Debiasing the Conversion Rate Prediction Model in the Presence of Delayed Implicit Feedback.

The recommender system (RS) has been widely adopted in many applications, including online advertisements. Predicting the conversion rate (CVR) can help in evaluating the effects of advertisements on users and capturing users' features, playing an important role in RS. In real-world scenarios, implicit rather than explicit feedback data are more abundant. Thus, directly training the RS with collected data may lead to suboptimal performance due to selection bias inherited from the nature of implicit feedback. Methods such as reweighting have been proposed to tackle selection bias; however, these methods omit delayed feedback, which often occurs due to limited observation times. We propose a novel likelihood approach combining the assumed parametric model for delayed feedback and the reweighting method to address selection bias. Specifically, the proposed methods minimize the likelihood-based loss using the multi-task learning method. The proposed methods are evaluated on the real-world Coat and Yahoo datasets. The proposed methods improve the AUC by 5.7% on Coat and 3.7% on Yahoo compared with the best baseline models. The proposed methods successfully debias the CVR prediction model in the presence of delayed implicit feedback.

Catalytic Enantioselective Synthesis of Planar Chiral Pillar[5]arenes via Asymmetric Sonogashira Coupling.

As a novel type of macrocycles with attractive planar chirality, pillar[5]arenes have gained increasing research interest over the past decades, enabling their widespread applications in diverse fields such as porous materials, molecular machines, and chiral luminescence materials. However, the catalytic methodology towards the enantioselective synthesis of planar chiral pillar[5]arenes remains elusive. Here we report a novel method for the enantioselective synthesis of planar chiral pillar[5]arenes via asymmetric Sonogashira coupling, giving access to a wide range of highly functionalized planar chiral pillar[5]arenes, including both homo- and hetero-rimmed ones, with excellent enantioselectivities. Attractively, the resultant planar chiral pillar[5]arenes show great potential for widespread use in many areas such as chiral luminescent materials. This work not only enables the successful synthesis of planar chiral pillar[5]arenes with abundant structural and functional diversity as key building blocks for practical applications but also enriches the asymmetric cross-coupling methodologies in organic synthetic chemistry.

Disentangling the Inverse LDL-C-hemorrhagic Stroke Association in Chinese Adults with Hypertension: Findings from the Chinese Multi-Provincial Cohort Study.

Why lower low-density lipoprotein cholesterol (LDL-C) was associated with a decreased atherosclerotic cardiovascular disease (ASCVD) risk but an increased hemorrhagic stroke (HS) risk in hypertensive adults remains unclear. We examined whether the inverse LDL-C-HS association partly arises from its effect on ASCVD. We estimated separable effects of LDL-C on HS outside (i.e., separable direct effect) or only through its effect on ASCVD (i.e., separable indirect effect) in hypertensive adults from the Chinese Multi-provincial Cohort Study. We quantified such effects using numbers needed to treat (NNT) to prevent or cause an extra HS based on the restricted mean event-free time till a 25-year follow-up. LDL-C $<$ 70 mg/dL was not associated with an increased HS risk compared to LDL-C $\ge$ 70 mg/dL regarding total and separable direct effects. However, a small separable indirect effect (i.e., NNT to harm: 9722 participants) was noted and validated via a series of sensitivity analyses. Moreover, modified effects were observed, particularly in the 35-49-year age group, men, and those with SBP $\ge$ 140 mm Hg. These results suggest the inverse LDL-C-HS association in hypertensive adults is partly due to its effect on ASCVD. A better understanding of such associations would provide more enlightening into stroke prevention.

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

Electron correlation and relativistic effects on the electronic properties of a plutonium and americium mixed oxide (PuAmO4): from single-particle approximation to dynamical mean-field theory.

First-principles calculations were performed on a plutonium and americium mixed oxide (PuAmO4), aiming at revealing the effects of electron correlation, Pu/Am 5f-conduction electrons' hybridization, and relativity on its electronic properties. The many-body calculation suggests that the spin-orbit-coupling (SOC)-splitting of j = 5/2 and j = 7/2 manifolds are both in the weakly and moderately correlated states, respectively, implying that the jj coupling scheme is more appropriate for Pu/Am 5f electrons. The density of states, 5f occupation numbers, and Green's functions all suggest that both Pu and Am 5f electrons exhibit the coexistence of the localized and delocalized states. The admixture of 5fn atomic configurations, Pu/Am 5f-conduction electrons' hybridization, and dual characteristics of 5f electrons yield average occupation numbers of 5f electrons n5f = 4.78 and 5.86 for Pu and Am ions, respectively. Within the DFT+DMFT calculation, the weighted-summation-derived occupation numbers in terms of 5f4/5f5/5f6 and 5f5/5f6 configurations for Pu and Am 5f electrons, respectively, are in reasonable agreement with those of other DFT-based calculations.

A bibliometric and thematic analysis of the trends in the research on ginkgo biloba extract from 1985 to 2022.

Background: Ginkgo biloba extract (GBE), a complementary and alternative medicine, has been widely used for disorders such as brain infarction, dementia, and coronary heart disease, in recent decades. Given its widespread clinical use, GBE has always been a vital research topic. However, there are no bibliometric analyses on this topic; furthermore, published reviews of GBE focus only on a specific research field or lack scientific and systematic evaluation. This study combined bibliometrics with thematic reviews by visual analysis to identify the current status of GBE research and to better identify research hotspots and trends in the past 40 years to understand future developments in basic and clinical research. Methods: Articles and reviews on GBE were retrieved by topic from the Web of Science Core Collection from inception to 2022.12.01. Countries, institutions, authors, journals, references, and keywords in the field were visually analyzed using CiteSpace, Scimago Graphica, and VOSviewer software; then, these visualization results for references and keywords were clarified in detail by thematic reviews in subdivisions of the fields. Results: In total, 2015 publications were included. The GBE-related literature has high volumes of publications and citations. The majority of literature is from China, and the USA cooperates most closely with other countries. In GBE research, Christen Yves is the most cited author, Phytotherapy Research is the most prolific journal, and the Journal of Ethnopharmacology is the most co-cited journal. Through a comprehensive analysis of keywords, references, and reviews, the quality of the meta-analysis of randomized controlled clinical trials of GBE in treating dementia was evaluated by the Risk of Bias in Systematic Reviews scale (ROBIS). Current research on GBE focuses on its pharmacological mechanisms, and neuroprotective application in diseases such as Alzheimer's disease, and glaucoma. Randomized controlled trials are the current research hotspot. Conclusion: Research on GBE is flourishing; using bibliometric and thematic analysis, we identified its hotspots and trends. The pharmacological mechanisms and clinical applications of GBE are the focus of present and likely future research.

Exploring the anatomical factors influencing testes elasticity via ultrasound shear wave elastography: Preliminary results.

PURPOSE: The aim of this study is to evaluate the anatomical factors influencing elasticity values of normal testicular parenchyma using shear wave elastography (SWE). METHODS: This study examined 68 healthy male volunteers (117 testes in which standard transverse axis ultrasonography views could be obtained) via conventional scrotal ultrasonography and SWE. Both the mean (EMean) and standard deviation (ESD) elasticity values were acquired. RESULTS: In the standard transverse axis view of the rete testis at the mid-lateral edge of the testes, the EMean values in 2mm the testicular parenchyma from the rete testis and the testicular capsule at the same level as the rete testis were all significantly larger than in the central zone (P<0.001, P<0.001, respectively). The EMean value in the testicular parenchyma 2mm from the testicular capsule on the line formed approximately 45° below the horizontal line of the rete testis was significantly larger than in the rete testis approximately 45° above the horizontal line (P<0.001). In two standard transverse axis views, the ESD values in other regions were significantly larger than those in the central zones (all P<0.001). Also, the EMean values in the transmediastinal arteries were larger than those of the surrounding normal testicular parenchyma (P<0.001). CONCLUSION: Based on SWE, factors including the testicular capsule, the density of testicular fibrous septa, the depth of the Q-Box™, and the transmediastinal artery may influence the testes elasticity measurement.

Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy.

OBJECTIVE: Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data. METHODS: Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes. RESULTS: The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level. SIGNIFICANCE: This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.

Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline.

The National Alzheimer's Coordinating Center Uniform Data Set includes test results from a battery of cognitive exams. Motivated by the need to model the cognitive ability of low-performing patients we create a composite score from ten tests and propose to model this score using a partially linear quantile regression model for longitudinal studies with non-ignorable dropouts. Quantile regression allows for modeling non-central tendencies. The partially linear model accommodates nonlinear relationships between some of the covariates and cognitive ability. The data set includes patients that leave the study prior to the conclusion. Ignoring such dropouts will result in biased estimates if the probability of dropout depends on the response. To handle this challenge, we propose a weighted quantile regression estimator where the weights are inversely proportional to the estimated probability a subject remains in the study. We prove that this weighted estimator is a consistent and efficient estimator of both linear and nonlinear effects.

Modeling Posttreatment Prognosis of Skin Lesions in Patients With Psoriasis in China.

Importance: Understanding the posttreatment prognosis of skin lesions in patients with psoriasis is essential for improving patients' treatment satisfaction. Objectives: To model the prognosis of skin lesions for patients with psoriasis after 3 types of therapy. Design, Setting, and Participants: This prospective cohort study included patients with psoriasis who visited a dermatologist and were enrolled in the platform of the Psoriasis Standardized Diagnosis and Treatment Center in China from August 2020 to December 2021. Interventions: Biologic, traditional, and systemic therapy for psoriasis. Main Outcomes and Measures: Skin lesions were measured by the Investigator's Global Assessment (IGA) scale subsumed into 4 stages of severity (IGA 0/1, IGA 2, IGA 3, and IGA 4), with higher scores indicating higher severity. The matching method was used to balance baseline covariates between patients receiving each of the 3 treatments. Transition probabilities from IGA scores at baseline to 0 to 1 month and 1 to 12 months were estimated. Results: A total of 8767 patients were included in the final analysis (median age, 38.6 years [IQR, 28.7-52.8 years]; 5809 [66.3%] male). Across the 3 therapies, as the follow-up duration increased, the probability of improvement transition into a less severe IGA stage (from IGA 4 to IGA 0/1) increased from 0.19 (95% CI, 0.18-0.21) in 0 to 1 month to 0.36 (95% CI, 0.34-0.37) in 1 to 12 months. Biologic therapy was associated with greater improvement transitions for severe conditions, with transition probabilities from IGA 4 to IGA 0/1 increasing by 0.06 (95% CI, 0.02-0.09) vs traditional therapy and by 0.06 (95% CI, 0.03-0.09) vs systemic therapy in 0 to 1 month and by 0.08 (95% CI, 0.04-0.12) vs traditional therapy and 0.11 (95% CI, 0.07-0.14) vs systemic therapy in 1 to 12 months. Conclusions and Relevance: This cohort study modeling psoriasis prognosis provided a complete prognosis of skin lesions, and biologic therapy was associated with improved prognosis of moderate to severe psoriasis compared with traditional and systemic therapies. The study provides insight on using transition diagrams to assess psoriasis prognosis and to communicate with patients in clinical practice.

Defining estimands in clinical trials: A unified procedure.

The ICH E9 (R1) addendum proposes five strategies to define estimands by addressing intercurrent events. However, mathematical forms of these targeted quantities are lacking, which might lead to discordance between statisticians who estimate these quantities and clinicians, drug sponsors, and regulators who interpret them. To improve the concordance, we provide a unified four-step procedure for constructing the mathematical estimands. We apply the procedure for each strategy to derive the mathematical estimands and compare the five strategies in practical interpretations, data collection, and analytical methods. Finally, we show that the procedure can help ease tasks of defining estimands in settings with multiple types of intercurrent events using two real clinical trials.

Long-term exposure to ambient air pollution and cognitive function in older US adults: The Multi-Ethnic Study of Atherosclerosis.

Air pollution effects on cognitive function have been increasingly recognized. Little is known about the impact of different sources of fine particulate (PM2.5). We aim to evaluate the associations between long-term air pollution exposure, including source-specific components in PM2.5, and cognition in older adults. Methods: Cognitive assessment, including the Cognitive Abilities Screening Instrument (CASI), Digit Symbol Coding (DSC), and Digit Span (DS), was completed in 4392 older participants in the United States during 2010-2012. Residence-specific air pollution exposures (i.e., oxides of nitrogen [NO2/NOx], PM2.5 and its components: elemental carbon [EC], organic carbon [OC], sulfur [S], and silicon [Si]) were estimated by geo-statistical models. Linear and logistic regression models were used to estimate the associations between each air pollutants metric and cognitive function. Results: An interquartile range (IQR) increase in EC (0.8 µg/m3) and Si (23.1 ng/m3) was associated with -1.27 (95% confidence interval [CI]: -0.09, -2.45) and -0.88 (95% CI: -0.21, -1.54) lower CASI scores in global cognitive function. For each IQR increase in Si, the odds of low cognitive function (LCF) across domains was 1.29 times higher (95% CI: 1.04, 1.60). For other tests, NO X was associated with slower processing speed (DSC: -2.01, 95% CI: -3.50, -0.52) and worse working memory (total DS: -0.4, 95% CI: -0.78, -0.01). No associations were found for PM2.5 and two PM2.5 components (OC and S) with any cognitive function outcomes. Conclusion: Higher exposure to traffic-related air pollutants including both tailpipe (EC and NO x ) and non-tailpipe (Si) species were associated with lower cognitive function in older adults.

An unusual autocatalysis with an air-stable Pd complex to promote enantioselective synthesis of Si-stereogenic enynes.

Given the powerful potential of chiral-at-silicon chemistry, enantioselective synthesis of Si-stereogenic centers has attracted substantial research interest in recent years. However, the catalytic asymmetric synthesis of Si-stereogenic organosilicon compounds remains an appealing venture and is a challenging subject because of the difficulty in achieving high reactivity and stereoselectivity for "silicon-center" transformations. Herein, we disclose a highly enantioselective palladium-catalyzed hydrosilylation of 1,3-diynes with dihydrosilanes, which enables the facile preparation of Si-stereogenic enynes and an enyne-linked chiral polymer (polyenyne) in good yields and excellent ees (up to >99%) by desymmetrization. The unusual stereoselectivity in this reaction is achieved by precisely controlling the steric hindrance and electronic effect of the newly developed chiral ligands, resulting in a wide range of chiral silanes and a Si-containing polymer bearing a Si-stereogenic center which is otherwise difficult to access. The key to the high enantioselectivity relies on catalyst aggregation-induced non-covalent interaction, which exerts a remarkably positive influence on the Si-H bond activation and enhancement of enantioselectivity, in which the palladium/P-ligand complex was proved to be air-stable and moisture-insensitive in this reaction.

Improving efficiency of inference in clinical trials with external control data.

Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.

Influenza's Plummeting During the COVID-19 Pandemic: The Roles of Mask-Wearing, Mobility Change, and SARS-CoV-2 Interference.

Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza's unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%-35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%-6.5%) and the domestic community (1.6%-2.8%). In 2020-2021, the mask-wearing intervention alone could decline percent positivity by 13.3-19.8. The mobility change alone could reduce percent positivity by 5.2-14.0, of which 79.8%-98.2% were attributed to the deflected international travel. Only in 2019-2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4-14.4) and 10.2 (7.2-13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.

A screening method for ultra-high dimensional features with overlapped partition structures.

Ultra-high dimensional data, such as gene and neuroimaging data, are becoming increasingly important in biomedical science. Identifying important biomarkers from the huge number of features can help us gain better insights into further researches. Variable screening is an efficient tool to achieve this goal under the large scale cases, which reduces the dimension of features into a moderate size by removing the major part of inactive ones. Developing novel variable screening methods for high-dimensional features with group structures is challenging, especially under the overlapped cases. For example, the huge-scaled genes usually can be partitioned into hundreds of pathways according to background knowledge. One primary characteristic for this type of data is that many genes may appear across more than one pathway, which means that different pathways are overlapped. However, existing variable screening methods only could deal with disjoint group structure cases. To fill this gap, we propose a novel variable screening method for the generalized linear model by incorporating overlapped partition structures with theoretical guarantee. Besides the sure screening property, we also test the performance of the proposed method through a series of numerical studies and apply it to statistical analysis of a breast cancer data.

An Explainable Statistical Method for Seizure Prediction Using Brain Functional Connectivity from EEG.

Background: Epilepsy is a group of chronic neurological disorders characterized by recurrent and abrupt seizures. The accurate prediction of seizures can reduce the burdens of this disorder. Now, existing studies use brain network features to classify patients' preictal or interictal states, enabling seizure prediction. However, most predicting methods are based on deep learning techniques, which have weak interpretability and high computational complexity. To address these issues, in this study, we proposed a novel two-stage statistical method that is interpretable and easy to compute. Methods: We used two datasets to evaluate the performance of the proposed method, including the well-known public dataset CHB-MIT. In the first stage, we estimated the dynamic brain functional connectivity network for each epoch. Then, in the second stage, we used the derived network predictor for seizure prediction. Results: We illustrated the results of our method in seizure prediction in two datasets separately. For the FH-PKU dataset, our approach achieved an AUC value of 0.963, a prediction sensitivity of 93.1%, and a false discovery rate of 7.7%. For the CHB-MIT dataset, our approach achieved an AUC value of 0.940, a prediction sensitivity of 93.0%, and a false discovery rate of 11.1%, outperforming existing state-of-the-art methods. Significance. This study proposed an explainable statistical method, which can estimate the brain network using the scalp EEG method and use the net-work predictor to predict epileptic seizures. Availability and Implementation. R Source code is available at https://github.com/HaoChen1994/Seizure-Prediction.

Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions.

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.