A penalized Cox proportional hazards model with multiple time-varying exposures

In recent pharmacoepidemiology research, the increasing use of electronic medication dispensing data provides an unprecedented opportunity to examine various health outcomes associated with long-term medication usage. Often, patients may take multiple types of medications intended for the same medical condition and the medication exposure status and intensity may vary over time, posing challenges to the statistical modeling of such data. In this article, we propose a penalized Cox proportional hazards (PH) model with multiple functional covariates and potential interaction effects. We also consider constrained coefficient functions to ensure a diminishing medication effect over time. Hypothesis testing of interaction effect and main effect was discussed under the penalized Cox PH model setting. Our simulation studies demonstrate the adequate performance of the proposed methods for both parameter estimation and hypothesis testing. Application to a primary care depression cohort study was also illustrated to examine the effects of two common types of antidepressants on the risk of coronary artery disease

Post-Intensive Care Unit Psychiatric Comorbidity and Quality of Life

The prevalence of psychiatric symptoms ranges from 17% to 44% in intensive care unit (ICU) survivors. The relationship between the comorbidity of psychiatric symptoms and quality of life (QoL) in ICU survivors has not been carefully examined. This study examined the relationship between psychiatric comorbidities and QoL in 58 survivors of ICU delirium. Patients completed 3 psychiatric screens at 3 months after discharge from the hospital, including the Patient Health Questionnaire-9 (PHQ-9) for depression, the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for anxiety, and the Post-Traumatic Stress Syndrome (PTSS-10) questionnaire for posttraumatic stress disorder. Patients with 3 positive screens (PHQ-9 ≥ 10; GAD-7 ≥ 10; and PTSS-10 > 35) comprised the high psychiatric comorbidity group. Patients with 1 to 2 positive screens were labeled the low to moderate (low-moderate) psychiatric comorbidity group. Patients with 3 negative screens were labeled the no psychiatric morbidity group. Thirty-one percent of patients met the criteria for high psychiatric comorbidity. After adjusting for age, gender, Charlson Comorbidity Index, discharge status, and prior history of depression and anxiety, patients who had high psychiatric comorbidity were more likely to have a poorer QoL compared with the low-moderate comorbidity and no morbidity groups, as measured by a lower EuroQol 5 dimensions questionnaire 3-level Index (no, 0.69 ± 0.25; low-moderate, 0.70 ± 0.19; high, 0.48 ± 0.24; P = 0.017). Future studies should confirm these findings and examine whether survivors of ICU delirium with high psychiatric comorbidity have different treatment needs from survivors with lower psychiatric comorbidity

Antidepressant Use and Depressive Symptoms in Intensive Care Unit Survivors

Nearly 30% of intensive care unit (ICU) survivors have depressive symptoms 2-12 months after hospital discharge. We examined the prevalence of depressive symptoms and risk factors for depressive symptoms in 204 patients at their initial evaluation in the Critical Care Recovery Center (CCRC), an ICU survivor clinic based at Eskenazi Hospital in Indianapolis, Indiana. Thirty-two percent (N = 65) of patients had depressive symptoms on initial CCRC visit. For patients who are not on an antidepressant at their initial CCRC visit (N = 135), younger age and lower education level were associated with a higher likelihood of having depressive symptoms. For patients on an antidepressant at their initial CCRC visit (N = 69), younger age and being African American race were associated with a higher likelihood of having depressive symptoms. Future studies will need to confirm these findings and examine new approaches to increase access to depression treatment and test new antidepressant regimens for post-ICU depression

Perioperative Risk Factors for Postoperative Delirium in Patients Undergoing Esophagectomy

Background Postoperative delirium affects up to 50% of patients undergoing esophagectomy and is associated with negative outcomes. The perioperative risk factors for delirium in this population are not well understood. We conducted this study to assess perioperative risk factors for postoperative delirium among esophagectomy patients. Methods We performed a secondary data analysis of patients enrolled in a randomized controlled trial evaluating the efficacy of haloperidol prophylaxis postoperatively in reducing delirium among esophagectomy patients. Postoperative delirium was assessed twice daily using the Confusion Assessment Method for the ICU. Univariate and logistic regression analyses were performed to examine the association between perioperative variables and development of postoperative delirium. Results Of 84 consecutive esophagectomy patients, 27 (32%) developed postoperative delirium. Patients who developed postoperative delirium had higher APACHE II scores [22.1 (6.5) versus 17.4 (6.8); p=0.003], longer mechanical ventilation days [1.7 (1.4) versus 1.0 (1.1); p=0.001], and longer ICU days [5.1 (2.6) versus 2.6 (1.6); p<0.001]. In a logistic regression model, only ICU length of stay was found to have significant association with postoperative delirium [OR 1.65; 95% CI 1.21-2.25]. Conclusions ICU length of stay was significantly associated with postoperative delirium. Other perioperative factors including duration of surgery, blood loss, and hemoglobin levels were not significantly associated with postoperative delirium

Overexpression of AtBMI1C, a Polycomb Group Protein Gene, Accelerates Flowering in Arabidopsis

Polycomb group protein (PcG)-mediated gene silencing is emerging as an essential developmental regulatory mechanism in eukaryotic organisms. PcGs inactivate or maintain the silenced state of their target chromatin by forming complexes, including Polycomb Repressive Complex 1 (PRC1) and 2 (PRC2). Three PRC2 complexes have been identified and characterized in Arabidopsis; of these, the EMF and VRN complexes suppress flowering by catalyzing the trimethylation of lysine 27 on histone H3 of FLOWER LOCUS T (FT) and FLOWER LOCUS C (FLC). However, little is known about the role of PRC1 in regulating the floral transition, although AtRING1A, AtRING1B, AtBMI1A, and AtBMI1B are believed to regulate shoot apical meristem and embryonic development as components of PRC1. Moreover, among the five RING finger PcGs in the Arabidopsis genome, four have been characterized. Here, we report that the fifth, AtBMI1C, is a novel, ubiquitously expressed nuclear PcG protein and part of PRC1, which is evolutionarily conserved with Psc and BMI1. Overexpression of AtBMI1C caused increased H2A monoubiquitination and flowering defects in Arabidopsis. Both the suppression of FLC and activation of FT were observed in AtBMI1C-overexpressing lines, resulting in early flowering. No change in the H3K27me3 level in FLC chromatin was detected in an AtBMI1C-overexpressing line. Our results suggest that AtBMI1C participates in flowering time control by regulating the expression of FLC; moreover, the repression of FLC by AtBMI1C is not due to the activity of PRC2. Instead, it is likely the result of PRC1 activity, into which AtBMI1C is integrated

Analysis and Construction of Efficient RFID Authentication Protocol with Backward Privacy

Privacy of RFID systems is receiving increasing attentions in the RFID community and an important issue required as to the security of RFID system. Backward privacy means the adversary can not trace the tag later even if he reveals the internal states of the tag sometimes before. In this paper, we analyze two recently proposed RFID authentication schemes: Randomized GPS and Randomized Hashed GPS scheme. We show both of them can not provide backward privacy in Juels and Weis privacy model, which allows the adversary to know whether the reader authenticates the tag successfully or not. In addition, we present a new protocol, called Challenge-Hiding GPS, based on the Schnorr identification scheme. The challenge is hidden from the eavesdropping through the technique of Diffie-Hellman key agreement protocol. The new protocol can satisfy backward privacy, and it has less communication overheads and almost the same computation, compared with the two schemes analyzed

Association between APOC3 polymorphisms and non-alcoholic fatty liver disease risk: a meta-analysis

Background and Aim: The apolipoprotein C3 (APOC3) polymorphism has been reported to predispose to non-alcoholic fatty liver disease (NAFLD). However, the results remain inconclusive. This meta-analysis aimed to provide insights into the association between APOC3 polymorphisms and NAFLD risk. Methods: Studies with terms \u201cNALFD\u201d and \u201cAPOC3\u201d were retrieved from PubMed, Web of Science, CNKI and Wanfang databases up to August 1, 2019. Pooled odds ratio (OR) and 95% confidence interval (95% CI) for the association of APOC3 polymorphisms and NAFLD risk were calculated using fixed and random-effects models. Results: A total of twelve studies from eleven articles were included. Of them, eight studies (1750 cases and 2181 controls) reported the strong association of variant rs2854116 with NAFLD and six studies (1523 cases and 1568 controls) found the association of rs2854117 polymorphism with NAFLD. Overall, a statistically significant association between rs2854116 polymorphism of APOC3 gene and NAFLD risk was found only under dominant model. However, association of rs2854117 polymorphism with NAFLD risk was not detected under all four genetic models. In sub-group analysis of NAFLD subjects based on country, no association among them in China was detected. Besides, four studies analyze the association between the two polymorphisms and clinical characteristics in all subjects or NAFLD patients, and we also failed detect any association between the wild carriers and variant carriers. Conclusion: The meta-analyses suggests that the rs2854116 polymorphism but not rs2854117 polymorphism in APOC3 gene might be a risk factor for NAFLD among Asians. That is, individuals with CT+CC genotype have higher risk of developing NAFLD. However, studies with sufficient sample size are needed for the further validation

Trainability Analysis of Quantum Optimization Algorithms from a Bayesian Lens

The Quantum Approximate Optimization Algorithm (QAOA) is an extensively studied variational quantum algorithm utilized for solving optimization problems on near-term quantum devices. A significant focus is placed on determining the effectiveness of training the $n$-qubit QAOA circuit, i.e., whether the optimization error can converge to a constant level as the number of optimization iterations scales polynomially with the number of qubits. In realistic scenarios, the landscape of the corresponding QAOA objective function is generally non-convex and contains numerous local optima. In this work, motivated by the favorable performance of Bayesian optimization in handling non-convex functions, we theoretically investigate the trainability of the QAOA circuit through the lens of the Bayesian approach. This lens considers the corresponding QAOA objective function as a sample drawn from a specific Gaussian process. Specifically, we focus on two scenarios: the noiseless QAOA circuit and the noisy QAOA circuit subjected to local Pauli channels. Our first result demonstrates that the noiseless QAOA circuit with a depth of $\tilde{\mathcal{O}}\left(\sqrt{\log n}\right)$ can be trained efficiently, based on the widely accepted assumption that either the left or right slice of each block in the circuit forms a local 1-design. Furthermore, we show that if each quantum gate is affected by a $q$-strength local Pauli channel with the noise strength range of $1/{\rm poly} (n)$ to 0.1, the noisy QAOA circuit with a depth of $\mathcal{O}\left(\log n/\log(1/q)\right)$ can also be trained efficiently. Our results offer valuable insights into the theoretical performance of quantum optimization algorithms in the noisy intermediate-scale quantum era