseases in different therapeutic areas that are caused by uncontrolled PK-mediated phosphorylation and aberrant signaling. Most PK inhibitors (PKIs) are directed against the ATP cofactor binding site that is largely conserved across the human kinome comprising 518 wild-type PKs (and many mutant forms). Hence, these PKIs often have varying degrees of multi-PK activity (promiscuity) that is also influenced by factors such as single-site mutations in the cofactor binding region, compound binding kinetics, and residence times. The promiscuity of PKIs is often-but not always-critically important for therapeutic efficacy through polypharmacology. Various in vitro and in vivo studies have also indicated that PKIs have the potential of interacting with additional targets other than PKs, and different secondary cellular targets of individual PKIs have been identified on a case-by-case basis. Given the strong interest in PKs as drug targets, a wealth of PKIs from medicinal chemistry and their activity data from many assays and biological screens have become publicly available over the years. On the basis of these data, for the first time, we conducted a systematic search for non-PK targets of PKIs across the human kinome. Starting from a pool of more than 155,000 curated human PKIs, our large-scale analysis confirmed secondary targets from diverse protein classes for 447 PKIs on the basis of high-confidence activity data. These PKIs were active against 390 human PKs, covering all kinase groups of the kinome and 210 non-PK targets, which included other popular pharmaceutical targets as well as currently unclassified proteins. The target distribution and promiscuity of the 447 PKIs were determined, and different interaction profiles with PK and non-PK targets were identified. As a part of our study, the collection of PKIs with activity against non-PK targets and the associated information are made freely available.

Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process.

Domain Generalization by Functional Regression

The problem of domain generalization is to learn, given data from different source distributions, a model that can be expected to generalize well on new target distributions which are only seen through unlabeled samples. In this paper, we study domain generalization as a problem of functional regression. Our concept leads to a new algorithm for learning a linear operator from marginal distributions of inputs to the corresponding conditional distributions of outputs given inputs. Our algorithm allows a source distribution-dependent construction of reproducing kernel Hilbert spaces for prediction, and, satisfies finite sample error bounds for the idealized risk. Numerical implementations and source code are available 1 .

Mapping Colorectal Distribution of Cell-free and Cell-associated HIV Surrogates Following Simulated Anal Intercourse to Aid Rectal Microbicide Development.

Anal sex remains the greatest HIV transmission risk for men who have sex with men and carries substantial population attributable risk among women. Despite a growing array of HIV pre-exposure prophylaxis (PrEP) options, rectal microbicides remain desirable as on demand, non-systemic PrEP. Rectal microbicide product development for PrEP requires understanding the spatiotemporal distribution of HIV infectious elements in the rectosigmoid to optimize formulation development. Outpatient setting with healthy research participants. Six healthy men underwent simulated receptive anal sex with an artificial phallus fitted with a triple lumen catheter in the urethral position. To simulate ejaculation of HIV-infected semen, autologous seminal plasma laden with autologous blood lymphocytes from apheresis labeled with 111Indium-oxine (cell-associated) and 99mTechnetium-sulfur colloid (cell-free) as HIV surrogates were injected into the rectal lumen through the phallic urethra. Spatiotemporal distribution of each radioisotope was assessed using SPECT/CT over eight hours. Analysis of radiolabel distribution used a flexible principal curve algorithm to quantitatively estimate rectal lumen distribution. Cell-free and cell-associated HIV surrogates distributed to a maximal distance of 15 and 16 cm, respectively, from the anorectal junction (∼19 and ∼20 cm from the anal verge), with a maximal signal intensity located 6 and 7 cm, respectively. There were no significant differences in any distribution parameters between cell-free and cell-associated HIV surrogate. Cell-free and cell-associated HIV surrogate distribution in the rectosigmoid can be quantified with spatiotemporal pharmacokinetic methods. These results describe the ideal luminal target distribution to guide rectal microbicide development.

Exploring the molecular mechanism of Xuebifang in the treatment of diabetic peripheral neuropathy based on bioinformatics and network pharmacology.

Xuebifang (XBF), a potent Chinese herbal formula, has been employed in managing diabetic peripheral neuropathy (DPN). Nevertheless, the precise mechanism of its action remains enigmatic. The primary objective of this investigation is to employ a bioinformatics-driven approach combined with network pharmacology to comprehensively explore the therapeutic mechanism of XBF in the context of DPN. The active chemicals and their respective targets of XBF were sourced from the TCMSP and BATMAN databases. Differentially expressed genes (DEGs) related to DPN were obtained from the GEO database. The targets associated with DPN were compiled from the OMIM, GeneCards, and DrugBank databases. The analysis of GO, KEGG pathway enrichment, as well as immuno-infiltration analysis, was conducted using the R language. The investigation focused on the distribution of therapeutic targets of XBF within human organs or cells. Subsequently, molecular docking was employed to evaluate the interactions between potential targets and active compounds of XBF concerning the treatment of DPN. The study successfully identified a total of 122 active compounds and 272 targets associated with XBF. 5 core targets of XBF for DPN were discovered by building PPI network. According to GO and KEGG pathway enrichment analysis, the mechanisms of XBF for DPN could be related to inflammation, immune regulation, and pivotal signalling pathways such as the TNF, TLR, CLR, and NOD-like receptor signalling pathways. These findings were further supported by immune infiltration analysis and localization of immune organs and cells. Moreover, the molecular docking simulations demonstrated a strong binding affinity between the active chemicals and the carefully selected targets. In summary, this study proposes a novel treatment model for XBF in DPN, and it also offers a new perspective for exploring the principles of traditional Chinese medicine (TCM) in the clinical management of DPN.

The impact of SLCO1B1 rs4149056 on LDL-C target achievement after lipid lowering therapy optimization in men and women with familial hypercholesterolemia.

FH women are less likely to receive intensive statin treatment and to obtain a 50% reduction of LDL-C from baseline compared to men with FH. SLCO1B1 rs4149056 might influence statin therapy compliance and thus LDL-C target achievement. Our aim was to evaluate the impact of SLCO1B1 rs4149056 on LDL-C target achievement after lipid lowering therapy (LLT) optimization in men and women with FH. This was a retrospective observational study involving 412 FH subjects with a probable or defined clinical diagnosis of FH who had had genetic analysis from June 2016 to September 2022. Biochemical analysis was obtained from all subjects at baseline and at the last follow-up after LLT optimization. After LLT optimization the percentage of FH subjects on high-intensity statins decreased from the M/SLCO1B1- group to the W/SLCO1B1+ group and the same was found in LDL-C target distribution (for both p for trend < 0.01). The prevalence of SASE fear increased from the M/SLCO1B1- group to the W/SLCO1B1+ group and the same was observed in reported myalgia distribution (for both p for trend < 0.01). Logistic regression analysis showed that the W/SCLO1B1-, M/SCLO1B1+ and W/SCLO1B1+ groups were inversely associated with LDL-C target achievement (p for trend < 0.001) and the W/SCLO1B1+ group exhibited the strongest association. A low prevalence of FH women with SLCO1B1 rs4149056 were on high intensity statins and they rarely achieved LDL-C target. The genotype effect of SLCO1B1 rs4149056 could be more pronounced in FH women than men.