Towards Optimal Variance Reduction in Online Controlled Experiments

We study optimal variance reduction solutions for count and ratio metrics in online controlled experiments. Our methods leverage flexible machine learning tools to incorporate covariates that are independent from the treatment but have predictive power for the outcomes, and employ the cross-fitting technique to remove the bias in complex machine learning models. We establish CLT-type asymptotic inference based on our estimators under mild convergence conditions. Our procedures are optimal (efficient) for the corresponding targets as long as the machine learning estimators are consistent, without any requirement for their convergence rates. In complement to the general optimal procedure, we also derive a linear adjustment method for ratio metrics as a special case that is computationally efficient and can flexibly incorporate any pre-treatment covariates. We evaluate the proposed variance reduction procedures with comprehensive simulation studies and provide practical suggestions regarding commonly adopted assumptions in computing ratio metrics. When tested on real online experiment data from LinkedIn, the proposed optimal procedure for ratio metrics can reduce up to 80\% of variance compared to the standard difference-in-mean estimator and also further reduce up to 30\% of variance compared to the CUPED approach by going beyond linearity and incorporating a large number of extra covariates

Spatio-Temporal Branching for Motion Prediction using Motion Increments

Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy

DeepRank: Adapting Neural Tensor Networks for Ranking the Recommendations

Online real estate property portals are gaining great attraction from masses due to ease in finding properties for rental or sale/purchase. With a few clicks, a real estate portal can display relevant information to a user by ranking the searched items according to user’s specifications. It is highly significant that the ranking results display the most relevant search results to the user. Therefore, an efficient ranking algorithm that takes user’s context is crucial for enhancing user experience in finding real estate properties online. This paper proposes an expressive Neural Tensor Network to rank the properties when searched for based on the similarity between the two property entities. Previous similarity techniques do not take into account the numerous complex features used to define a property. We showed that the performance can be enhanced if the property entities are represented as an average of their constituting features before finding the similarity between them. The proposed method takes into account each feature dynamically and ranks properties according to similarity with an accuracy of 86.6%

A novel liposomal S-propargyl-cysteine: a sustained release of hydrogen sulfide reducing myocardial fibrosis via TGF-β1/Smad pathway

Purpose: S-propargyl-cysteine (SPRC; alternatively known as ZYZ-802) is a novel modulator of endogenous tissue H2S concentrations with known cardioprotective and anti-inflammatory effects. However, its rapid metabolism and excretion have limited its clinical application. To overcome these issues, we have developed some novel liposomal carriers to deliver ZYZ-802 to cells and tissues and have characterized their physicochemical, morphological and pharmacological properties. Methods :Two liposomal formulations of ZYZ-802 were prepared by thin-layer hydration and the morphological characteristics of each liposome system were assessed using a laser particle size analyzer and transmission electron microscopy. The entrapment efficiency and ZYZ-802 release profiles were determined following ultrafiltration centrifugation, dialysis tube and HPLC measurements. LC-MS/MS was used to evaluate the pharmacokinetic parameters and tissue distribution profiles of each formulation via the measurements of plasma and tissues ZYZ-802 and H2S concentrations. Using an in vivo model of heart failure (HF), the cardio-protective effects of liposomal carrier were determined by echocardiography, histopathology, western blot and the assessment of antioxidant and myocardial fibrosis markers.Results: Both liposomal formulations improved ZYZ-802 pharmacokinetics and optimized H2S concentrations in plasma and tissues. Liposomal ZYZ-802 showed enhanced cardioprotective effects in vivo. Importantly, liposomal ZYZ-802 could inhibit myocardial fibrosis via the inhibition of the TGF-β1/Smad signaling pathway. Conclusion: The liposomal formulations of ZYZ-802 have enhanced pharmacokinetic and pharmacological properties in vivo. This work is the first report to describe the development of liposomal formulations to improve the sustained release of H2S within tissues.Key word: Liposome; S-Propargyl-cysteine (SPRC, ZYZ-802); Hydrogen sulfide; Heart failure; Myocardial fibrosis; TGF-β1/Smad pathwa

Mutations in porin LamB contribute to ceftazidime-avibactam resistance in KPC-producing Klebsiella pneumoniae.

Ceftazidime-avibactam (CAZ-AVI) shows promising activity against carbapenem-resistant Klebsiella pneumoniae (CRKP), however, CAZ-AVI resistance have emerged recently. Mutations in KPCs, porins OmpK35 and/or OmpK36, and PBPs are known to contribute to the resistance to CAZ-AVI in CRKP. To identify novel CAZ-AVI resistance mechanism, we generated 10 CAZ-AVI-resistant strains from 14 CAZ-AVI susceptible KPC-producing K. pneumoniae (KPC-Kp) strains through in vitro multipassage resistance selection using low concentrations of CAZ-AVI. Comparative genomic analysis for the original and derived mutants identified CAZ-AVI resistance-associated mutations in KPCs, PBP3 (encoded by ftsI), and LamB, an outer membrane maltoporin. CAZ-AVI susceptible KPC-Kp strains became resistant when complemented with mutated blaKPC genes. Complementation experiments also showed that a plasmid borne copy of wild-type lamB or ftsI gene reduced the MIC value of CAZ-AVI in the induced resistant strains. In addition, blaKPC expression level increased in four of the six CAZ-AVI-resistant strains without KPC mutations, indicating a probable association between increased blaKPC expression and increased resistance in these strains. In conclusion, we here identified a novel mechanism of CAZ-AVI resistance associated with mutations in porin LamB in KPC-Kp

Ertapenem versus piperacillin/tazobactam for the treatment of complicated infections: a meta-analysis of randomized controlled trials

<p>Abstract</p> <p>Background</p> <p>Ertapenem, a new carbapenem with a favorable pharmacokinetic profile, has been approved for the treatment of complicated intra-abdominal Infections (cIAIs), acute pelvic infections (APIs) and complicated skin and skin-structure infections (cSSSIs). The aim of this study is to compare the efficacy and safety of ertapenem with piperacillin/tazobactam, which has been reported to possess good efficacy for the treatment of these complicated infections.</p> <p>Methods</p> <p>We performed a meta-analysis of randomized controlled trials identified in PubMed, Cochrane library and Embase that compared the efficacy and safety of ertapenem with piperacillin/tazobactam for the treatment of complicated infections including cIAIs, APIs, cSSSIs. The primary efficacy outcome was clinical treatment success assessed at the test-of-cure visit. The primary safety outcome was drug related clinical and laboratory adverse events occurred during the treatment and the post-treatment period.</p> <p>Result</p> <p>Six RCTs, involving 3161 patients, were included in our meta-analysis. Ertapenem was associated similar clinical treatment success with piperacillin/tazobactam for complicated infections treatment (clinically evaluable population, 1937 patients, odds ratios: 1.15, 95% confidence intervals: 0.89-1.49; modified intention to treat population, 2855 patients, odds ratios: 1.03, 95% confidence intervals: 0.87-1.22). All of secondary efficacy outcomes analysis obtained similar findings with clinical treatment success. No difference was found about the incidence of drug related adverse events between ertapenem and piperacillin/tazobactam groups.</p> <p>Conclusion</p> <p>This meta-analysis provides evidence that ertapenem 1 g once a day can be used as effectively and safely as recommended dose of piperacillin/tazobactam, for the treatment of complicated infections, particularly of mild to moderate severity. It is an appealing option for the treatment of these complicated infections.</p

Potential Implications of Quercetin in Autoimmune Diseases

Autoimmune diseases are a worldwide health problem with growing rates of morbidity, and are characterized by breakdown and dysregulation of the immune system. Although their etiology and pathogenesis remain unclear, the application of dietary supplements is gradually increasing in patients with autoimmune diseases, mainly due to their positive effects, relatively safety, and low cost. Quercetin is a natural flavonoid that is widely present in fruits, herbs, and vegetables. It has been shown to have a wide range of beneficial effects and biological activities, including anti-inflammation, anti-oxidation, and neuroprotection. In several recent studies quercetin has reportedly attenuated rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, and systemic lupus erythematosus in humans or animal models. This review summarizes the evidence for the pharmacological application of quercetin for autoimmune diseases, which supports the view that quercetin may be useful for their prevention and treatment