Spin transfer nano-oscillators

The use of spin transfer nano-oscillators (STNOs) to generate microwave signal in nanoscale devices have aroused tremendous and continuous research interest in recent years. Their key features are frequency tunability, nanoscale size, broad working temperature, and easy integration with standard silicon technology. In this feature article, we give an overview of recent developments and breakthroughs in the materials, geometry design and properties of STNOs. We focus in more depth on our latest advances in STNOs with perpendicular anisotropy showing a way to improve the output power of STNO towards the {\mu}W range. Challenges and perspectives of the STNOs that might be productive topics for future research were also briefly discussed.Comment: 11 pages, 10 figures, nanoscale 201

An empirical study on factors affecting purchase intention of cross-border ecommerce consumer in post-pandemic era

The outbreak of COVID-19 promoted the further development of the Cross-border E-commerce (CBEC) industry worldwide. Due to lockdown and home quarantine policies, many countries across the globe followed the complete closure of shopping malls, transport networks, schools, universities, etc. This study aims to investigate factors that influence purchase intention of consumers in CBEC in post-pandemic era. Stimulus-Organism-Response (SOR) model, along with Howard-Sheth Model of consumer behavior and Technology Acceptance Model (TAM) has been employed to develop a structural model for the research. An empirical study including 322 copies of questionnaire were collected and were analyzed by SPSSAU. It has been found that input stimuli such as significant stimuli, symbol stimuli, social stimuli affect personal perception, while personal perception such as perceived risk, perceived usefulness and perceived ease of use affect purchase intention negatively or positively. Based on the study, suggestions were put forward for CBEC platform for further development

Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies

Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including 'Sphingoid metabolic process,' 'Ubiquitin protein ligase activity,' 'Regulation of cytokine secretion,' and 'Ceramide metabolic process.' These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data. © 2013 Wang et al

Reduced neural responses to reward reflect anhedonia and inattention: an ERP study

An inhibited neural response to reward is typical of clinical depression and can predict an individual's overall depressive symptoms. However, the mechanism underlying this are unclear. Previous studies have found that anhedonia and inattention may mediate the relationship between reward sensitivity and depressive symptoms. Therefore, this study aimed to verify the relationship between reward sensitivity and overall depressive symptoms in a depressive tendency sample as well as to explore the mechanism underlying the ability of neural responses to reward to predict overall depressive symptoms via a mediation model. Sixty-four participants (33 with depressive tendencies and 31 without; dichotomized by BDI-II) finished simple gambling tasks while their event-related potential components (ERPs) were recorded and compared. Linear regression was conducted to verify the predictive effect of ERPs on overall depressive symptoms. A multiple mediator model was used, with anhedonia and distractibility as mediators reward sensitivity and overall depressive symptoms. The amplitude of reward positivity (ΔRewP) was greater in healthy controls compared to those with depressive tendencies (p = 0.006). Both the gain-locked ERP component (b = − 1.183, p = 0.007) and the ΔRewP (b = − 0.991, p = 0.024) could significantly negatively predict overall depressive symptoms even after controlling for all anxiety symptoms. The indirect effects of anhedonia and distractibility were significant (both confidence intervals did not contain 0) while the direct effect of reward sensitivity on depressive symptom was not significant (lower confidence interval = − 0.320, upper confidence interval = 0.065). Individuals with depressive tendencies display impaired neural responses to reward compared to healthy controls and reduced individual neural responses to reward may reflect the different biotypes of depression such as anhedonia and inattention.publishedVersio

Oral administration of interferon-α2b-transformed Bifidobacterium longum protects BALB/c mice against coxsackievirus B3-induced myocarditis

Multiple reports have claimed that low-dose orally administered interferon (IFN)-α is beneficial in the treatment of many infectious diseases and provides a viable alternative to high-dose intramuscular treatment. However, research is needed on how to express IFN stably in the gut. Bifidobacterium may be a suitable carrier for human gene expression and secretion in the intestinal tract for the treatment of gastrointestinal diseases. We reported previously that Bifidobacterium longum can be used as a novel oral delivery of IFN-α. IFN-transformed B. longum can exert an immunostimulatory role in mice; however the answer to whether this recombinant B. longum can be used to treat virus infection still remains elusive. Here, we investigated the efficacy of IFN-transformed B. longum administered orally on coxsackie virus B3 (CVB3)-induced myocarditis in BALB/c mice. Our data indicated that oral administration of IFN-transformed B. longum for 2 weeks after virus infection reduced significantly the severity of virus-induced myocarditis, markedly down regulated virus titers in the heart, and induced a T helper 1 cell pattern in the spleen and heart compared with controls. Oral administration of the IFN-transformed B. longum, therefore, may play a potential role in the treatment of CVB3-induced myocarditis

One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks

Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into artificial neural networks. Recently proposed quadratic neuron, which replaces the inner-product operation in conventional neurons with a quadratic one, have achieved great success in many essential tasks. Despite the promising results of quadratic neurons, there is still an unresolved issue: \textit{Is the superior performance of quadratic networks simply due to the increased parameters or due to the intrinsic expressive capability?} Without clarifying this issue, the performance of quadratic networks is always suspicious. Additionally, resolving this issue is reduced to finding killer applications of quadratic networks. In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability. This intrinsic expressive ability comes from that quadratic neurons can easily represent nonlinear interaction, while it is hard for conventional neurons. Theoretically, we derive the approximation efficiency of the quadratic network over conventional ones in terms of real space and manifolds. Moreover, from the perspective of the Barron space, we demonstrate that there exists a functional space whose functions can be approximated by quadratic networks in a dimension-free error, but the approximation error of conventional networks is dependent on dimensions. Empirically, experimental results on synthetic data, classic benchmarks, and real-world applications show that quadratic models broadly enjoy parametric efficiency, and the gain of efficiency depends on the task.Comment: We have shared our code in https://github.com/asdvfghg/quadratic_efficienc

The interface states in gate-all-around transistors (GAAFETs)

The atomic-level structural detail and the quantum effects are becoming crucial to device performance as the emerging advanced transistors, representatively GAAFETs, are scaling down towards sub-3nm nodes. However, a multiscale simulation framework based on atomistic models and ab initio quantum simulation is still absent. Here, we propose such a simulation framework by fulfilling three challenging tasks, i.e., building atomistic all-around interfaces between semiconductor and amorphous gate-oxide, conducting large-scale first-principles calculations on the interface models containing up to 2796 atoms, and finally bridging the state-of-the-art atomic level calculation to commercial TCAD. With this framework, two unnoticed origins of interface states are demonstrated, and their tunability by changing channel size, orientation and geometry is confirmed. The quantitative study of interface states and their effects on device performance explains why the nanosheet channel is preferred in industry. We believe such a bottom-up framework is necessary and promising for the accurate simulation of emerging advanced transistors

A spintronic Huxley-Hodgkin-analogue neuron implemented with a single magnetic tunnel junction

Spiking neural networks aim to emulate the brain's properties to achieve similar parallelism and high-processing power. A caveat of these neural networks is the high computational cost to emulate, while current proposals for analogue implementations are energy inefficient and not scalable. We propose a device based on a single magnetic tunnel junction to perform neuron firing for spiking neural networks without the need of any resetting procedure. We leverage two physics, magnetism and thermal effects, to obtain a bio-realistic spiking behavior analogous to the Huxley-Hodgkin model of the neuron. The device is also able to emulate the simpler Leaky-Integrate and Fire model. Numerical simulations using experimental-based parameters demonstrate firing frequency in the MHz to GHz range under constant input at room temperature. The compactness, scalability, low cost, CMOS-compatibility, and power efficiency of magnetic tunnel junctions advocate for their broad use in hardware implementations of spiking neural networks.Comment: 23 pages, 6 figures, 2 table