Compared to the control, the total arsenic and iAs of treatments decreased by 14-41% and 12-32% respectively. Finally, we found that rainwater and trace H2O2 irrigation likely increased rice fields.The quality of generative models (such as Generative adversarial networks and Variational Auto-Encoders) depends heavily on the choice of a good probability distance. However some popular metrics like the Wasserstein or the Sliced Wasserstein distances, the Jensen-Shannon divergence, the Kullback-Leibler divergence, lack convenient properties such as (geodesic) convexity, fast evaluation and so on. To address these shortcomings, we introduce a class of distances that have built-in convexity. PF573228 We investigate the relationship with some known paradigms (sliced distances - a synonym for Radon distances - reproducing kernel Hilbert spaces, energy distances). The distances are shown to possess fast implementations and are included in an adapted Variational Auto-Encoder termed Radon-Sobolev Variational Auto-Encoder (RS-VAE) which produces high quality results on standard generative datasets.This article is devoted to the H∞ estimation problem for stochastic semi-Markovian switching complex-valued neural networks subject to incomplete measurement outputs, where the time-varying delay also depends on another semi-Markov process. A sequence of random variables with known statistical property is introduced to depict the missing measurement phenomenon. Based on the generalized Itoˆ's formula in complex form concerning with the semi-Markovian systems, complex-valued reciprocal convex inequality as well as intensive stochastic analysis method, some mode-dependent sufficient conditions are presented guaranteeing the estimation error system to be exponentially mean-square stable with a prespecified H∞ disturbance attenuation level. In addition, the mode-dependent estimator gain matrices are appropriately designed according to the feasible solutions of certain complex matrix inequalities. In the end, one numerical example is provided to illustrate effectiveness of the theoretical results.Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism. In the experiments, our method either reduces VGG16/19 and ResNet34/50 computation by nearly 5× on CIFAR10 and 2× on ImageNet without harming the performance, where the weights are compressed by 10× and 4× respectively; or improves the accuracy by up to 1.0% on CIFAR10 and 0.5% on ImageNet with slightly higher efficiency. The code will be available on www.github.com/Genera1Z/LearnableHeterogeneousConvolution.The paper focuses on the synchronization problem for a class of coupled neural networks with impulsive control, where the saturation structure of impulse action is fully considered. The coupled neural networks under consideration are subject to mixed delays including transmission delay and coupled delay. The sector condition in virtue of a new constraint of set inclusion is given for a addressed network, based on which a sufficient condition for exponential synchronization problem is obtained by replacing saturation nonlinearity with a dead-zone function. In the framework of saturated impulses, our results relying on the domain of attraction can still achieve the synchronization of coupled delayed neural networks. In addition, the estimating domain of attraction is proposed as large as possible by solving an optimization problem. Finally, a numerical simulation example is presented to demonstrate the effectiveness of the proposed results.COVID-19 pandemic causative SARS-CoV-2 coronavirus is still rapid in progression and transmission even after a year. Understanding the viral transmission and impeding the replication process within human cells are considered as the vital point to control and overcome COVID-19 infection. Non-structural Protein 1, one among the proteins initially produced upon viral entry into human cells, instantly binds with the human ribosome and inhibit the host translation process by preventing the mRNA attachment. However, the formation of NSP1 bound Ribosome complex does not affect the viral replication process. NSP1 plays an indispensable role in modulating the host gene expression and completely steals the host cellular machinery. The full-length structure of NSP1 is essential for the activity in the host cell and importantly the loop connecting N and C-terminal domains are reported to play a role in ribosome binding. Due to the unavailability of the experimentally determined full-length structure of NSP1, we have mode to -37.23 kcal/mol, indicating its binding efficacy in the predicted binding site of NSP1. The density functional theory calculations were performed for the selected five phytochemicals to determine the complex stability and chemical reactivity. Thus, the identified phytochemicals could further be used as effective anti-viral agents to overcome COVID-19 and as well as several other viral infections.Persistent cognitive and mood dysfunction is the primary CNS symptom in veterans afflicted with Gulf War Illness (GWI). This study investigated the efficacy of melatonin (MEL) for improving cognitive and mood function with antioxidant, antiinflammatory, and pro-cognitive effects in a rat model of chronic GWI. Six months after exposure to GWI-related chemicals and stress, rats were treated with vehicle or MEL (5, 10, 20, 40, and 80 mg/kg) for eight weeks. Behavioral tests revealed cognitive and mood dysfunction in GWI rats receiving vehicle, which were associated with elevated oxidative stress, reduced NRF2, catalase and mitochondrial complex proteins, astrocyte hypertrophy, activated microglia with NLRP3 inflammasomes, elevated proinflammatory cytokines, waned neurogenesis, and synapse loss in the hippocampus. MEL at 10 mg/kg alleviated simple and associative recognition memory dysfunction and anhedonia, along with reduced oxidative stress, enhanced glutathione and complex III, and reduced NLRP3 inflammasomes, IL-18, TNF-α, and IFN-γ.