Mixture of easy trials enables transient and sustained perceptual improvements through priming and perceptual learning.

The sense of vision allows us to discriminate fine details across a wide range of tasks. How to improve this perceptual skill, particularly within a short training session, is of substantial interest. Emerging evidence suggests that mixing easy trials can quickly improve performance in hard trials, but it is equivocal whether the improvement is short-lived or long-lasting, and additionally what accounts for this improvement. Here, by tracking objective performance (accuracy) and subjective experience (ratings of target visibility and choice confidence) over time and in a large sample of participants, we demonstrate the coexistence of transient and sustained effects of mixing easy trials, which differ markedly in their timescales, in their effects on subjective awareness, and in individual differences. In particular, whereas the transient effect was found to be ubiquitous and manifested similarly across objective and subjective measures, the sustained effect was limited to a subset of participants with weak convergence from objective and subjective measures. These results indicate that mixture of easy trials enables two distinct, co-existing forms of rapid perceptual improvements in hard trials, as mediated by robust priming and fragile learning. Placing constraints on theory of brain plasticity, this finding may also have implications for alleviating visual deficits

Stacking tunable interlayer magnetism in bilayer CrI3

Diverse interlayer tunability of physical properties of two-dimensional layers mostly lies in the covalent-like quasi-bonding that is significant in electronic structures but rather weak for energetics. Such characteristics result in various stacking orders that are energetically comparable but may significantly differ in terms of electronic structures, e.g. magnetism. Inspired by several recent experiments showing interlayer anti-ferromagnetically coupled CrI3 bilayers, we carried out first-principles calculations for CrI3 bilayers. We found that the anti-ferromagnetic coupling results from a new stacking order with the C2/m space group symmetry, rather than the graphene-like one with R3 as previously believed. Moreover, we demonstrated that the intra- and inter-layer couplings in CrI3 bilayer are governed by two different mechanisms, namely ferromagnetic super-exchange and direct-exchange interactions, which are largely decoupled because of their significant difference in strength at the strong- and weak-interaction limits. This allows the much weaker interlayer magnetic coupling to be more feasibly tuned by stacking orders solely. Given the fact that interlayer magnetic properties can be altered by changing crystal structure with different stacking orders, our work opens a new paradigm for tuning interlayer magnetic properties with the freedom of stacking order in two dimensional layered materials

Effects of Layering Milling Technology on Dough Properties of Highland Barley and Bread Qualities

Highland barley (Qingke) is rich in nutrients and has the nutrient composition of “three highs and two lows,” which are high vitamin, high soluble dietary fiber, high β-glucan, low fat, and low sugar. In this paper, it was proposed to remove the layers of different ratios with different peeling rates. Then, different peeled highland barley was milled into flour and added to bread flour in the same proportion to make wheat-highland barley bread. The results showed that the removal of the cortex of highland barley flour was beneficial to its fermentation characteristics, the comprehensive capacity of gas production and gas holding has been improved, and the maximum fermentation height and retention coefficient were both at QK2-35%, while the gas production at QK4-35% is higher than other samples. From QK0-35% to QK5-35%, the  significance of the highland barley bread increased, from 56.31 to 70.88. The results showed that choosing QK4-35% as the best peeling rate of highland barley flour blends could not only retain the nutritional value of highland barley bread but also optimize the quality of bread to a certain extent, which could attract consumers and has a good development prospect

Physics-informed neural networks with hard constraints for inverse design

Inverse design arises in a variety of areas in engineering such as acoustic, mechanics, thermal/electronic transport, electromagnetism, and optics. Topology optimization is a major form of inverse design, where we optimize a designed geometry to achieve targeted properties and the geometry is parameterized by a density function. This optimization is challenging, because it has a very high dimensionality and is usually constrained by partial differential equations (PDEs) and additional inequalities. Here, we propose a new deep learning method -- physics-informed neural networks with hard constraints (hPINNs) -- for solving topology optimization. hPINN leverages the recent development of PINNs for solving PDEs, and thus does not rely on any numerical PDE solver. However, all the constraints in PINNs are soft constraints, and hence we impose hard constraints by using the penalty method and the augmented Lagrangian method. We demonstrate the effectiveness of hPINN for a holography problem in optics and a fluid problem of Stokes flow. We achieve the same objective as conventional PDE-constrained optimization methods based on adjoint methods and numerical PDE solvers, but find that the design obtained from hPINN is often simpler and smoother for problems whose solution is not unique. Moreover, the implementation of inverse design with hPINN can be easier than that of conventional methods

Critical charge and spin instabilities in superconducting La3_3Ni2_2O7_7

Motivated by the recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ under high pressure, we explore its potential charge and spin instabilities through combined model analysis and first-principles calculations. Taking into account the negative charge-transfer nature of high valence nickel, a fully correlated two-cluster model identifies a lattice-coupled rocksalt-type charge instability characterized by substantial fluctuations of oxygen holes. This instability is corroborated by density-functional-theory plus $U$ calculations that also reveal a strong tendency towards concurrent antiferromagnetic ordering. The charge, spin, and associated lattice instabilities are significantly suppressed with increasing external pressure, contributing to the emergence of superconductivity in pressurized La$_3$Ni$_2$O$_7$. Carrier doping is found to effectively suppress these instabilities, suggesting a viable strategy to stabilize a superconducting phase under ambient pressure

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant reconstruction score consisting on discrimination and reconstruction loss is taken into account. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.Comment: 7 pages,6 figure

Overexpression of luxS

LuxS/AI-2 quorum sensing (QS) system involves the production of cell signaling molecules via luxS-based autoinducer-2 (AI-2). LuxS has been reported to plays critical roles in regulating various behaviors of bacteria. AI-2 is a byproduct of the catabolism of S-adenosylhomocysteine (SAH) performed by the LuxS and Pfs enzymes. In our previous study, the function of LuxS in AI-2 production was verified in Streptococcus suis (SS). Decreased levels of SS biofilm formation and host-cell adherence as well as an inability to produce AI-2 were observed in bacteria having a luxS mutant gene. In this study, the level of AI-2 activity exhibits a growth-phase dependence with a maximum in late exponential culture in SS. An SS strain that overexpressed luxS was constructed to comprehensively understand the function of AI-2. Overexpressed luxS was not able to increase the level of pfs expression and produce additional AI-2, and the bacteria were slower growing and produced only slightly more biofilm than the wild type. Thus, AI-2 production is not correlated with luxS transcription. luxS expression is constitutive, but the transcription of pfs is perhaps correlated with AI-2 production in SS

Placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia

Objectives: This study aimed to investigate placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia and discuss about its clinical significance. Material and methods: mRNA expression levels of acetylcholine (AChE), alpha-7 nicotinic acetylcholine receptor (α7nAChR) and nuclear factor-kB (NF-κB) in placenta were detected by qRT-PCR, and protein levels were determined by immunohis­tological analysis and Western Blot in 35 women with preeclampsia (including 20 cases of mild preeclampsia and 15 cases of severe preeclampsia) and 30 cases in control group, respectively. Results: The expression of AChE mRNA and protein in placenta increased significantly in patients with preeclampsia compared with the control group (p < 0.01). It was lower in patients with severe preeclampsia than in patients with mild preeclampsia (p < 0.05). The expression of α7nAChR mRNA and protein in placenta decreased significantly in patients with preeclampsia compared with the control group (p < 0.01). However, the expression of α7nAChR mRNA and protein in patients with severe preeclampsia was higher than that in patients with mild preeclampsia, without significant difference(p > 0.05). The expression of NF-κB protein in placenta decreased significantly in patients with preeclampsia compared with the control group(p < 0.01). It was higher in patients with severe preeclampsia than in patients with mild preeclampsia (p < 0.05), but there was no significant difference between preeclampsia group and control group in the expression of NF-κB mRNA in placenta (p > 0.05). The results of Western blotting assay were consistent with those of immunohistochemistry. Conclusions: Abnormal expression of AChE, α7nAChR and NF-κB in placenta may be associated with preeclampsia. Cho­linergic anti-inflammatory pathway may play an important role in the pathogenesis of preeclampsia