A Six-Year Examination of the Influence of Surgical Technique and Intraoperative Intraglandular Clostridium Botulinum Toxin Application in Salivary Gland Tumor Operations.

Salivary gland tumor operations are associated with complications including facial nerve dysfunction (FND) and salivary fistula. The objective of this study was to investigate the effect of extracapsular dissection (ECD) and the application of Clostridium botulinum toxin (CBT) in contrast to partial and lateral parotidectomy on complications. All salivary gland tumor operations performed within the last 6 years were retrospectively examined.

STAREG: Statistical replicability analysis of high throughput experiments with applications to spatial transcriptomic studies.

Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies.

Scalable lipid droplet microarray fabrication, validation, and screening.

High throughput screening of small molecules and natural products is costly, requiring significant amounts of time, reagents, and operating space. Although microarrays have proven effective in the miniaturization of screening for certain biochemical assays, such as nucleic acid hybridization or antibody binding, they are not widely used for drug discovery in cell culture due to the need for cells to internalize lipophilic drug candidates. Lipid droplet microarrays are a promising solution to this problem as they are capable of delivering lipophilic drugs to cells at dosages comparable to solution delivery.

A powerful approach to identify replicable variants in genome-wide association studies.

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.

Motivation: Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap.

JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies.

Motivation: Replicability is the cornerstone of scientific research. The current statistical method for high-dimensional replicability analysis either cannot control the false discovery rate (FDR) or is too conservative.

Results: We propose a statistical method, JUMP, for the high-dimensional replicability analysis of two studies.

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.

Motivation: Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap.

Proposal for collinear integrated acousto-optic tunable filters featuring ultrawide tuning ranges and multi-band operations.

Integrated optical tunable filters are key components for a wide spectrum of applications, including optical communications and interconnects, spectral analysis, and tunable light sources, among others. Compared with their thermo-optic counterparts, integrated acousto-optic (AO) tunable filters provide a unique approach to achieve superior performance, including ultrawide continuous tuning ranges of hundreds of nm, low power consumption of sub-mW and fast tuning speed of sub-µs. Based on suspended one-dimensional (1D) AO waveguides in the collinear configuration, we propose and theoretically investigate an innovative family of integrated AO tunable filters (AOTFs) on thin-film lithium niobate.

Diastereoselective Synthesis of Chromeno[3,2-]isoxazoles via Brønsted Acid Catalyzed Tandem 1,6-Addition/Double Annulations of -Hydroxyl Propargylic Alcohols.

A Brønsted acid catalyzed tandem process to access densely functionalized chromeno[3,2-]isoxazoles with good to excellent yields and diastereoselectivities was disclosed. The procedure is proposed to involve a 1,6-conjugate addition/electrophilic addition/double annulations process of alkynyl -quinone methides (-AQMs) in situ generated from -hydroxyl propargylic alcohols with nitrones. Mild conditions, good functional group compatibility, easy scale-up of the reaction, and further product transformation demonstrated its potential application.

Statistical analysis of spatially resolved transcriptomic data by incorporating multiomics auxiliary information.

Effective control of false discovery rate is key for multiplicity problems. Here, we consider incorporating informative covariates from external datasets in the multiple testing procedure to boost statistical power while maintaining false discovery rate control. In particular, we focus on the statistical analysis of innovative high-dimensional spatial transcriptomic data while incorporating external multiomics data that provide distinct but complementary information to the detection of spatial expression patterns.

OPTIMAL FALSE DISCOVERY RATE CONTROL FOR LARGE SCALE MULTIPLE TESTING WITH AUXILIARY INFORMATION.

Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power.

Regression analysis of additive hazards model with sparse longitudinal covariates.

Additive hazards model is often used to complement the proportional hazards model in the analysis of failure time data. Statistical inference of additive hazards model with time-dependent longitudinal covariates requires the availability of the whole trajectory of the longitudinal process, which is not realistic in practice. The commonly used last value carried forward approach for intermittently observed longitudinal covariates can induce biased parameter estimation.

The best COVID-19 predictor is recent smell loss: a cross-sectional study.

Background: COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.

Methods: This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness.

On computation of semiparametric maximum likelihood estimators with shape constraints.

Large sample theory of semiparametric models based on maximum likelihood estimation (MLE) with shape constraint on the nonparametric component is well studied. Relatively less attention has been paid to the computational aspect of semiparametric MLE. The computation of semiparametric MLE based on existing approaches such as the expectation-maximization (EM) algorithm can be computationally prohibitive when the missing rate is high.

Intensive Surveillance with Biannual Dynamic Contrast-Enhanced Magnetic Resonance Imaging Downstages Breast Cancer in Mutation Carriers.

Purpose: To establish a cohort of high-risk women undergoing intensive surveillance for breast cancer. We performed dynamic contrast-enhanced MRI every 6 months in conjunction with annual mammography (MG). Eligible participants had a cumulative lifetime breast cancer risk ≥20% and/or tested positive for a pathogenic mutation in a known breast cancer susceptibility gene.

Efficacy of Anti-HER2 Agents in Combination With Adjuvant or Neoadjuvant Chemotherapy for Early and Locally Advanced HER2-Positive Breast Cancer Patients: A Network Meta-Analysis.

Background: Several (neo)adjuvant treatments for patients with HER2-positive breast cancer have been compared in different randomized clinical trials. Since it is not feasible to conduct adequate pairwise comparative trials of all these therapeutic options, network meta-analysis offers an opportunity for more detailed inference for evidence-based therapy.

Methods: Phase II/III randomized clinical trials comparing two or more different (neo)adjuvant treatments for HER2-positive breast cancer patients were included.

False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing.

Motivation: Next generation sequencing technologies have enabled the study of the human microbiome through direct sequencing of microbial DNA, resulting in an enormous amount of microbiome sequencing data. One unique characteristic of microbiome data is the phylogenetic tree that relates all the bacterial species. Closely related bacterial species have a tendency to exhibit a similar relationship with the environment or disease.

Clinical Evaluation of Cisplatin Sensitivity of Germline Polymorphisms in Neoadjuvant Chemotherapy for Urothelial Cancer.

Background: Level 1 evidence has demonstrated increased overall survival with cisplatin-based neoadjuvant chemotherapy for patients with muscle-invasive urothelial cancer. Usage remains low, however, in part because neoadjuvant chemotherapy will not be effective for every patient. To identify the patients most likely to benefit, we evaluated germline pharmacogenomic markers for association with neoadjuvant chemotherapy sensitivity in 2 large cohorts of patients with urothelial cancer.

Assessing agreement with multiple raters on correlated kappa statistics.

In clinical studies, it is often of interest to see the diagnostic agreement among clinicians on certain symptoms. Previous work has focused on the agreement between two clinicians under two different conditions or the agreement among multiple clinicians under one condition. Few have discussed the agreement study with a design where multiple clinicians examine the same group of patients under two different conditions.

Regression analysis of sparse asynchronous longitudinal data.

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data.

Serum Aβ is predictive for short-term neurological deficits after acute ischemic stroke.

Mounting evidence suggests that ischemic stroke (IS) is associated with Alzheimer's disease (AD). IS and vascular risk factors increase the risk for AD. However, whether AD pathologies exist in IS and the effects of these pathologies on stroke remain unknown.