A previous pilot trial evaluating computer-based training for cognitive-behavioral therapy (CBT4CBT) in 77 heterogeneous substance users (alcohol, marijuana, cocaine, and opioids) demonstrated ...preliminary support for its efficacy in the context of a community-based outpatient clinic. The authors conducted a more definitive trial in a larger, more homogeneous sample.
In this randomized clinical trial, 101 cocaine-dependent individuals maintained on methadone were randomly assigned to standard methadone maintenance or methadone maintenance with weekly access to CBT4CBT, with seven modules delivered within an 8-week trial.
Treatment retention and data availability were high and comparable across the treatment conditions. Participants assigned to the CBT4CBT condition were significantly more likely to attain 3 or more consecutive weeks of abstinence from cocaine (36% compared with 17%; p<0.05, odds ratio=0.36). The group assigned to CBT4CBT also had better outcomes on most dimensions, including urine specimens negative for all drugs, but these reached statistical significance only for individuals completing the 8-week trial (N=69). Follow-up data collected 6 months after treatment termination were available for 93% of the randomized sample; these data indicate continued improvement for those assigned to the CBT4CBT group, replicating previous findings regarding its durability.
This trial replicates earlier findings indicating that CBT4CBT is an effective adjunct to addiction treatment with durable effects. CBT4CBT is an easily disseminable strategy for broadening the availability of CBT, even in challenging populations such as cocaine-dependent individuals enrolled in methadone maintenance programs.
Artificial neural networks (ANNs) have been frequently used in industrial applications to model complex systems. However, using traditional ANNs for long-term planning tasks remains a challenge as ...they lack the capability to model uncertainty. Process noise and approximation errors cause ANN long-term estimations to deviate from the real behavior of the system. Unlike traditional ANNs, stochastic models provide a natural way to model uncertainty, providing estimations over a range of several possible outcomes. This paper introduces a stochastic modeling and planning approach using deep Bayesian neural networks (DBNNs). We use DBNNs to learn a stochastic model of the system dynamics. Planning is addressed as an open-loop trajectory optimization problem. We present two approaches for learning the dynamics: using single-step predictions and using multistep predictions. The advantages of the proposed methodology are as follows. First, accurate long-term estimations of the system state-trajectory probability distribution without the need for expert knowledge of the dynamics. Second, improved generalization and faster convergence rates in the trajectory optimization task when using multistep predictions to train the model. Third, viable for real-world applications since all expensive optimizations are executed offline while using a reasonable number of data samples. Testing is performed using challenging underactuated benchmark problems: the Cartpole and the Acrobot. The presented methodology successfully learns the swing-up maneuver using a relatively small number of iterations, with less than 125 sampled trajectories, and without any expert knowledge of the dynamics.
Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased ...approach, no need for prior knowledge or expensive manual processing, and ability to handle exponential data growth. Deep Autoencoder (AE) is a state-of-the-art deep neural network for unsupervised feature learning, which learns embedded-representations using a series of stacked layers. However, as the AE network gets deeper, these learned embedded-representations can deteriorate due to vanishing gradient, leading to performance degradation. This article presents ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature learning. The advantage of RAE and C-RAE is that it enables the user to add residual connections for increased network capacity without incurring the cost of degradation for unsupervised feature learning compared to standard AEs. While RAE and C-RAE inherit all the advantages of AEs, such as automated non-linear feature extraction and unsupervised learning, they also allow users to design larger networks without adverse effects on feature learning performance. We performed classification on learned embedded-representation to evaluate RAE and C-RAE. RAE and C-RAE were compared against AEs on MNIST, Fashion MNIST, and CIFAR10 datasets. When increasing the number of layers, C-RAE outperformed AE by showing significantly lower performance degradation of classification accuracy (less than 3%) compared to AE (33% to 65%). Further, C-RAE exhibited higher mean accuracy and lower variance of accuracy than standard AE. When comparing RAE and C-RAE with widely used feature learning methods (Convolutional AE, PCA, ICA, LLE, Factor Analysis, and SVD), C-RAE showed the highest accuracy.
Summary
Although ammonium (NH4+) is a key intermediate of plant nitrogen metabolism, high concentrations of NH4+ in the soil provoke physiological disorders that lead to the development of stress ...symptoms.
Ammonium nutrition was shown to induce the accumulation of glucosinolates (GSLs) in leaves of different Brassicaceae species. To further understand the link between ammonium nutrition and GSLs, we analysed the ammonium stress response of Arabidopsis mutants impaired in GSL metabolic pathway.
We showed that the MYB28 and MYB29 double mutant (myb28myb29), which is almost deprived of aliphatic GSLs, is highly hypersensitive to ammonium nutrition. Moreover, we evidenced that the stress symptoms developed were not a consequence of the lack of aliphatic GSLs. Transcriptomic analysis highlighted the induction of an iron (Fe) deficiency response in myb28myb29 under ammonium nutrition. Consistently, ammonium‐grown myb28myb29 plants showed altered Fe accumulation and homeostasis. Interestingly, we showed overall that growing Arabidopsis with increased Fe availability relieved ammonium stress symptoms and that this was associated with MYB28 and MYB29 expression.
Taken together, our data indicated that the control of Fe homeostasis was crucial for the Arabidopsis response to ammonium nutrition and evidenced that MYB28 and MYB29 play a role in this control.
Reactive Oxygen Species （ROS） are continuously produced as a result of aerobic metabolism or in response to biotic and abiotic stresses. ROS are not only toxic by-products of aerobic metabolism, but ...are also signalling molecules involved in several developmental processes in all organisms. Previous studies have clearly shown that an oxidative burst often takes place at the site of attempted invasion during the early stages of most plant-pathogen interac- tions. Moreover, a second ROS production can be observed during certain types of plant-pathogen interactions, which triggers hyper- sensitive cell death （HR）. This second ROS wave seems absent during symbiotic interactions. This difference between these two responses is thought to play an important signalling role leading to the establishment of plant defense. In order to cope with the deleterious effects of ROS, plants are fitted with a large panel of enzymatic and non-enzymatic antioxidant mechanisms. Thus,increasing numbers of publications report the characterisation of ROS producing and scavenging systems from plants and from microorganisms during interactions. In this review, we present the current knowledge on the ROS signals and their role during plant-microorganism interactions.
Selenium supplementation has been shown to have therapeutic value in chronic liver disease. We aimed to investigate the association between serum selenium, severity of liver fibrosis, and mortality ...in patients with Nonalcoholic Fatty Liver Disease (NAFLD).
A total of 33,944 patients were identified from the Third National Health and Nutrition Examination Survey. NAFLD was diagnosed by hepatic ultrasound after the exclusion of other forms of liver diseases. The severity of liver fibrosis was determined by NAFLD Fibrosis Score >0.676. Multivariate logistic regression analysis was used to investigate the relationship between serum selenium level and liver fibrosis. Association between serum selenium and all-cause mortality in NAFLD patients was also evaluated.
Multivariate logistic regression analysis demonstrated odds ratio of advanced liver fibrosis (NFS > 0.676) was significantly reduced with increasing serum selenium levels; OR 0.55, 95% CI 0.32−0.94 in the highest selenium quartile. On stratification analysis, the following populations had a significantly reduced risk of advanced liver fibrosis: non-Hispanic white = OR 0.41 0.24,0.68; female = OR 0.32 0.15−0.66 and age >47 = OR 0.47 0.28−0.79. The relationship was significant regardless of BMI as noted by BMI ≤ 30 Kg/m2= OR 0.42 0.19−0.91 and BMI > 30 Kg/m2=OR 0.52 0.28−0.97. Hazard ratio for all-cause mortality was HR 0.72 0.56−0.95.
The risk of advanced liver fibrosis is inversely associated with serum selenium levels, particularly in older patients, Caucasians, and females. All-cause mortality decreased with increased selenium levels. Selenium may play a role in the prevention of liver fibrosis in NAFLD.
Introduction
Marginal ulcer is one of the most common complications after Roux-en-Y gastric bypass and is defined as an ulceration of any depth at or near the gastrojejunal anastomosis. Different ...risk factors have been advocated to be the causative agent.
Materials and Methods
The weighted discharges from the Nationwide Inpatient Sample from 2003 to 2011 were used to assess for risk factors to develop marginal ulcer such as
Helicobacter pylori
infection, chronic nonsteroidal anti-inflammatory use, chronic aspirin use, alcohol dependence, smoking, hypertension, and diabetes mellitus type II.
Results
Chronic nonsteroidal anti-inflammatory drug use was the most significant risk factor for marginal ulcer, followed by
Helicobacter pylori
infection, obstructive sleep apnea, female sex, smoking, and alcohol dependence in decreasing order. Diabetes mellitus and hypertension were found to slightly increase the risk for marginal ulcer. The use of aspirin was found to be a minor risk factor during univariate analysis but a protective factor during multivariate analysis.
Conclusions
Multiple well-documented single factors are related to the genesis of a marginal ulcer, although it is likely that a combination of risk factors is responsible for this problem. These risk factors should be identified and removed or controlled.
Abstract
Nitrous oxide (N
2
O) emissions have been increasing as a result of intensive nitrogen (N) fertilisation. Soil nitrification and denitrification are the main sources of N
2
O, and the use of ...ammonium-based fertilisers combined with nitrification inhibitors (NIs) could be useful in mitigating N
2
O emissions from agricultural systems. In this work we looked at the N
2
O mitigation capacity of two dimethylpyrazol-based NIs, 3,4-dimethylpyrazole phosphate (DMPP) and 2-(N-3,4-dimethyl-1H-pyrazol-1-yl) succinic acid isomeric mixture (DMPSA), on soil nitrifying and denitrifying microbial populations under two contrasting soil water contents (40% and 80% soil water filled pore space; WFPS). Our results show that DMPP and DMPSA are equally efficient at reducing N
2
O emissions under 40% WFPS conditions by inhibiting bacterial ammonia oxidation. In contrast, at 80% WFPS DMPSA was less efficient than DMPP at reducing N
2
O emissions. Interestingly, at 80% WFPS, where lowered oxygen availability limits nitrification, both DMPP and DMPSA not only inhibited nitrification but also stimulated N
2
O reduction to molecular nitrogen (N
2
) via nitrous oxide reductase activity (Nos activity). Therefore, in this work we observed that DMP-based NIs stimulated the reduction of N
2
O to N
2
by nitrous oxide reductase during the denitrification process.
Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems. As such, topics such as reliability, ...performance, and security of CPSs continue to receive increased attention from the research community. CPSs produce massive amounts of data, creating opportunities to use predictive Machine Learning (ML) models for performance monitoring and optimization, preventive maintenance, and threat detection. However, the "black-box" nature of complex ML models is a drawback when used in safety-critical systems such as CPSs. While explainable ML has been an active research area in recent years, much of the work has been focused on supervised learning. As CPSs rapidly produce massive amounts of unlabeled data, relying on supervised learning alone is not sufficient for data-driven decision making in CPSs. Therefore, if we are to maximize the use of ML in CPSs, it is necessary to have explainable unsupervised ML models. In this paper, we outline how unsupervised explainable ML could be used within CPSs. We review the existing work in unsupervised ML, present initial desiderata of explainable unsupervised ML for CPS, and present a Self-Organizing Maps based explainable clustering methodology which generates global and local explanations. We evaluate the fidelity of the generated explanations using feature perturbation techniques. The results show that the proposed method identifies the most important features responsible for the decision-making process of Self-organizing Maps. Further, we demonstrated that explainable Self-Organizing Maps are a strong candidate for explainable unsupervised machine learning by comparing its model capabilities and limitations with current explainable unsupervised methods.
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