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Byline: Haodong. Liao, Yuxin. Deng A 58-year-old female presented to the hospital with complaints of hearing loss, tinnitus, and dizziness. The complaints started 3 years prior with slow progression. The [...]

Byline: Yuxin. Deng, Mingguo. Xie, Lichuan. Zeng A 53-year-old man initially presented with a 1-year history of dizziness and whole-body involuntary tremor. Physical examination was notable for a mild bilateral [...]

Byline: Yuxin. Deng, Mingguo. Xie, Lichuan. Zeng A 64-year-old male was admitted to our hospital due to recurrent headache, cough, and wheezing. He had a past medical history of dizziness [...]

Level 3 automated vehicles (L3 AVs) enable the driver to perform non-driving tasks, taking over in an emergency. In recent years, studies have extensively discussed the influencing factors of L3 AV takeovers. Extensive literature review shows that L3 AV takeovers are affected by human factors, traffic environment, and automatic driving systems. On this basis, this study proposes a conceptual framework of L3 AV takeovers. The main findings of this study include the following: (1) non-driving tasks, non-driving posture, individual characteristics, and trust have an impact on takeover behavior; (2) high traffic density, poor road geometry, and extreme weather have a negative impact on the takeover; (3) multimodal interaction design can improve collection performance. Although the existing research has made rich achievements, there are still many challenges. The influence of human factors on takeover performance is controversial, the quantification standard of takeover influencing factors is insufficient, and the prediction accuracy needs to be improved. It is suggested to refine the criteria of driver participation in NDRT, formulate an effective measurement standard of driver fatigue, and develop a takeover prediction model combining driver status and traffic environment conditions. It provides a research basis for the formulation of laws, infrastructure construction, and human–computer interaction design. [ABSTRACT FROM AUTHOR]

Gallium Oxide (Ga2O3) devices have shown great potential for radiation detection. However, developing Ga2O3 detectors for pulsed radiation monitoring is still challenging, which requires high response sensitivity (R), low noise, high time resolution, and linear outputs. Herein, a vertical p‐NiO/β‐Ga2O3 heterojunction diode (HJD) is fabricated and its performance in X‐ray detection is analyzed. Benefiting from the high quality of the p‐n heterojunction, the HJD exhibits a low‐leakage current density of less than 1.73 × 10−7 A cm−2 at 100 V and features a high R of 7.92 μC Gy−1 cm−2, a low noise equivalent dose rate of 9.14 × 10−12 Gy−1 Hz−0.5 at an X‐ray dose rate of 0.383 Gy s−1, and good linear outputs to X‐ray dose rates from 0.0383 to 1.149 Gy s−1. The conductive process of the HJD detector is dominated by the tunneling mechanism, which leads to a longer response time associated with the trapping and releasing process of the deep‐level traps under the continuous X‐ray illumination. However, the pulsed X‐ray response property shows no significant influence from the slow trapping/releasing process of the traps. As a result, pulsed X‐ray radiation detection with a 50 ns full width is achieved, and the time resolution of the detector is characterized to be 3.31 ns by a 20 ps laser source. [ABSTRACT FROM AUTHOR]

Myocyte enhancer factor 2C (MEF2C) is highly expressed in the nervous system, and regulates neuro-development, synaptic plasticity, and inflammation. However, its mechanism in Alzheimer’s disease (AD) is underestimated. In this study, the role and mechanism of MEF2C were investigated in the brain tissue specimens from patients with AD, APPswe/PSEN1dE9 double transgenic (APP/PS1_DT) mice, and SH-SY5Y cells treated with β-amyloid peptide (Aβ). The results indicated that the expression of MEF2C is significantly reduced, and the expression of MEF2C/Aβ in different parts of brain is negatively correlated in patients with AD. Knockdown of MEF2C promotes cell apoptosis and the level of β-amyloid precursor protein cleaving enzyme 1 (BACE) but reduces BACE2 expression. In addition, knockdown of Mef2c enhances the generation and aggregation of Aβ in the cortex of APP/PS1_DT mice, reduces the expression of synaptic proteins, exacerbates the ability of learning and memory of APP/PS1_DT mice, damages the structure of mitochondria, increases the oxidative stress (OS) level, and inhibits the expression levels of members of the Nrf2-ARE signal pathway. In summary, inhibition of MEF2C exacerbates the toxic effect of Aβ in vivo and in vitro, damages synaptic plasticity, reduces the ability of learning and memory of APP/PS1 mice, and increases the level of OS via the Nrf2-ARE signal pathway.

In order to visualize the content and development patterns of microplastic biodegradation research, the American Chemical Society (ACS), Elsevier, Springer Link, and American Society for Microbiology (ASM) were searched for the years 2012–2022 using Citespace and VOSvivewer for bibliometrics and visual analysis. The biodegradation processes and mechanisms of microplastics were reviewed on this basis. The results showed a sharp increase in the number of publications between 2012 and 2022, peaking in 2020–2021, with 62 more publications than the previous decade. The University of Chinese Academy of Sciences (UCAS), Northwest A&F University (NWAFU), and Chinese Academy of Agricultural Sciences (CAAS) are the top three research institutions in this field. Researchers are mainly located in China, The United States of America (USA), and India. Furthermore, the research in this field is primarily concerned with the screening of functional microorganisms, the determination of functional enzymes, and the analysis of microplastic biodegradation processes and mechanisms. These studies have revealed that the existing functional microorganisms for microplastic biodegradation are bacteria, predominantly Proteobacteria and Firmicutes; fungi, mainly Ascomycota; and some intestinal microorganisms. The main enzymes secreted in the process are hydrolase, oxidative, and depolymerization enzymes. Microorganisms degrade microplastics through the processes of colonization, biofilm retention, and bioenzymatic degradation. These studies have elucidated the current status of and problems in the microbial degradation of microplastics, and provide a direction for further research on the degradation process and molecular mechanism of functional microorganisms. [ABSTRACT FROM AUTHOR]

Soil microplastic pollution is currently a worldwide concern. Microplastics are organic pollutants that are abundant in the natural environment, are persistent and difficult to degrade and may endanger human health while harming the environment. This article offers a bibliometric analysis of the environmental behaviour of microplastics in soils, as well as a thorough statistical analysis of research goals and trends in this field. We conducted a thorough search of all relevant literature from 2012 to 2022 in the Web of Science core database. The data analysis shows that, starting in 2012, there has been an upward trend in the number of articles about soil microplastic pollution. It can also be seen that China is relatively ahead of the curve in this area of research, followed by the United Kingdom and the United States. This article also systematically describes the research hotspots in this field. The results show that the current research on soil microplastics is mainly focused on their identification, enrichment and toxicity, whereas studies on the migration and transformation of soil microplastics and the mechanism of interaction with other pollutants are still lacking. Our results provide ideas and prospects for future research in this field. [ABSTRACT FROM AUTHOR]

In order to verify the functional correctness of quantum circuits or algorithms, a prominent approach is to specify them as quantum programs and semi-automatically deduce them in a theorem prover. It is indispensable to first formalize the semantics of the basic quantum language. We formalize in Coq an imperative language which allows for classical and quantum information interactions. We define small-step operational semantics and state-based denotational semantics. Then we prove a consistency theorem between these two semantics. A distribution-based denotational semantics is also defined and related to the state-based one. Finally, we describe a few typical quantum algorithms and utilize the distribution-based denotational semantics to verify their correctness. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution was applied to Xception to achieve lightweighting, and Inception-ResNet introduces residual connections to accelerate model convergence. However, existing improvements for the Inception module often neglect further enhancement of its receptive field, while increasing the receptive field of CNNs has been widely studied and proven to be effective in improving classification performance. Motivated by this fact, three effective expansion modules are proposed in this paper. The first expansion module, Inception expand (Inception-e) module, is proposed to improve the classification accuracy by concatenating more and deeper convolutional branches. To reduce the number of parameters for Inception e, this paper proposes a second expansion module—Equivalent Inception-e (Eception) module, which is equivalent to Inception-e in terms of feature extraction capability, but which suppresses the growth of the parameter quantity brought by the expansion by effectively reducing the redundant convolutional layers; on the basis of Eception, this paper proposes a third expansion module—Lightweight Eception (Lception) module, which crosses depthwise convolution with ordinary convolution to further effectively reduce the number of parameters. The three proposed modules have been validated on the Cifar10 dataset. The experimental results show that all these extensions are effective in improving the classification accuracy of the models, and the most significant effect is the Lception module, where Lception (rank = 4) on the Cifar10 dataset improves the accuracy by 1.5% compared to the baseline model (Inception module A) by using only 0.15 M more parameters. [ABSTRACT FROM AUTHOR]

This study investigates the broad-energy-spectrum reactor-neutron irradiation effects on the electrical characteristics of Ni/β-Ga2O3 Schottky barrier diodes (SBDs), where the irradiated neutron fluence was up to 1 × 1016 cm−2. On the one hand, the high neutron fluence of 1016 cm−2 resulted in a reduction in forward current density by two orders of magnitude and an extremely high on-resistance property due to the radiation-generated considerable series resistance in the SBD. On the other hand, the irradiation brought little influence on the Ni/β-Ga2O3 Schottky contact, since the extracted ideality factor and barrier height from temperature-dependent current–voltage (I–V–T) characteristics showed no significant changes after the radiation. Moreover, the capacitance–voltage (C–V) characterization revealed that the net carrier density in the β-Ga2O3 material was only reduced by 25% at the neutron fluence of 1015 cm−2 but a significant reduction by 2–3 orders at 1016 cm−2. Within the neutron fluence range of 2 × 1014 cm−2 up to 1016 cm−2, the carrier removal rates trended to be saturated with the increased fluences, following an exponential regular. In addition, the C–V measurement on the 1016 cm−2 irradiated sample exhibited an obvious frequency dispersion, and the extracted carrier distribution was not uniform. [ABSTRACT FROM AUTHOR]

The risks brought by microplastics (MPs) to agricultural soil structure and crop growth in the agricultural system are the focus of global debate. MPs enter the soil through various routes, such as through the use of agricultural mulch and atmospheric deposition. Here, we review the research on MP pollution in the soil during the last 30 years. This review focuses on (i) the sources, types, and distribution characteristics of MPs in agricultural soils; (ii) the migration and transformation of MPs and their interactions with microorganisms, organic matter, and contaminants in agricultural soils; and (iii) the effects of environmental factors on the composition and structure of MPs in agricultural soils. This review also proposes key directions for the future research and management of MPs in the agricultural soil. We aim to provide a theoretical basis for the fine management of agricultural farmland. [ABSTRACT FROM AUTHOR]

The introduction of contaminants of emerging concern (CECs) into the environment has raised concerns due to the significant risks they pose to both ecosystems and human health. In this sys-tematic review, we investigate research trends on CECs worldwide over the past 10 years, focus-ing on four critical aspects: (i) the identification and distribution of typical CECs across various media, (ii) the sources and environmental behavior of CECs, (iii) the implications of CECs expo-sure on human health, and (iv) risk assessment and control measures for CECs. The review re-veals a comprehensive understanding of the typical types and distribution of CECs in different environmental media, shedding light on their prevalence and potential impact on ecosystems. Furthermore, insights into the sources and behavior of CECs provide crucial information for de-vising effective strategies to mitigate their release into the environment. By examining the health effects of EC exposure, we highlight the importance of considering potential risks to human well-being. This aspect of the review emphasizes the significance of monitoring and managing CECs to safeguard public health. The review also synthesizes the advancements in risk assessment methodologies and control measures for CECs, which are essential for developing comprehensive regulations and guidelines to manage these contaminants effectively. Drawing from the findings, we identify future research directions for CECs in aquatic environments. [ABSTRACT FROM AUTHOR]

Alpha particle detection has been achieved using a NiO/β-Ga2O3 heterojunction diode (HJD) detector, demonstrating the great potential of β-Ga2O3 devices for ionizing particle detection. Featuring a large area (700 × 700 μm2) and a low reverse leakage current (10−10 A), the HJD detector exhibited apparent energy spectra of 239Pu-generated alpha particles with an energy of 5.155 MeV at various bias voltages from −20 to −150 V. The peak energy of the energy spectra increased with the bias voltages. In contrast, the full width at half-maximum of the spectra was almost unchanged at high bias voltages, leading to an increased energy resolution with the increase in the bias voltages. The detector's charge collection efficiency (CCE) and energy resolution reached 14.82% and 17.54% at −150 V, respectively. The compromised CCE and energy resolution were revealed to be limited by the insufficient depletion width of the NiO/β-Ga2O3 HJD. [ABSTRACT FROM AUTHOR]