Explore the vast range of titles available.

The study of kagome materials has recently attracted much attention due to the presence of many electron-electron interaction-driven phases in a single material. In this work, we report the time-reversal symmetry-breaking superconductivity in the thin-flake kagome material RbV 3 Sb 5 . Firstly, when an in-plane magnetic field is swept in opposite directions, we observe an unconventional form of hysteresis in magnetoresistance, which is different from the hysteresis induced by extrinsic mechanisms. In contrast, no such hysteresis is observed in CsV 3 Sb 5 samples below their superconducting transition temperature. Strikingly, at a fixed magnetic field, the finite-resistance state in RbV 3 Sb 5 can be transitioned into the superconducting state by applying and subsequently removing a large current. Secondly, at temperatures around 400 mK, the re-entrance of superconductivity occurs during an in-plane field-sweeping process with a fixed sweeping direction. The observations of the unconventional hysteresis and re-entrance suggest the existence of time-reversal symmetry-breaking superconducting states in RbV 3 Sb 5 . The authors report the time-reversal symmetry breaking superconductivity in the thin-flake kagome material RbV3Sb5, manifesting primarily as an unconventional form of hysteresis that is not observed in CsV3Sb5. They also observe reentrance of superconductivity during an in-plane field-sweeping process at 400 mK.

Lockdown measures including school closures due to COVID-19 may affect youths’ activity patterns and obesity status. This will be for the first time examined in China in this study on the basis of a large national sample from the COVID-19 Impact on Lifestyle Change Survey (COINLICS). Through an online questionnaire, 10,082 participants from high schools, colleges, and graduate schools, aged 19.8 ± 2.3 years, voluntarily reported their lifestyles and weight status before (January 2020) and after lockdown (April–May 2020). The significance of these changes was assessed between sexes and across education levels. We found that the youths’ average body mass index significantly increased from 21.8 to 22.1 kg/m2, with the prevalences of overweight/obesity and obesity increasing from 21.4% to 24.6% and from 10.5% to 12.6%, respectively. Also, significant decreases were seen in the frequency of engaging in active transport, moderate-/vigorous-intensity housework, leisure-time moderate-/vigorous-intensity physical activity, and leisure-time walking, while significant increases were observed in the average sedentary time during workdays and weekends, the average sleeping time during workdays and weekends, and screen time. Our findings would serve as important evidence for shaping global strategies to counteract or reverse the lockdown effects on youths’ obesity.

Immunotherapy, as a powerful strategy for cancer treatment, has achieved tremendous efficacy in clinical trials. Despite these advancements, there is much to do in terms of enhancing therapeutic benefits and decreasing the side effects of cancer immunotherapy. Advanced nanobiomaterials, including liposomes, polymers, and silica, play a vital role in the codelivery of drugs and immunomodulators. These nanobiomaterial-based delivery systems could effectively promote antitumor immune responses and simultaneously reduce toxic adverse effects. Furthermore, nanobiomaterials may also combine with each other or with traditional drugs via different mechanisms, thus giving rise to more accurate and efficient tumor treatment. Here, an overview of the latest advancement in these nanobiomaterials used for cancer immunotherapy is given, describing outstanding systems, including lipid-based nanoparticles, polymer-based scaffolds or micelles, inorganic nanosystems, and others.

Lithium metal is considered the ultimate anode material for future rechargeable batteries1,2, but the development of Li metal-based rechargeable batteries has achieved only limited success due to uncontrollable Li dendrite growth3–7. In a broad class of all-solid-state Li batteries, one approach to suppress Li dendrite growth has been the use of mechanically stiff solid electrolytes8,9. However, Li dendrites still grow through them10,11. Resolving this issue requires a fundamental understanding of the growth and associated electro-chemo-mechanical behaviour of Li dendrites. Here, we report in situ growth observation and stress measurement of individual Li whiskers, the primary Li dendrite morphologies12. We combine an atomic force microscope with an environmental transmission electron microscope in a novel experimental set-up. At room temperature, a submicrometre whisker grows under an applied voltage (overpotential) against the atomic force microscope tip, generating a growth stress up to 130 MPa; this value is substantially higher than the stresses previously reported for bulk13 and micrometre-sized Li14. The measured yield strength of Li whiskers under pure mechanical loading reaches as high as 244 MPa. Our results provide quantitative benchmarks for the design of Li dendrite growth suppression strategies in all-solid-state batteries.Lithium whisker growth and mechanical properties can be studied in situ using a combination of two microscopies.

Pollution by heavy metals limits the area of land available for cultivation of food crops. A potential solution to this problem might lie in the molecular breeding of food crops for phytoremediation that accumulate toxic metals in straw while producing safe and nutritious grains. Here, we identify a rice quantitative trait locus we name cadmium (Cd) accumulation in leaf 1 ( CAL1 ), which encodes a defensin-like protein. CAL1 is expressed preferentially in root exodermis and xylem parenchyma cells. We provide evidence that CAL1 acts by chelating Cd in the cytosol and facilitating Cd secretion to extracellular spaces, hence lowering cytosolic Cd concentration while driving long-distance Cd transport via xylem vessels. CAL1 does not appear to affect Cd accumulation in rice grains or the accumulation of other essential metals, thus providing an efficient molecular tool to breed dual-function rice varieties that produce safe grains while remediating paddy soils. Crops that allocate heavy metals to leaves rather than grains could allow phytoremediation of polluted soil while producing food that is safe to eat. Here, the authors show that a defensin-like protein promotes cadmium secretion from rice cells and allocation to leaves without causing accumulation in grain.

In China, English teachers usually take a three-step approach to grammar teaching, namely, the presentation—practice—production principle (3P Principle). This method consists of both \"morphology\" and \"syntax.\" It adopts the traditional mode of grammar teaching, including knowledge presentation, grammar exercises, and grammatical competence production, which will inevitably lead to time consumption and low efficiency in grammar learning. This study is about cultivating English majors' grammatical competence through the theoretical mode of grammar teaching. The research results show that the theoretical mode of grammar teaching helps cultivate learners' grammatical competence.

With the increasing prevalence of obesity, there is a growing awareness of its impact on infectious diseases. In past epidemics of influenza A and Middle East respiratory syndrome (MERS) coronavirus, obesity has been identified as a risk factor influencing the severity of illness in infected persons. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for a large number of deaths and health damages worldwide. Increasing numbers of reports have linked obesity to more severe COVID-19 disease and death. This review focuses on the impact of obesity on patients with COVID-19. We comprehensively analyzed the various mechanisms of obesity affecting the severity of the disease. In addition, on the basis of the vulnerability of people with obesity during the COVID-19 epidemic, we summarized both individual-level and hospital-level prevention and management measures for COVID-19 patients with obesity and discussed the impact of isolation on people with obesity. •We summarize the epidemiological characteristics of COVID-19 patients with obesity.•We provide a systematical review of the impact of obesity on COVID-19 severity.•We analyze various mechanisms via which obesity affects the severity of COVID-19 patients.•We summarize multiple-level management measures for COVID-19 patients with obesity.•We review the impact of isolation on and provide suggestions for people with obesity.

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death and it has been confirmed that increased low density lipoprotein cholesterol (LDL-C) is an independent risk factor for atherosclerosis. Recently, the increasing evidence has showed that hypertriglyceridemia is associated with incremental ASCVD risk. But the proatherogenic mechanism of triglyceride (TG) remains unclear. Therefore, this article focuses on the clinical studies and proatherogenic mechanism related to hypertriglyceridemia, in order to provide reference for the prevention and treatment of ASCVD.

Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzz testing faces many challenges, such as how to mutate input seed files, how to increase code coverage, and how to bypass the format verification effectively. Therefore machine learning techniques have been introduced as a new method into fuzz testing to alleviate these challenges. This paper reviews the research progress of using machine learning techniques for fuzz testing in recent years, analyzes how machine learning improves the fuzzing process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies five different stages in which machine learning has been used. Then this paper systematically studies machine learning-based fuzzing models from five dimensions of selection of machine learning algorithms, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Secondly, this paper assesses the performance of the machine learning techniques in existing research for fuzz testing. The results of the evaluation prove that machine learning techniques have an acceptable capability of prediction for fuzzing. Finally, the capability of discovering vulnerabilities both traditional fuzzers and machine learning-based fuzzers is analyzed. The results depict that the introduction of machine learning techniques can improve the performance of fuzzing. We hope to provide researchers with a systematic and more in-depth understanding of fuzzing based on machine learning techniques and provide some references for this field through analysis and summarization of multiple dimensions.