Multivariate Setting Research Articles

Surgical Treatment and Outcome of Ovarian Cancer Patients With Liver Metastases: Experience of a Tertiary Hepatic and Peritoneal Surface Malignancy Center.

The aim of this study was to describe and evaluate the patterns, perioperative outcomes, and survival rates of patients subjected to hepatic resections for ovarian-derived liver metastasis as part of cytoreductive surgery with or without hyperthermic intraperitoneal chemotherapy (HIPEC). Furthermore, we investigated two subgroups of tumor patterns: hematogenous liver metastasis and infiltrative liver metastatic spread. A retrospective study was conducted. Patients from a University Tertiary Hepatic and Peritoneal Surface Malignancy Center with primary or recurrent ovarian cancer, who underwent liver resection as part of cytoreductive surgery between January 1992 and December 2022, were included. Data from 35 patients were analyzed. Both median overall survival (OS) and disease-specific survival (DSS) were 24.97 months. In a multivariate setting, the combined effect of age, peritoneal carcinomatosis index, body mass index, hematogenous liver metastasis vs. infiltrative spread types, and HIPEC (HR=0.2372; 95%CI=0.0719-0.7823; p=0.0181) over OS was tested. Survival analysis revealed no differences between the two metastatic spread types (OS: p=0.9720; DSS: p=0.9610). Younger age (p=0.0301), splenectomy (p=0.0320), lesser omentectomy (p=0.0178), and right upper quadrant peritonectomy (p=0.0373) were more characteristic for those patients with infiltrative liver metastatic spread. Complete cytoreductive surgery, including hepatic resection is a feasible approach with or without additional HIPEC, which may provide survival benefit for patients with advanced and/or recurrent ovarian cancer. If metastatic and infiltrative liver involvement is suspected, liver-specific imaging is recommended.

PM2.5 Is Insufficient to Explain Personal PAH Exposure.

To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

Exchange rate prediction research based on LSTM-ELM hybrid model

The fluctuation of exchange rates holds paramount importance for a country's economic and trade activities. Due to the non-stationary and nonlinear structural characteristics of exchange rate time series, accurately predicting exchange rate movements is a challenging task. Single machine learning models often exhibit lower precision in exchange rate prediction compared to combined machine learning models. Hence, employing a combined model approach aims to enhance the predictive performance of exchange rate models. Both Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) exhibit intricate structures, making their direct integration challenging. To address this issue, an innovative weighted approach is adopted in this study, combining LSTM and ELM models and further refining the combination weights using an improved Marine Predators Algorithm. This paper encompasses both univariate and multivariate prediction scenarios, employing two distinct allocation strategies for training and testing datasets. This is done to investigate the influence of different dataset allocations on exchange rate prediction. Finally, the proposed LSTM-ELM weighted combination exchange rate prediction model is compared with SVM, Random Forest, ELM, LSTM, and LSTM-ELM average combination models. Experimental results demonstrate that the LSTM-ELM weighted combination exchange rate prediction model outperforms the others in both univariate and multivariate prediction settings, yielding higher predictive accuracy and superior fitting performance. Consequently, the LSTM-ELM weighted combination prediction model proves to be effective in exchange rate forecasting.

A Python package for the local multiscale analysis of spatial point processes (LomPy)

The consideration of multiscale characteristics has emerged as a popular component of geospatial data analysis and modeling. However, the practical implementation of such analysis tasks involves time-consuming and computationally intensive processes that require the integration of knowledge and methods from different disciplines (e.g., quantitative geography, signal processing, and natural sciences) and in which large amounts of data have to be processed. Yet, to date, there is few open-source software that enables an efficient and transparent computational workflow. This paper introduces a Python package for the local multiscale analysis of spatial point processes (LomPy). LomPy is specifically designed for processing and analyzing data that is either irregularly spaced or has large data holes over the spatial territory: a common and methodologically challenging property of geoprocessing operations. The first main function of the package computes the multiresolution quantities, while the second applies them to the extraction of fractal and multifractal features at arbitrarily “local” spatial scales. The third function extends the univariate analysis option to multivariate settings. Powerful tools for regression modeling, density estimation, and visualization are also provided. It should be emphasized that the package is efficient on both vector and raster data structures, ensuring a wide range of applicability in urban data science and beyond.

Multivariate Generalized Linear Mixed Models for Count Data

Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of responses, measuring the correlation between them through random effects that follows a multivariate normal distribution. This model is based on a GLMM with a random intercept and the estimation process remains the same as a standard GLMM with random effects integrated out via Laplace approximation. We efficiently implemented this model through the TMB package available in R. We used Poisson, negative binomial (NB), and COM-Poisson distributions. To assess the estimator properties, we conducted a simulation study considering four different sample sizes and three different correlation values for each distribution. We achieved unbiased and consistent estimators for Poisson and NB distributions; for COM-Poisson estimators were consistent, but biased, especially for dispersion, variance, and correlation parameter estimators. These models were applied to two datasets. The first concerns a sample from 30 different sites collected in Australia where the number of times each one of the 41 different ant species was registered; which results in an impressive 820 variance-covariance and 41 dispersion parameters are estimated simultaneously, let alone the regression parameters. The second is from the Australia Health Survey with 5 response variables and 5190 respondents. These datasets can be considered overdispersed by the generalized dispersion index. The COM-Poisson model overcame the other two competitors considering three goodness-of-fit indexes, AIC, BIC, and maximized log-likelihood values. As a result, it estimated parameters with smaller standard errors and a greater number of significant correlation coefficients. Therefore, the proposed model is capable of dealing with multivariate count data, either under- equi- or overdispersed responses, and measuring any kind of correlation between them taking into account the effects of the covariates.

An ultra-short-term forecasting method for multivariate loads of user-level integrated energy systems in a microscopic perspective: based on multi-energy spatio-temporal coupling and dual-attention mechanism

An ultra-short-term multivariate load forecasting method under a microscopic perspective is proposed to address the characteristics of user-level integrated energy systems (UIES), which are small in scale and have large load fluctuations. Firstly, the spatio-temporal correlation of users’ energy use behavior within the UIES is analyzed, and a multivariate load input feature set in the form of a class image is constructed based on the various types of load units. Secondly, in order to maintain the feature independence and temporal integrity of each load during the feature extraction process, a deep neural network architecture with spatio-temporal coupling characteristics is designed. Among them, the multi-channel parallel convolutional neural network (MCNN) performs independent spatial feature extraction of the 2D load component pixel images at each moment in time, and feature fusion of various types of load features in high dimensional space. A bidirectional long short-term memory network (BiLSTM) is used as a feature sharing layer to perform temporal feature extraction on the fused load sequences. In addition, a spatial attention layer and a temporal attention layer are designed in this paper for the original input load pixel images and the fused load sequences, respectively, so that the model can better capture the important information. Finally, a multi-task learning approach based on the hard sharing mechanism achieves joint prediction of each load. The measured load data of a UIES is analyzed as an example to verify the superiority of the method proposed in this paper.

Multivariate nearest‐neighbors Gaussian processes with random covariance matrices

AbstractWe propose a non‐stationary spatial model based on a normal‐inverse‐Wishart framework, conditioning on a set of nearest‐neighbors. The model, called nearest‐neighbor Gaussian process with random covariance matrices is developed for both univariate and multivariate spatial settings and allows for fully flexible covariance structures that impose no stationarity or isotropic restrictions. In addition, the model can handle duplicate observations and missing data. We consider an approach based on integrating out the spatial random effects that allows fast inference for the model parameters. We also consider a full hierarchical approach that leverages the sparse structures induced by the model to perform fast Monte Carlo computations. Strong computational efficiency is achieved by leveraging the adaptive localized structure of the model that allows for a high level of parallelization. We illustrate the performance of the model with univariate and bivariate simulations, as well as with observations from two stationary satellites consisting of albedo measurements.

A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

PGC1α as a downstream effector of KDM5B promotes the progression of androgen receptor-positive and androgen receptor-negative prostate cancers.

PPARγ coactivator-1α (PGC1α), as a co-activator, is known to optimize the action of several transcription factors, including androgen receptor (AR). However, the precise functions of PGC1α in prostate cancer, particularly those via the non-AR pathways, remain poorly understood. Meanwhile, our bioinformatics search suggested that PGC1α could be a direct downstream target of lysine-specific demethylase 5B (KDM5B/JARID1B/PLU1). We herein aimed to investigate how PGC1α induced prostate cancer outgrowth. Immunohistochemistry in radical prostatectomy specimens showed that the levels of PGC1α expression were significantly higher in prostatic adenocarcinoma [H-score (mean ± SD): 179.0 ± 111.6] than in adjacent normal-appearing tissue (16.7 ± 29.9, P<0.001) or high-grade prostatic intraepithelial neoplasia (79.0 ± 94.7, P<0.001). Although there were no strong associations of PGC1α expression with tumor grade or stage, outcome analysis revealed that patients with high PGC1α (H-score of ≥200) tumor had a significantly higher risk of postoperative biochemical recurrence even in a multivariable setting (hazard ratio 5.469, P=0.004). In prostate cancer LNCaP and C4-2 cells, PGC1α silencing resulted in considerable reduction in the levels of prostate-specific antigen expression. Interestingly, PGC1α silencing inhibited the cell viability of not only AR-positive LNCaP/C4-2/22Rv1 lines but also AR-negative PC3/DU145 lines. Chromatin immunoprecipitation assay further revealed the binding of KDM5B to the promoter region of PGC1α in these lines. Additionally, treatment with a KDM5 inhibitor KDM5-C70 considerably reduced the expression of PGC1α and prostate-specific antigen, as well as the cell viability of all the AR-positive and AR-negative lines examined. PGC1α silencing or KDM5-C70 treatment also down-regulated the expression of phospho-JAK2 and phospho-STAT3 in both AR-positive and AR-negative cells. These findings suggest the involvement of PGC1α, as a downstream effector of KDM5B, in prostate cancer progression via both AR-dependent and AR-independent pathways. KDM5B-PGC1α is thus a potential therapeutic target for both androgen-sensitive and castration-resistant tumors. Meanwhile, PGC1α overexpression may serve as a useful prognosticator in those undergoing radical prostatectomy.

Multistep estimators of the between-study covariance matrix under the multivariate random-effects model for meta-analysis.

A wide variety of methods are available to estimate the between-study variance under the univariate random-effects model for meta-analysis. Some, but not all, of these estimators have been extended so that they can be used in the multivariate setting. We begin by extending the univariate generalised method of moments, which immediately provides a wider class of multivariate methods than was previously available. However, our main proposal is to use this new type of estimator to derive multivariate multistep estimators of the between-study covariance matrix. We then use the connection between the univariate multistep and Paule-Mandel estimators to motivate taking the limit, where the number of steps tends toward infinity. We illustrate our methodology using two contrasting examples and investigate its properties in a simulation study. We conclude that the proposed methodology is a fully viable alternative to existing estimation methods, is well suited to sensitivity analyses that explore the use of alternative estimators, and should be used instead of the existing DerSimonian and Laird-type moments based estimator in application areas where data are expected to be heterogeneous. However, multistep estimators do not seem to outperform the existing estimators when the data are more homogeneous. Advantages of the new multivariate multistep estimator include its semi-parametric nature and that it is computationally feasible in high dimensions. Our proposed estimation methods are also applicable for multivariate random-effects meta-regression, where study-level covariates are included in the model.

The observed trend in unusual daily mean temperatures over Germany from 1949 to 2018 and their relationships to major climatic drivers

Unusual events are detected by the statistical changes in ‘extremes’, when extreme anomalies persist through the temporal and spatial interactions of the variable of interest. To identify the occurrences of unusual daily mean temperature events in 3- and 5-day sequences, a statistical method based on an “outlyingness” function is proposed in this study. This function is based on the geometrical position of a point on the multivariate set. To illustrate the methodology, this study uses daily mean temperature records from 18 observation stations across Germany (1949–2018). The findings indicate discernible changes in the frequency of unusual events at the stations, mostly during the boreal winter months between the first and last 35 years of the study period. A wide range of temperature anomaly averages (− 12 °C to + 12 °C) are produced by the interaction of series between warm and cold conditions, which affects the occurrence of disappearing days. While this is happening, the unusual warming is more pronounced on days that emerge from both the 3- and 5-day sequences, with temperature anomaly averages ranging from + 4 to + 12 °C. The Atlantic Multi-Decadal Variability and the Arctic Oscillation/North Atlantic Oscillation, respectively, are both implicated in the unusual surface warming over Germany. The disappearance days of unusual events do not exhibit statistically significant correlations with climatic indices, suggesting a possible anthropogenic effect. The emphasis of this study is on the necessity of determining whether unusual events in daily temperature anomalies across Germany can be attributed to anthropogenic factors.

An analytical link of disaggregated green energy sources in achieving carbon neutrality in China: A policy based novel wavelet local multiple correlation analysis

As China, the world's largest emerging economy, pursues its objective of achieving net-zero emissions by 2060, the role of reliable and green energy sources, such as biomass, geothermal, and hydro, in facilitating the country's decarbonization efforts assumes utmost significance. In this study, we employ the wavelet local multiple correlation (WLMC) methodology to explore the dynamic interactions between these variables in a multivariate setting, spanning the period of 1990Q1 to 2020Q4. The WLCM approach offers improved signal resolution in the time-frequency domain, allowing for more accurate analysis of non-stationary signals and provides enhanced capability to capture and represent localized features and patterns in data. This analysis uncovers long-run asymmetric dynamic inter-correlations, which describe the mechanisms underlying carbon emissions escalation. Our findings reveal that biomass and hydro energy consumption negatively impact the environment, leading to emissions mitigation, while geothermal energy contributes positively throughout the research period. Notably, we establish that hydro energy consumption is the primary driving factor of carbon neutrality, with a more significant impact than any other determinant, facilitating a substantial reduction in carbon emissions and enhancing environmental quality. Our study has critical policy implications for meeting the Sustainable Development Goals (SDGs) and provides valuable insights for environmentalists in China, emphasizing the need to prioritize hydro energy consumption to achieve carbon neutrality.

The association between homocysteine and bacterial vaginosis: results from NHANES 2001–2004

Although no study has directly shown the relationship between bacterial vaginosis (BV) and homocysteine (HCY), we still found some association between these two through extensive literature and data analysis. BV score was calculated by Nugent’s method, less than equal to 6 is negative and greater than equal to 7 is positive. This article describes interrelationships we mined from data extracted by NHANES regarding BV and HCY under multiple covariates. We used two cycles of NHANES 2001–2002 and 2003–2004 in our study. We included 2398 participants in our study who recently completed the interview and the MEC tests. By investigating the relationship between BV and HCY under multivariate conditions, multiple linear regression analysis was performed. These factors may have influenced the results, such as ethnicity, age, education level, body mass index (BMI), etc. Serum vitamin B12, ferritin, percentage of segmented centrioles, and number of segmented centrioles were selected as potential covariates in our study. We observed that both the coarse model and the two adjusted models showed a high correlation between HCY and BV, and the correlation was positive. In the coarse model, OR = 1.26, 95% confidence interval (CI) 1.10, 1.44, P = 0.0018); HCY was positively correlated with BV (OR = 1.19, 95% confidence interval (CI) 1.05, 1.34, P = 0.0121). Multiple linear regression analysis was used to investigate the connection between BV and HCY under multivariate settings. The results of this study indicate that HCY is positively associated with the prevalence of BV and may play an important role in the prevention and management of BV.

Analysis and predictability of technologists' perception of MR exam complexity

IntroductionAs MRI becomes a routine clinical diagnostic method, its complexity of techniques, protocols and scanning is growing. On the other hand, aggravated by the ubiquitous shortage of workforce, technologists' stress level and burnout rates are increasing. In this context, our study aims to shed light on technologists’ perceived complexity of MR exams, by analyzing a multidimensional dataset composed of workflow, patient, and operational details, and further predicting perceived exam complexity. MethodsIn this IRB-approved study, data about imaging workflow, exam context, and patient characteristics were collected over one year from MR modality logfiles and from technologist questionnaires, including the perceived exam complexity. The association of individual factors with complexity was analyzed via Fisher's exact tests and Cramér's V values. Predictability of exam complexity was further evaluated via ROC analysis of three different multivariate classifiers. ResultsRetakes, delays, and extended exam duration are associated with perceived complexity (V ≥ 0.2). From the set of possible predictors, patient mobility and communication ability have the most influence on perceived complexity (V > 0.2), followed by special equipment needs (pulse oximetry, intubation, or ECG), protocol details and other patient characteristics. Feasibility of predicting the perceived exam complexity from a multivariate set of exam and patient details known at the time of scheduling has been demonstrated (AUC = 0.73), and suitable classification algorithms have been identified. ConclusionPerceived exam complexity is associated with various factors. Our results suggest that it can be predicted sufficiently well to support early operational decision making. Some factors, however, may not be readily available in hospital IT systems and must be obtained before scheduling. Implications for practiceResults and observations of this study could be utilized to assist operational scheduling in the radiology department and reduce MR technologists’ stress levels.

Confounding Factor Analysis for Vocal Fold Oscillations.

This paper provides a methodology to better understand the relationships between different aspects of vocal fold motion, which are used as features in machine learning-based approaches for detecting respiratory infections from voice recordings. The relationships are derived through a joint multivariate analysis of the vocal fold oscillations of speakers. Specifically, the multivariate setting explores the displacements and velocities of the left and right vocal folds derived from recordings of five extended vowel sounds for each speaker (/aa/, /iy/, /ey/, /uw/, and /ow/). In this multivariate setting, the differences between the bivariate and conditional interactions are analyzed by information-theoretic quantities based on transfer entropy. Incorporation of the conditional quantities reveals information regarding the confounding factors that can influence the statistical interactions among other pairs of variables. This is demonstrated on a vector autoregressive process where the analytical derivations can be carried out. As a proof of concept, the methodology is applied on a clinically curated dataset of COVID-19. The findings suggest that the interaction between the vocal fold oscillations can change according to individuals and presence of any respiratory infection, such as COVID-19. The results are important in the sense that the proposed approach can be utilized to determine the selection of appropriate features as a supplementary or early detection tool in voice-based diagnostics in future studies.

Life cycle stage as determinant of operational efficiency with implications for benchmarking

ABSTRACT We investigate the link between a firm’s life cycle and its operational efficiency and find that operational efficiency first increases and then decreases with the life cycle thus exhibiting an inverted – U shaped relationship. Our empirical examination reveals the relationship holds for subsamples of dividend- and non-dividend-paying firms, with dividend-paying firms displaying higher efficiency scores than their non-dividend-paying counterparts in each life cycle stage. In the literature on benchmarking the moderating role of a firm’s life cycle stage has not been considered. Thus, our findings have empirical significance for efficiency comparison in benchmarking studies. Including the life cycle stage of comparable firms permits a more homogeneous cohort for efficiency comparisons. Consequently, firm policy revolving around capital structure and dividend pay-outs need to take into cognizance that operational inefficiencies could also be a life stage phenomenon rather than agency issues led misallocation of resources. Additionally, we find that when operating efficiency is the variable being studied in a multivariate setting, controlling for the dividend status of a firm reduces omitted variable bias as dividend pay-outs and firm lifecycle are known to be intertwined.

Synergistic adverse impact of diabetes and chronic kidney disease on cardiovascular mortality in patients underwent percutaneous coronary intervention

Abstract Background Diabetes and chronic kidney disease (CKD) are frequently complicated each other. While approximately 40% of patients have diabetes among those with CKD, it is the most major (40-50%) and clinically important complication of diabetic patients which critically effects on their quality of life and outcomes. Moreover, numerous previous evidence has indicated the adverse prognostic impact by each of diabetes and CKD particularly in the secondary prevention of patients with established coronary artery disease (CAD). Nevertheless, to date, limited data is available regarding the synergistic impact of these two major risk factors on outcomes of patients with CAD. Purpose We studied the synergistic adverse effects of diabetes and chronic kidney disease on cardiovascular mortality in patients undergoing percutaneous coronary intervention (PCI). Methods This study is a part of a single-center prospective registry database of patients underwent PCI, which launched at 1984. A total of 4880 patients who underwent their first PCI (index PCI) at our university hospital between 2000 and 2018 were enrolled for the study. Participants were divided into 4 groups by the presence or absence of CKD and DM: DM−/CKD− (n=2028, 41.6%), DM+/CKD− (n=1411, 28.9%), DM−/CKD+ (n=754, 15.5%), and DM+/CKD+ (n=687, 14.1%). The endpoint was set as cardiovascular-related death (CV death). The median follow-up duration was 5.2 years since PCI procedure. Results During follow-up, 288 (5.9%) CV deaths were identified, in which 79.1% had DM or CKD. Unadjusted Kaplan-Meier curves showed patients with DM+/CKD+ had a significantly higher cumulative incidence of CV death compared to those with DM-/CKD- (log-rank p&amp;lt;0.0001) and DM-/CKD+ (p=0.03) (Figure 1). Moreover, multivariate Cox-proportional hazard analysis set DM-/CKD- as a reference and adjusted by age, gender, body mass index, hemoglobin, ejection fraction, acute coronary syndrome, statin and smoking history showed significantly increased risk of having both of diabetes and CKD (Hazard ratio (HR) and 95% confidence interval (CI) of DM and CKD: 3.1,: 1.9-5.1, p&amp;lt;0.0001) than having each of these (HR and 95% CI of DM alone: 1.2, 0.7-2.0, p=0.5, and of CKD alone: 1.8, 1.1-3.0, p=0.02 (Figure 2). Conclusions Findings in the present study indicated that CKD is a significant risk for CV death in patients following PCI, while only DM without CKD was not a significant risk factor in this population. Moreover, the risk for CV death was synergistically increased, when a patient had both of DM and CKD, compared to having each of these.Kaplan-Meier curves in CV deathAdjusted Cox hazard model in CV death

Prognostic Significance of NCAM1 (CD56) Expression in AML Patients Treated with HMA+Venetoclax

Introduction : NCAM1 (CD56) is a cellular adhesion molecule highly expressed on natural killer (NK) cells and other immune cells. NCAM1 is aberrantly expressed in 20% of patients with acute myeloid leukemia (AML) and is associated with resistance to chemotherapy, upregulation of the MAPK pathway, and poor prognosis. The significance of NCAM1 in the setting of venetoclax (VEN) therapy has not been explored. We set out to determine the functional and prognostic role of CD56 in vitro and in newly diagnosed (ND) patients receiving VEN-based therapy. Methods : OCI-AML2, KG-1, and MOLM-14 cell lines were purchased from ATCC and maintained in RPMI, 10% or 20% FBS, L-Glutamine, and penicillin/streptomycin. Ex vivo drug testing on cell viability was performed using an MTS assay. Briefly, AML cells were plated for 72 hours in replicates of six at 1,250 cells/well in R20 and titrated using a log scale over 7 doses (0-1000 nM) of VEN. After 3 days of culture at 37 °C in 5% CO2, MTS reagent was added, optical density was measured at 490 nm, and raw absorbance values were adjusted to a positive control and (normalized to untreated wells to produce cell viability estimates). VEN-resistant (VR) OCI-AML2 cells were generated by continuous incubation with escalating doses of VEN. Viability was monitored by Guava EasyCyte. Cells maintained resistant by co-culture with 1 µM of VEN twice weekly. Flow for NCAM1 was performed with commercially available antibodies on parental and VR cells. NCAM1 expression on parental vs. VR OCI-AML2 cells was compared with student's T test. Inactivation of NCAM1 in OCI-AML2 cells was carried out by CRISPR/Cas9 using sgRNAs cloned into plentiCRISPRv2 (Addgene, #52961). AUC was derived from 4-parameter logistic regression curves fit to normalized cell viability values for the 7 tested drug doses. Clinical outcomes including overall survival (OS) were assessed in a retrospective cohort of ND AML patients treated with a hypomethylating agent (HMA)+VEN at Oregon Health &amp; Science University (OHSU). CD56 positivity on AML blasts was based on surface immunophenotype determined by multiparameter flow cytometry. OS was measured from AML diagnosis (with time from diagnosis to VEN start as left-truncation), estimated by Kaplan-Meier, compared with the log-rank test, and modeled with Cox regression to estimate unadjusted and adjusted hazard ratios (HRs) for CD56. Results : Across the 3 AML cell lines evaluated, higher baseline NCAM1 expression levels correlated with increased ex vivo resistance to VEN. The MOLM14 cell line was most resistant with highest median NCAM1 expression (90%), followed by OCI-AML2 with median NCAM1 expression (55%) and then KG-1 with lowest NCAM1 expression (3%). VR OCI-AML2 cells had higher expression of NCAM1 compared to parental OCI-AML2 cells (80% expression vs 55%; p=0.0003). CRISPR knockout of NCAM1 sensitized parental OCI-AML2 cells to VENcompared to cells containing a non-targeting control guide (Fig 1). To support our pre-clinical findings, we retrospectively identified N=98 ND AML patients treated with HMA+VEN at OHSU from 12/2018 to 12/2022. Median follow-up was 16 months and 60% of patients died. Median age at diagnosis was 73 (range=19-87), with 39% at least 75 years old. While 65% of patients were male, 63% had de novo AML and 23% had an antecedent hematologic disorder (AHD). By European LeukemiaNet (ELN) 2022 prognostic risk classification, 58% had adverse risk (14% unknown). The most common somatic mutations were IDH1/2 (27% of patients), NRAS/KRAS (26%), FLT3 ITD or TKD (19%), NPM1 (13%), and TP53 (13%). According to diagnostic flow, 30% of patients were CD56+, 22% CD11b+, 15% CD64+, and 85% HLA-DR+. Median (range) days between diagnosis and VEN start was 13 (1-285). NCAM1 positivity significantly correlated with shorter OS in the univariable (HR=1.80 [95% CI: 1.04-3.09], p=0.032; Fig 2) and multivariable settings (HR=2.10 [95% CI: 1.17-3.75], p=0.012, N=97), with the latter model controlling for age ≥75, AHD, and CD11b. Conclusion : NCAM1 expression is a marker of poor prognosis in ND AML patients treated with HMA+VEN. Knockout of NCAM1 in the OCI-AML2 cell line sensitized cells to VEN. Our data suggest that NCAM1 expression on AML blasts plays a biological role in promoting cellular resistance to VEN. Given emerging knowledge of VEN targeted downstream pathway associations, experiments are underway to explore the role of MAPK pathway and BCL2 family member dependence.

Nanosensor Chemical Cytometry: Advances and Opportunities in Cellular Therapy and Precision Medicine.

With the definition of therapeutics now encompassing transplanted or engineered cells and their molecular products, there is a growing scientific necessity for analytics to understand this new category of drugs. This Perspective highlights the recent development of new measurement science on label-free single cell analysis, nanosensor chemical cytometry (NCC), and their potential for cellular therapeutics and precision medicine. NCC is based on microfluidics integrated with fluorescent nanosensor arrays utilizing the optical lensing effect of a single cell to real-time extract molecular properties and correlate them with physical attributes of single cells. This new class of cytometry can quantify the heterogeneity of the multivariate physicochemical attributes of the cell populations in a completely label-free and nondestructive way and, thus, suggest the vein-to-vein conditions for the safe therapeutic applications. After the introduction of the NCC technology, we suggest the technological development roadmap for the maturation of the new field: from the sensor/chip design perspective to the system/software development level based on hardware automation and deep learning data analytics. The advancement of this new single cell sensing technology is anticipated to aid rich and multivariate single cell data setting and support safe and reliable cellular therapeutics. This new measurement science can lead to data-driven personalized precision medicine.

On the weak convergence and the uniform-in-bandwidth consistency of the general conditional $U$-processes based on the copula representation: multivariate setting

$U$-statistics represent a fundamental class of statistics from modeling quantities of interest defined by multi-subject responses. $U$-statistics generalise the empirical mean of a random variable $X$ to sums over every $m$-tuple of distinct observations of $X$. Stute [Conditional U -statistics, Ann. Probab., 1991] introduced a class of estimators called conditional $U$-statistics. In the present work, we provide a new class of estimators of conditional $U$-statistics. More precisely, we investigate the conditional $U$-statistics based on copula representation. We establish the uniform-in-bandwidth consistency for the proposed estimator. In addition, uniform consistency is also established over $\varphi \in \mathscr{F}$ for a suitably restricted class $\mathscr{F}$, in both cases bounded and unbounded, satisfying some moment conditions. Our theorems allow data-driven local bandwidths for these statistics. Moreover, in the same context, we show the uniform bandwidth consistency for the nonparametric Inverse Probability of Censoring Weighted estimators of the regression function under random censorship, which is of its own interest. We also consider the weak convergence of the conditional $U$-statistics processes. We discuss the wild bootstrap of the conditional $U$-statistics processes. These results are proved under some standard structural conditions on the Vapnik-Chervonenkis class of functions and some mild conditions on the model.