Production Change Optimization Model of Nonlinear Supply Chain System under Emergencies.

Aiming at the problem that the upstream manufacturer cannot accurately formulate the production plan after the link of the nonlinear supply chain system changes under emergencies, an optimization model of production change in a nonlinear supply chain system under emergencies is designed. Firstly, based on the structural characteristics of the supply chain system and the logical relationship between production, sales, and storage parameters, a three-level single-chain nonlinear supply chain dynamic system model containing producers, sellers, and retailers was established based on the introduction of nonlinear parameters. Secondly, the radial basis function (RBF) neural network and improved fast variable power convergence law were introduced to improve the traditional sliding mode control, and the improved adaptive sliding mode control is proposed so that it can have a good control effect on the unknown nonlinear supply chain system. Finally, based on the numerical assumptions, the constructed optimization model was parameterized and simulated for comparison experiments. The simulation results show that the optimized model can reduce the adjustment time by 37.50% and inventory fluctuation by 42.97%, respectively, compared with the traditional sliding mode control, while helping the supply chain system to return the smooth operation after the change within 5 days.

Research on Real-Time Robust Optimization of Perishable Supply-Chain Systems Based on Digital Twins.

Aiming at the real-time robust optimization problem of perishable supply-chain systems in complex environments, a real-time robust optimization scheme based on supply-chain digital twins is proposed. Firstly, based on the quantitative logical relationship between production and sales of single-chain series supply-chain system products, the state space equation of the supply-chain system with logical characteristics, structural characteristics, and quantitative characteristics was constructed, and twin data were introduced to construct the digital twins of supply chains based on the state-space equation. Secondly, the perishable supply-chain system in complex environments was regarded as an uncertain closed-loop system from the perspective of the state space equation, and then a robust H∞ controller design strategy was proposed, and the supply-chain digital twins was used to update and correct the relevant parameters of the supply-chain system in real-time, to implement the real-time robust optimization based on the supply-chain digital twins. Finally, the simulation experiment was carried out with a cake supply-chain production as an example. The experimental results show that the real-time updating of relevant parameters through the digital twins can help enterprise managers to formulate reasonable management plans, effectively avoid the shortage problem of enterprises in the cake supply-chain system, and reduce the maximum inventory movement standard deviation of each link by 12.65%, 6.50%, and 14.87%, and the maximum production movement standard deviation by 70.21%, 56.84%, and 45.19%.

[A review on voluntary or involuntary eye movement classification methods based on electro-oculogram and their applications].

The eye-computer interaction technology based on electro-oculogram provides the users with a convenient way to control the device, which has great social significance. However, the eye-computer interaction is often disturbed by the involuntary eye movements, resulting in misjudgment, affecting the users' experience, and even causing danger in severe cases. Therefore, this paper starts from the basic concepts and principles of eye-computer interaction, sorts out the current mainstream classification methods of voluntary/involuntary eye movement, and analyzes the characteristics of each technology. The performance analysis is carried out in combination with specific application scenarios, and the problems to be solved are further summarized, which are expected to provide research references for researchers in related fields.

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model.

Purpose: Deformable registration problems are conventionally posed in a regularized optimization framework, where balance between fidelity and prescribed regularization usually needs to be tuned for each case. Even so, using a single weight to control regularization strength may be insufficient to reflect spatially variant tissue properties and limit registration performance. In this study, we proposed to incorporate a spatially variant deformation prior into image registration framework using a statistical generative model. Approach: A generator network is trained in an unsupervised setting to maximize the likelihood of observing the moving and fixed image pairs, using an alternating back-propagation approach. The trained model imposes constraints on deformation and serves as an effective low-dimensional deformation parametrization. During registration, optimization is performed over this learned parametrization, eliminating the need for explicit regularization and tuning. The proposed method was tested against SimpleElastix, DIRNet, and Voxelmorph. Results: Experiments with synthetic images and simulated CTs showed that our method yielded registration errors significantly lower than SimpleElastix and DIRNet. Experiments with cardiac magnetic resonance images showed that the method encouraged physical and physiological feasibility of deformation. Evaluation with left ventricle contours showed that our method achieved a dice of ( 0.93 ± 0.03 ) with significant improvement over all SimpleElastix options, DIRNet, and VoxelMorph. Mean average surface distance was on millimeter level, comparable to the best SimpleElastix setting. The average 3D registration time was 12.78 s, faster than 24.70 s in SimpleElastix. Conclusions: The learned implicit parametrization could be an efficacious alternative to regularized B-spline model, more flexible in admitting spatial heterogeneity.

Deep-learning augmented RNA-seq analysis of transcript splicing.

A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions with empirical RNA-seq evidence to infer differential alternative splicing between biological samples. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.

Single nucleotide polymorphism near CREB1, rs7591784, is associated with pretreatment methamphetamine use frequency and outcome of outpatient treatment for methamphetamine use disorder.

Although stimulant dependence is highly heritable, few studies have examined genetic influences on methamphetamine dependence. We performed a candidate gene study of 52 SNPs and pretreatment methamphetamine use frequency among 263 methamphetamine dependent Hispanic and Non-Hispanic White participants of several methamphetamine outpatient clinical trials in Los Angeles. One SNP, rs7591784 was significantly associated with pretreatment methamphetamine use frequency following Bonferroni correction (p < 0.001) in males but not females. We then examined rs7591784 and methamphetamine urine drug screen results during 12 weeks of outpatient treatment among males with treatment outcome data available (N = 94) and found rs7591784 was significantly associated with methamphetamine use during treatment controlling for pretreatment methamphetamine use. rs7591784 is near CREB1 and in a linkage disequilibrium block with rs2952768, previously shown to influence CREB1 expression. The CREB signaling pathway is involved in gene expression changes related to chronic use of multiple drugs of abuse including methamphetamine and these results suggest that variability in CREB signaling may influence pretreatment frequency of methamphetamine use as well as outcomes of outpatient treatment. Medications targeting the CREB pathway, including phosphodiesterase inhibitors, warrant investigation as pharmacotherapies for methamphetamine use disorders.

Randomized, placebo-controlled trial of bupropion in methamphetamine-dependent participants with less than daily methamphetamine use.

AIMS: Two previous randomized trials found an effect for bupropion in reducing methamphetamine use in the subgroup with lower frequency of methamphetamine use at baseline. This study aimed to replicate these results by comparing bupropion versus placebo in methamphetamine-dependent participants with less than daily methamphetamine use at baseline. METHODS: Methamphetamine-dependent volunteers reporting methamphetamine use on ≤29 of past 30 days were randomized to bupropion 150 mg twice daily (n = 41) or placebo (n = 43) and out-patient counseling for 12 weeks. The primary outcome was the proportion achieving end-of-treatment (EOT) methamphetamine abstinence (weeks 11 and 12) for bupropion versus placebo. A post-hoc analysis compared EOT abstinence by medication adherence assessed via plasma bupropion/hydroxybupropion level. RESULTS: There was no significant difference in EOT abstinence between bupropion (29%, 12 of 41) and placebo (14%, six of 43; P = 0.087). Among participants receiving bupropion, EOT abstinence was significantly higher in participants assessed as medication adherent by plasma bupropion/hydroxybupropion levels (54%, seven of 13) compared to non-adherent participants (18%, five of 28; P = 0.018). Medication adherence by plasma levels was low (32%). CONCLUSIONS: Bupropion may be efficacious for reducing methamphetamine in people with less than daily baseline methamphetamine use, but the evidence remains inconclusive.

A random-effects Markov transition model for Poisson-distributed repeated measures with non-ignorable missing values.

In biomedical research with longitudinal designs, missing values due to intermittent non-response or premature withdrawal are usually 'non-ignorable' in the sense that unobserved values are related to the patterns of missingness. By drawing the framework of a shared-parameter mechanism, the process yielding the repeated count measures and the process yielding missing values can be modelled separately, conditionally on a group of shared parameters. For chronic diseases, Markov transition models can be used to study the transitional features of the pathologic processes. In this paper, Markov Chain Monte Carlo algorithms are developed to fit a random-effects Markov transition model for incomplete count repeated measures, within which random effects are shared by the counting process and the missing-data mechanism. Assuming a Poisson distribution for the count measures, the transition probabilities are estimated using a Poisson regression model. The missingness mechanism is modelled with a multinomial-logit regression to calculate the transition probabilities of the missingness indicators. The method is demonstrated using both simulated data sets and a practical data set from a smoking cessation clinical trial.

A high-resolution map of active promoters in the human genome.

In eukaryotic cells, transcription of every protein-coding gene begins with the assembly of an RNA polymerase II preinitiation complex (PIC) on the promoter. The promoters, in conjunction with enhancers, silencers and insulators, define the combinatorial codes that specify gene expression patterns. Our ability to analyse the control logic encoded in the human genome is currently limited by a lack of accurate information regarding the promoters for most genes. Here we describe a genome-wide map of active promoters in human fibroblast cells, determined by experimentally locating the sites of PIC binding throughout the human genome. This map defines 10,567 active promoters corresponding to 6,763 known genes and at least 1,196 un-annotated transcriptional units. Features of the map suggest extensive use of multiple promoters by the human genes and widespread clustering of active promoters in the genome. In addition, examination of the genome-wide expression profile reveals four general classes of promoters that define the transcriptome of the cell. These results provide a global view of the functional relationships among transcriptional machinery, chromatin structure and gene expression in human cells.