The Impact of D-amino acids on Formation and Integrity of Biofilm – Effect of Growth Condition and Bacteria Type

Biofouling is a major issue in applying nanofiltration and reverse osmosis technologies for wastewater treatment. Biofilm formed on the surface of membranes will severely decline the flux and cause energy waste. In this study, a novel biofouling control method that applies D-amino acids to inhibit biofilm formation was investigated. The D-amino acids previously reported to inhibit biofilm formation and disrupt existing biofilm – D-tyrosine and the mixture of D-tyrosine, D-tryptophan, D-leucine and D-methionine were tested. Pseudomonas aeruginosa and Bacillus subtilis were used as model Gram-negative and Gram-positive bacteria, respectively. D-amino acids have little effect and some effect on inhibition of biofilm formation and disruption of exiting biofilm to Pseudomonas aeruginosa, but have good effect to Bacillus subtilis. A commonly used microtiter plate assay for quantitative biofilm measurement was systematically evaluated and optimized for screening biofilm control agents. The effect of D-tyrosine on inhibition of organic fouling and P. aeruginosa biofouling on NF90 membrane surface in bench scale dead end filtration experiment was examined, which shows that D-tyrosine can effectively inhibit organic fouling and P. aeruginosa biofouling on NF90 membrane surface

Distributed Logistic Regression for Massive Data with Rare Events

Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression in a distributed system. For a distributed framework, we face the following two challenges. The first challenge is how to distribute the data. In this regard, two different distribution strategies (i.e., the RANDOM strategy and the COPY strategy) are investigated. The second challenge is how to select an appropriate type of objective function so that the best asymptotic efficiency can be achieved. Then, the under-sampled (US) and inverse probability weighted (IPW) types of objective functions are considered. Our results suggest that the COPY strategy together with the IPW objective function is the best solution for distributed logistic regression with rare events. The finite sample performance of the distributed methods is demonstrated by simulation studies and a real-world Sweden Traffic Sign dataset

Distributed Estimation and Inference for Spatial Autoregression Model with Large Scale Networks

The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This enables us to obtain a one-step estimator by taking a weighted average of local estimators on each worker. Afterwards, a refined two-step estimation is designed to further reduce the estimation bias. For statistical inference, we utilize a random projection method to reduce the expensive communication cost. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. The theoretical findings and computational advantages are validated by several numerical simulations implemented on the Spark system. Lastly, an experiment on the Yelp dataset further illustrates the usefulness of the proposed methodology

An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models

Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descent methods with momentum (MGDM) are widely used to solve large-scale optimization problems with massive datasets. Despite the success of the MGDM methods in practice, their theoretical properties are still underexplored. To this end, we investigate the theoretical properties of MGDM methods based on the linear regression models. We first study the numerical convergence properties of the MGDM algorithm and further provide the theoretically optimal tuning parameters specification to achieve faster convergence rate. In addition, we explore the relationship between the statistical properties of the resulting MGDM estimator and the tuning parameters. Based on these theoretical findings, we give the conditions for the resulting estimator to achieve the optimal statistical efficiency. Finally, extensive numerical experiments are conducted to verify our theoretical results.Comment: 45 pages, 5 figure

The Exploration and Evaluation of Generating Affective 360∘^\circ Panoramic VR Environments Through Neural Style Transfer

Affective virtual reality (VR) environments with varying visual style can impact users' valence and arousal responses. We applied Neural Style Transfer (NST) to generate 360$^\circ$ VR environments that elicited users' varied valence and arousal responses. From a user study with 30 participants, findings suggested that generative VR environments changed participants' arousal responses but not their valence levels. The generated visual features, e.g., textures and colors, also altered participants' affective perceptions. Our work contributes novel insights about how users respond to generative VR environments and provided a strategy for creating affective VR environments without altering content

Novel solar forecasting scheme modelled by mixer dual path network and based on sky images

The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model

Prediction of pediatric dose of tirzepatide from the reference adult dose using physiologically based pharmacokinetic modelling

Tirzepatide is an emerging hypoglycemic agent that has been increasing used in adults, yet its pharmacokinetic (PK) behavior and dosing regimen in pediatric population remain unclear. This study aimed to employ the physiologically based pharmacokinetic (PBPK) model to predict changes of tirzepatide exposure in pediatric population and to provide recommendations for its dose adjustments. A PBPK model of tirzepatide in adults was developed and verified by comparing the simulated plasma exposure with the observed data using PK-Sim&MoBi software. This model was then extrapolated to three specific age subgroups, i.e., children (10–12 years), early adolescents (12–15 years), and adolescents (15–18 years). Each subgroup included healthy and obese population, respectively. All known age-related physiological changes were incorporated into the pediatric model. To identify an appropriate dosing regimen that yielded PK parameters which were comparable to those in adults, the PK parameters for each aforementioned subgroup were predicted at pediatric doses corresponding to 87.5%, 75%, 62.5%, and 50% of the adult reference dose. According to the results of simulation, dose adjustments of tirzepatide are necessary for the individuals aged 10–12 years, as well as those aged 12–15 years with healthy body weights. In conclusion, the adult PBPK model of tirzepatide was successfully developed and validated for the first time, and the extrapolated pediatric model could be used to predict pediatric dosing regimen of tirzepatide, which will provide invaluable references for the design of future clinical trials and its rational use in the pediatric population

Lysine-specific demethylase 5C promotes hepatocellular carcinoma cell invasion through inhibition BMP7 expression

The primers used for the amplification of the indicated genes.(DOCX 17 kb

Differentiable Logic Machines

The integration of reasoning, learning, and decision-making is key to build more general AI systems. As a step in this direction, we propose a novel neural-logic architecture that can solve both inductive logic programming (ILP) and deep reinforcement learning (RL) problems. Our architecture defines a restricted but expressive continuous space of first-order logic programs by assigning weights to predicates instead of rules. Therefore, it is fully differentiable and can be efficiently trained with gradient descent. Besides, in the deep RL setting with actor-critic algorithms, we propose a novel efficient critic architecture. Compared to state-of-the-art methods on both ILP and RL problems, our proposition achieves excellent performance, while being able to provide a fully interpretable solution and scaling much better, especially during the testing phase