Unrevealing hardening and strengthening mechanisms in high-entropy ceramics from lattice distortion

Revealing the hardening and strengthening mechanisms is crucial for facilitating the design of superhard and high-strength high-entropy ceramics (HECs). Here, we take high-entropy diborides (HEB$_2$) as the prototype to thoroughly investigate the hardening and strengthening mechanisms of HECs. Specifically, the equiatomic 4- to 9-cation single-phase HEB$_2$ ceramics (4-9HEB$_2$) are fabricated by an ultra-fast high-temperature sintering method. The as-fabricated 4-9HEB$_2$ samples possess similar grain sizes, comparable relative densities (up to ~98%), uniform compositions, and clean grain boundaries without any impurities. The experimental results show that the hardness and flexural strength of the as-fabricated 4-9HEB$_2$ samples have an increasing tendency with the increase of metal components. The first-principles calculations find that lattice distortion is essential to the hardness and strength of HEB$_2$. With the increase of metal components, an aggravation of lattice distortion accompanied by B-B bond strengthening is determined, resulting in the enhancement of the hardness and flexural strength. Moreover, the correlation between other potential indicators and the hardness/flexural strength of HEB$_2$ has been disproved, including valence electron concentration, electronegativity mismatch, and metallic states. Our results unravel the hardening and strengthening mechanisms of HECs by intensifying lattice distortion, which may provide guidance for developing superhard and high-strength HECs

Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering

As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applications, since different views usually contain diverse statistic properties. To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation Learning (TISRL) for multi-view clustering in this paper. Concretely, the rank preserving decomposition is proposed firstly to effectively deal with the diverse statistic information contained in different views. Then, to achieve the intrinsic subspace representation, the tensor-singular value decomposition based low-rank tensor constraint is also utilized in our method. It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint. The objective function can be optimized by an augmented Lagrangian multiplier based alternating direction minimization algorithm. Experimental results on nine common used real-world multi-view datasets illustrate the superiority of TISRL

Ultralow Loss Coupling Tuning of Photonic Accelerators

By leveraging the high propagation speed and inherent parallelism of light, hardware accelerators based on photonic integrated circuits enable high‐speed, low‐power computing, positioning them as promising solutions to meet the rapidly increasing computational demands driven by advancements in artificial intelligence (AI). Within photonic accelerators directional couplers are crucial components for splitting and combining light, facilitating parallel computation and addition operations. However, fabrication imperfections frequently cause deviations in the coupling ratio from its intended value, significantly impairing accelerator performance. This study demonstrates a scalable and nonvolatile approach to flexibly adjust the coupling ratio of fabricated directional couplers by strategically placing polymer patches around their waveguides. This method introduces exceptionally low insertion loss of ≈0.01 dB and can effectively adapt directional couplers with varying initial coupling ratios. This method is applied to a photonic crossbar array, substantially reducing the fabrication‐induced power discrepancy among output ports from 389% to just 8%. This approach presents an innovative strategy for efficiently compensating fabrication errors in integrated photonic circuits

Expression, Purification and Activity Analysis of Proteus vulgaris Phage Lys66

Objective: The gene cloning, protein expression, purification and activity analysis of a new type of Proteus vulgaris bacteriophage lyase Lys66 were performed. Methods: The whole gene sequence of bacteriophage was compared in the Genbank database. The gene sequence of lysase was excavated and cloned. The protein was expressed in Escherichia coli and was further purified to explore its antibacterial effect. Results: A gene sequence with high similarity to lyase was discovered through comparison, with a size of 393 bp. By using ExPAsy Bioinformatics Resource Portal, the lyase was predicted that its molecular weight was 15.20 kDa, the isoelectric point was 9.40, and it was composed of 130 amino acids. The whole optimized synthetic gene was constructed onto vector pET-32α to obtain the recombinant plasmid pET-32α-Lys66. The recombinant plasmid was transferred into competent cells of E. coli BL21 (DE3) to induce its expression. After purification and validation, 1.86 mg/mL Lys66 protein was obtained. The diameter of the bacteriostatic ring of Lys66 lyase on the plate was 19.30 mm. Thirteen Gram-negative bacteria out of 15 tested strains treated with chloroform showed lytic activity, with a wide host spectrum. When Lys66 (1.89 mg/mL) was used in combination with ethylene diamine tetraacetic acid (1 mmol/L), the OD600 nm decreased by 0.61 after 2 h, indicating a good antibacterial effect. Conclusion: The recombinant lysase Lys66 expressed in this study had good antibacterial effects and could be used as a potential antibacterial agent

AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator

Artificial intelligence has significantly advanced healthcare, particularly through large language models (LLMs) that excel in medical question answering benchmarks. However, their real-world clinical application remains limited due to the complexities of doctor-patient interactions. To address this, we introduce \textbf{AI Hospital}, a multi-agent framework simulating dynamic medical interactions between \emph{Doctor} as player and NPCs including \emph{Patient}, \emph{Examiner}, \emph{Chief Physician}. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation (MVME) benchmark, utilizing high-quality Chinese medical records and NPCs to evaluate LLMs' performance in symptom collection, examination recommendations, and diagnoses. Additionally, a dispute resolution collaborative mechanism is proposed to enhance diagnostic accuracy through iterative discussions. Despite improvements, current LLMs exhibit significant performance gaps in multi-turn interactions compared to one-step approaches. Our findings highlight the need for further research to bridge these gaps and improve LLMs' clinical diagnostic capabilities. Our data, code, and experimental results are all open-sourced at \url{https://github.com/LibertFan/AI_Hospital}.Comment: https://github.com/LibertFan/AI_Hospita

Incidence and factors associated of early non-response in first-treatment and drug-naïve patients with schizophrenia: a real-world study

BackgroundSchizophrenia is a severe and persistent mental condition that causes disability. For subsequent clinical care, it is extremely practical to effectively differentiate between patients who respond to therapy quickly and those who do not. This study set out to document the prevalence and risk factors for patient early non-response.MethodsThe current study included 143 individuals with first-treatment and drug-naïve (FTDN) schizophrenia. Patients were classified as early non-responders based on a Positive and Negative Symptom Scale (PANSS) score reduction of less than 20% after 2 weeks of treatment, otherwise as early responders. Clinical subgroups’ differences in demographic data and general clinical data were compared, and variables related to early non-response to therapy were examined.ResultsTwo weeks later, a total of 73 patients were described as early non-responders, with an incidence of 51.05%. The early non-response subgroup had significantly higher PANSS scores, Positive symptom subscale (PSS) scores, General psychopathology subscale (GPS) scores, Clinical global impression scale - severity of illness (CGI-SI) and Fasting blood glucose (FBG) levels compared to the early-response subgroup. CGI-SI and FBG were risk factors for early non-response.ConclusionHigh rates of early non-response have been seen in FTDN schizophrenia patients, and risk variables for predicting early non-response include CGI-SI scores and FBG levels. However, we need more in-depth studies to confirm the generalizable range of these two parameters

Understanding the relationship between HCV infection and progression of kidney disease

Hepatitis C virus (HCV) can cause a range of kidney diseases. HCV is the primary cause of mixed cryoglobulinaemia, which leads to cryoglobulinaemic vasculitis and cryoglobulinaemic glomerulonephritis (GN). Patients with acute cryoglobulinaemic vasculitis often exhibit acute kidney disease due to HCV infection, which typically progresses to acute kidney injury (AKI). HCV also increases the risk of chronic kidney disease (CKD) and the likelihood of developing end-stage renal disease (ESRD). Currently, direct-acting antiviral agents (DAAs) can be used to treat kidney disease at different stages. This review focuses on key findings regarding HCV and kidney disease, discusses the impact of DAAs, and highlights the need for further research and treatment