Explore the vast range of titles available.

Refractory high-entropy alloys (HEAs) are a class of emerging multi-component alloys, showing superior mechanical properties at elevated temperatures and being technologically interesting. However, they are generally brittle at room temperature, fail by cracking at low compressive strains and suffer from limited formability. Here we report a strategy for the fabrication of refractory HEA thin films and small-sized pillars that consist of strongly textured, columnar and nanometre-sized grains. Such HEA pillars exhibit extraordinarily high yield strengths of ∼10 GPa—among the highest reported strengths in micro-/nano-pillar compression and one order of magnitude higher than that of its bulk form—and their ductility is considerably improved (compressive plastic strains over 30%). Additionally, we demonstrate that such HEA films show substantially enhanced stability for high-temperature, long-duration conditions (at 1,100 °C for 3 days). Small-scale HEAs combining these properties represent a new class of materials in small-dimension devices potentially for high-stress and high-temperature applications. Refractory high-entropy alloys show promising mechanical properties at elevated temperatures, but are generally brittle at room temperature. Here, the authors observe an improved ductility and yield strength in high-entropy alloy micropillars consisting of nanometre-sized grains that also exhibit excellent thermal stability.

Ketamine, an N -methyl- d -aspartate receptor (NMDAR) antagonist 1 , has revolutionized the treatment of depression because of its potent, rapid and sustained antidepressant effects 2 – 4 . Although the elimination half-life of ketamine is only 13 min in mice 5 , its antidepressant activities can last for at least 24 h 6 – 9 . This large discrepancy poses an interesting basic biological question and has strong clinical implications. Here we demonstrate that after a single systemic injection, ketamine continues to suppress burst firing and block NMDARs in the lateral habenula (LHb) for up to 24 h. This long inhibition of NMDARs is not due to endocytosis but depends on the use-dependent trapping of ketamine in NMDARs. The rate of untrapping is regulated by neural activity. Harnessing the dynamic equilibrium of ketamine–NMDAR interactions by activating the LHb and opening local NMDARs at different plasma ketamine concentrations, we were able to either shorten or prolong the antidepressant effects of ketamine in vivo. These results provide new insights into the causal mechanisms of the sustained antidepressant effects of ketamine. The ability to modulate the duration of ketamine action based on the biophysical properties of ketamine–NMDAR interactions opens up new opportunities for the therapeutic use of ketamine. The discrepancy between the short half-life of ketamine and its long-lasting effects is due to ketamine being trapped in NMDA receptors, and its release depends on neural activity in the lateral habenula.

The rapid proliferation of Internet of Things (IoT) devices has significantly expanded the network attack surface, necessitating the deployment of advanced AI (artificial intelligence)-based intrusion detection systems (IDS) to bolster IoT security. But existing methods face two significant challenges: (1) Feature redundancy: Current approaches extract numerous flow-level features to learn attack behavior, resulting in high computational complexity and substantial redundant information. (2) Class imbalance: Limited attack traffic samples hinder models from effectively learning attack patterns. However, existing algorithms typically address only one of these issues, overlooking their interconnection. Therefore, we propose a Feature Selection and Large Language Models (LLMs)-based IoT intrusion detection framework (FSLLM). At its core is a multi-stage feature selection algorithm combining Minimum Redundancy Maximum Relevance algorithm (mRMR) and a Pearson Correlation Coefficient (PCC)-improved Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES). This algorithm utilizes the CMA-ES algorithm for feature search while also taking into account the mutual information and collinearity among features, thereby more effectively reducing redundancy features. Subsequently, we employ the selected representative features to fine-tune LLMs and generate additional attack samples. This approach effectively reduces the computational cost of fine-tuning while producing higher-quality samples. Furthermore, we employ Focal Loss (FL) function-improved LightGBM as the classifier to improve detection performance. We evaluate our framework on five IoT intrusion detection datasets: NF-ToN-IoT-v2, NF-UNSW-NB15-v2, NF-BoT-IoT-v2, NF-CSE-CIC-IDS2018-v2, and CIC-ToN-IoT. Experimental results demonstrate that FSLLM achieves comparable or superior accuracy to current state-of-the-art methods while reducing redundant features by over 80%.

Precise control of interlayer spacing and functionality is crucial in two-dimensional material based membrane separation technology. Here we show anion intercalation in protonated graphite phase carbon nitride (GCN) that tunes the interlayer spacing and functions of GCN-based membranes for selective permeation in aqueous/organic solutions. Sulfate anion intercalation leads to a crystalline and amphipathic membrane with an accessible interlayer spacing at ~10.8 Å, which allows high solvent permeability and sieves out the solutes with sizes larger than the spacing. We further extend the concept and illustrate the example of GCN-based chiral membrane via incorporating (1R)-(-)-10-camphorsulfonic anion into protonated GCN layers. The membrane exhibits a molecular weight cutoff around 150 among various enantiomers and highly enantioselective permeation towards limonene racemate with an enantiomeric excess value of 89%. This work paves a feasible way to achieve water purification and chiral separation technologies using decorated laminated membranes. In 2D materials membrane separation technology, control of interlayer spacing and functionality determines the separation performance. Here the authors realize graphitic carbon nitride membranes with anion intercalation into the protonated sheets, achieving (enantio-) selective separation in aqueous and organic solution.

Distributed energy systems encompass a diverse range of generation and storage solutions on the user side, where decentralized management schemes to maximize the overall social welfare are preferred considering their dispersed ownership. However, either security or privacy problems occur in recently proposed schemes. Here we report a decentralized framework leveraging the strengths of blockchain and parallelizable mathematical algorithms to overcome these potential drawbacks. The system owners bid cost functions and operating constraints through masked but coupled management subproblems, which are redistributed by the blockchain to be verifiably solved by competent peers. Such processes are iteratively executed as decisions and shadow prices are exchanged among participants, until an equilibrium is reached. The interactive framework ensures decentralized, privacy-preserving, and secure management of multiple energy sources, and reduces the total cost by 3.0 ~ 7.5% in the test system. Our results benefit the energy prosumers and promote a more active and competitive power grid. Decentralized management of distributed energy sources for lower energy costs is of high interest. Here, authors show how privacy and security concerns are addressed under a decentralization framework through blockchain and parallelizable algorithms.

During previous pandemic outbreaks, medical staff have reported high levels of psychological distress. The aim of the current study was to report a snapshot of the psychological impact of the coronavirus disease 2019 (COVID-19) pandemic and its correlated factors on medical staff in Guangdong, China. On the 2nd and 3rd February 2020, soon after the start of the COVID-19 pandemic, we surveyed medical staff at four hospitals in Guangdong, China, to collect demographic characteristics, Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS-14), and Insomnia Severity Index (ISI) scores. Complete responses were received from 1045 medical staff. Respondents were divided into high- and low-risk groups according to their working environment of contacting with potential or confirmed COVID-19 cases. The proportion of staff with anxiety (55.4% v. 43.0%, p < 0.001) or depression (43.6% v. 36.8%, p = 0.028) was significantly higher in the high-risk group than the low-risk group. The percentage of staff with severe anxiety was similar in the two groups. Doctors were more susceptible to moderate-to-severe depressive symptoms. The high-risk group had higher levels of clinical insomnia (13.5% v. 8.5%, p = 0.011) and were more likely to be in the upper quartile for stress symptoms (24.7% v. 19.3%, p = 0.037) than the low-risk group. Additionally, work experience negatively correlated with insomnia symptoms. It is important for hospitals and authorities to protect both the physical and psychological health of medical staff during times of pandemic, even those with a low exposure risk.

The incidence and disability rate of spinal cord injury (SCI) worldwide are high, imposing a heavy burden on patients. Considerable research efforts have been directed toward identifying new strategies to effectively treat SCI. Governor Vessel electro‐acupuncture (GV‐EA), used in traditional Chinese medicine, combines acupuncture with modern electrical stimulation. It has been shown to improve the microenvironment of injured spinal cord (SC) by increasing levels of endogenous neurotrophic factors and reducing inflammation, thereby protecting injured neurons and promoting myelination. In addition, axons extending from transplanted stem cell‐derived neurons can potentially bridge the two severed ends of tissues in a transected SC to rebuild neuronal circuits and restore motor and sensory functions. However, every single treatment approach to severe SCI has proven unsatisfactory. Combining different treatments—for example, electro‐acupuncture (EA) with adult stem cell transplantation—appears to be a more promising strategy. In this review, we have summarized the recent progress over the past two decades by our team especially in the use of GV‐EA for the repair of SCI. By this strategy, we have shown that EA can stimulate the nerve endings of the meningeal branch. This would elicit the dorsal root ganglion neurons to secrete excess amounts of calcitonin gene‐related peptide centrally in the SC. The neuropeptide then activates the local cells to secrete neurotrophin‐3 (NT‐3), which mediates the survival and differentiation of donor stem cells overexpressing the NT‐3 receptor, at the injury/graft site of the SC. Increased local production of NT‐3 facilitates reconstruction of host neural tissue such as nerve fiber regeneration and myelination. All this events in sequence would ultimately strengthen the cortical motor‐evoked potentials and restore the motor function of paralyzed limbs. The information presented herein provides a basis for future studies on the clinical application of GV‐EA and adult stem cell transplantation for the treatment of SCI. A schematic diagram showing that EA stimulates the peripheral nerve endings of the meningeal branch, which transmits information to the SC through nerve fibers of neurons in the DRG. EA can promote the survival and differentiation of transplanted stem cell‐derived neurons or neuron‐like cells in injured SC by inducing the secretion of neurotrophin‐3 by host neurons, which promotes their integration into existing neural circuits.

ObjectivesEmergency department physicians and nurses are at high risk of compassion fatigue, burnout and depression. The purpose of this study was to examine the inter-relationship between compassion fatigue, burnout, compassion satisfaction and depression in emergency department physicians and nurses.DesignA cross-sectional study.SettingThis study was conducted in five tertiary hospitals in five different cities across the province of Sichuan, China, in 2021.ParticipantsA total of 342 emergency department physicians and nurses participated in the study.Main outcome measuresCompassion fatigue, burnout, compassion satisfaction and depression scores.ResultsAmong the study participants, 100% were found to have depressive symptoms, 27.8% had low compassion satisfaction, 2.3% had high burnout and 3.8% had compassion fatigue. In the final multiple linear regression model, marital status (p=0.008; 95% CI –5.205 to –0.789), history of chronic disease (p=0.003; 95% CI –6.461 to –1.386), compassion satisfaction (p<0.001; 95% CI 0.593 to 1.274), burnout (p=0.019; 95% CI 0.084 to 0.930) and compassion fatigue (p<0.001; 95% CI –1.527 to –1.053) among emergency department physicians and nurses were considered to be significant predictors of depression.ConclusionsThe prevalence of depression among emergency department physicians and nurses is high in the province of Sichuan, China. Compassion fatigue, burnout and compassion satisfaction were significantly associated with depression in emergency department physicians and nurses. Hospital administrations should consider these findings to develop appropriate psychological interventions and strategies, to prevent, alleviate or treat severe depression among emergency department physicians and nurses in the province of Sichuan.

Several studies show that the immunosuppressive drugs targeting the interleukin-6 (IL-6) receptor, including tocilizumab, ameliorate lethal inflammatory responses in COVID-19 patients infected with SARS-CoV-2. Here, by employing single-cell analysis of the immune cell composition of two severe-stage COVID-19 patients prior to and following tocilizumab-induced remission, we identify a monocyte subpopulation that contributes to the inflammatory cytokine storms. Furthermore, although tocilizumab treatment attenuates the inflammation, immune cells, including plasma B cells and CD8 + T cells, still exhibit robust humoral and cellular antiviral immune responses. Thus, in addition to providing a high-dimensional dataset on the immune cell distribution at multiple stages of the COVID-19, our work also provides insights into the therapeutic effects of tocilizumab, and identifies potential target cell populations for treating COVID-19-related cytokine storms. Tocilizumab has been used to treat the excessive inflammatory responses in COVID-19 patients. Here, the authors use single-cell RNA sequencing results from two severe COIVD-19 patients to provide high-dimensional immune profiling data, and to implicate potential cellular and molecular insights for the therapeutic effects of tocilizumab.

Tactile sensing plays an important role for robots’ perception, but the existing tactile technologies have multiple limitations. Visual-tactile sensor (VTS) is a newly developed tactile detector; it perceives the contacting surface shape, or even more refined texture, by way of the contact deformation image captured by a camera. A conventional visual perception is usually formulated as a data processing. It suffers issues of cumbersome training set and complicated calibration procedures. A novel model-based depth perceptual scheme is proposed where a mapping from the image intensity to the contact geometry is mathematically formulated with an associated tailored fast solver. The hardware calibration requires single image only, leading to an outstanding algorithmic robustness. The non-uniformity of the illumination condition is embodied by the stereo model, resulting in a robust depth perception precision. Compression tests on a prototype VTS showed the method’s capability in high-quality geometry reconstruction. Both contacting shape and texture were captured at a root-mean-square error down to a sub-millimeter level. The feasibility of the proposed in a pose estimation application is further experimentally validated. The associated tests yielded estimation errors that were all less than 3° in terms of spatial orientation and all less than 1mm in terms of translation.