Slow cooling and efficient extraction of C-exciton hot carriers in MoS2 monolayer

In emerging optoelectronic applications, such as water photolysis, exciton fission and novel photovoltaics involving low-dimensional nanomaterials, hot-carrier relaxation and extraction mechanisms play an indispensable and intriguing role in their photo-electron conversion processes. Two-dimensional transition metal dichalcogenides have attracted much attention in above fields recently; however, insight into the relaxation mechanism of hot electron-hole pairs in the band nesting region denoted as C-excitons, remains elusive. Using MoS2 monolayers as a model two-dimensional transition metal dichalcogenide system, here we report a slower hot-carrier cooling for C-excitons, in comparison with band-edge excitons. We deduce that this effect arises from the favourable band alignment and transient excited-state Coulomb environment, rather than solely on quantum confinement in two-dimension systems. We identify the screening-sensitive bandgap renormalization for MoS2 monolayer/graphene heterostructures, and confirm the initial hot-carrier extraction for the C-exciton state with an unprecedented efficiency of 80%, accompanied by a twofold reduction in the exciton binding energy

Deformation of the Fermi surface in the extended Hubbard model

The deformation of the Fermi surface induced by Coulomb interactions is investigated in the t-t'-Hubbard model. The interplay of the local U and extended V interactions is analyzed. It is found that exchange interactions V enhance small anisotropies producing deformations of the Fermi surface which break the point group symmetry of the square lattice at the Van Hove filling. This Pomeranchuck instability competes with ferromagnetism and is suppressed at a critical value of U(V). The interaction V renormalizes the t' parameter to smaller values what favours nesting. It also induces changes on the topology of the Fermi surface which can go from hole to electron-like what may explain recent ARPES experiments.Comment: 5 pages, 4 ps figure

Energy-efficient precoding in multicell networks with full-duplex base stations

© 2017, The Author(s). This paper considers multi-input multi-output (MIMO) multicell networks, where the base stations (BSs) are full-duplex transceivers, while uplink and downlink users are equipped with multiple antennas and operate in a half-duplex mode. The problem of interest is to design linear precoders for BSs and users to optimize the network’s energy efficiency. Given that the energy efficiency objective is not a ratio of concave and convex functions, the commonly used Dinkelbach-type algorithms are not applicable. We develop a low-complexity path-following algorithm that only invokes one simple convex quadratic program at each iteration, which converges at least to the local optimum. Numerical results demonstrate the performance advantage of our proposed algorithm in terms of energy efficiency

Review: The increasing importance of carbon nanotubes and nanostructured conducting polymers in biosensors

The growing need for analytical devices requiring smaller sample volumes, decreased power consumption and improved performance have been driving forces behind the rapid growth in nanomaterials research. Due to their dimensions, nanostructured materials display unique properties not traditionally observed in bulk materials. Characteristics such as increased surface area along with enhanced electrical/optical properties make them suitable for numerous applications such as nanoelectronics, photovoltaics and chemical/biological sensing. In this review we examine the potential that exists to use nanostructured materials for biosensor devices. By incorporating nanomaterials, it is possible to achieve enhanced sensitivity, improved response time and smaller size. Here we report some of the success that has been achieved in this area. Many nanoparticle and nanofibre geometries are particularly relevant, but in this paper we specifically focus on organic nanostructures, reviewing conducting polymer nanostructures and carbon nanotubes

Receptor-targeted liposome-peptide-siRNA nanoparticles represent a novel and efficient therapeutic approach to prevent conjunctival fibrosis.

There is increasing evidence that the Myocardin-related transcription factor/Serum response factor (MRTF/SRF) pathway plays a key role in fibroblast activation and that knocking down MRTF can lead to reduced scarring and fibrosis. Here, we have developed a receptor-targeted liposome-peptide-siRNA nanoparticle as a non-viral delivery system for MRTF-B siRNA in conjunctival fibrosis. Using 50 nM siRNA, the MRTF-B gene was efficiently silenced by 76% and 72% with LYR and LER nanoparticles, respectively. The silencing efficiency was low when non-targeting peptides or siRNA alone or liposome-siRNA alone were used. LYR and LER nanoparticles also showed higher silencing efficiency than PEGylated LYR-P and LER-P nanoparticles. The nanoparticles were not cytotoxic using different liposomes, targeting peptides, and 50 nM siRNA. Three-dimensional fibroblast-populated collagen matrices were also used as a functional assay to measure contraction in vitro, and showed that MRTF-B LYR nanoparticles completely blocked matrix contraction after a single transfection treatment. In conclusion, this is the first study to develop and show that receptor-targeted liposome-peptide-siRNA nanoparticles represent an efficient and safe non-viral siRNA delivery system that could be used to prevent fibrosis after glaucoma filtration surgery and other contractile scarring conditions in the eye

Does sex matter in the associations between classic risk factors and fatal coronary heart disease in populations from the Asia-Pacific region?

Background: There is much interest in promoting healthy heart awareness among women. However, little is known about the reasons behind the lower rates of heart disease among women compared with men, and why this risk difference diminishes with age. Previous comparative studies have generally had insufficient numbers of women to quantify such differences reliably. Methods: We carried out an individual participant data meta-analysis of 39 cohort studies (32 from Asian countries and 7 from Australia and New Zealand). Cox models were used to estimate hazard ratios (HR) for coronary death, comparing men to women. Further adjustments were made for several proven coronary risk factors to quantify their contributions to the sex differential. Sex interactions were tested for the same risk factors. Results: During 4 million person-years of follow-up, there were 1989 (926 female) deaths from coronary heart disease (CHD). The age-adjusted and study-adjusted male/female HR (95% confidence interval [95% CI]) was 2.05 (1.89-2.22). At baseline, 54% of men vs. 7% of women were current smokers; hence, adjustment for smoking explained the largest component (20%) of this HR. A significant sex interaction was observed between systolic blood pressure (SBP) and CHD mortality such that a 10 mm Hg increase was associated with a 15% greater increase in the relative risk (RR) of coronary death in women compared with men (p = 0.002). Conclusions: Only a small amount of the sex differential in coronary death could be explained by differences in the prevalence of classic risk factors. Alternative explanations are required to explain the age-related attenuation of the sex difference in CHD risk. © Mary Ann Liebert, Inc.published_or_final_versio

AI-Driven Large Language Models in Health Consultations for HIV Patients

Chun-Yan Zhao,1,2,* Chang Song,1,2,* Tong Yang,3,* Ai-Chun Huang,1 Hang-Biao Qiang,1 Chun-Ming Gong,1 Jing-Song Chen,4 Qing-Dong Zhu1 1Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, Guangxi, People’s Republic of China; 2Clinical Medical School, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 3Department of Rehabilitation, Hepu County People’s Hospital, Beihai, Guangxi, People’s Republic of China; 4Department of Gastroenterology, Hepu County People’s Hospital, Beihai, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing-Dong Zhu, Department of Tuberculosis, The Fourth People’s Hospital of Nanning, No. 1 Changgang Two-Li, Xingning District, Nanning, Guangxi, 530023, People’s Republic of China, Tel +86 0771-5636973, Email [email protected] Jing-Song Chen, Department of Gastroenterology, Hepu County People’s Hospital, No. 95, Dinghai North Road, Hepu County, Beihai, Guangxi, 536100, People’s Republic of China, Tel +86 0779-7106010, Email [email protected]: This study endeavors to conduct a comprehensive assessment on the performance of large language models (LLMs) in health consultation for individuals living with HIV, delve into their applicability across a diverse array of dimensions, and provide evidence-based support for clinical deployment.Patients and Methods: A 23-question multi-dimensional HIV-specific question bank was developed, covering fundamental knowledge, diagnosis, treatment, prognosis, and case analysis. Four advanced LLMs—ChatGPT-4o, Copilot, Gemini, and Claude—were tested using a multi-dimensional evaluation system assessing medical accuracy, comprehensiveness, understandability, reliability, and humanistic care (which encompasses elements such as individual needs attention, emotional support, and ethical considerations). A five-point Likert scale was employed, with three experts independently scoring. Statistical metrics (mean, standard deviation, standard error) were calculated, followed by consistency analysis, difference analysis, and post-hoc testing.Results: Claude obtained the most outstanding performance with regard to information comprehensiveness (mean score 4.333), understandability (mean score 3.797), and humanistic care (mean score 2.855); Copilot demonstrated proficiency in diagnostic questions (mean score 3.880); Gemini illustrated exceptional performance in case analysis (mean score 4.111). Based on the post-hoc analysis, Claude outperformed other models in thoroughness and humanistic care (P < 0.05). Copilot showed better performance than ChatGPT in understandability (P = 0.045), while Gemini performed significantly better than ChatGPT in case analysis (P < 0.001). It is important to note that performance varied across tasks, and humanistic care remained a consistent weak point across all models.Conclusion: The superiority of diverse models in specific tasks suggest that LLMs hold extensive application potential in the management of HIV patients. Nevertheless, their efficacy in the realm of humanistic care still needs improvement.Keywords: artificial intelligence, large language model, HIV, health consultation, performance analysi

Clinical Characteristics Analysis of 30 Cases of Interferon-γ Autoantibody-Positive Patients with Concurrent Mycobacterial Infection: A 6-Year Retrospective Study

Chun-Yan Zhao,1,2,* Chang Song,1,2,* Hua-Wei He,1,* Xian-Zhen Huang,1 Xia-Yan Meng,1 Ai-Chun Huang,1 Chao-Yan Xu,1 Li-Li Luo,1 Shao-Yong Xi,3 Yan-Qun Lan,1 Wei-Wen Li,1 Yan-Rong Lin,1 Qing-Dong Zhu1 1Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, People’s Republic of China; 2Clinical Medical School, Guangxi Medical University, Nanning, People’s Republic of China; 3Department of Clinical Laboratory, The Fourth People’s Hospital of Nanning, Nanning, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qing-Dong Zhu; Yan-Rong Lin, Department of Tuberculosis, The Fourth People’s Hospital of Nanning, No. 1 Changgang Two-Li, Xingning District, Nanning, 530023, People’s Republic of China, Tel +8615207717261 ; +860771-5636973, Email [email protected]; [email protected]: This study aimed to investigate and elucidate the clinical characteristics, immune status, infection types and patterns, treatment responses, and disease progression in patients with positive anti-interferon-gamma (IFN-γ) autoantibodies in combination with Mycobacterium infections.Patients and Methods: We conducted a retrospective analysis of clinical data from patients with positive anti-IFN-γ autoantibodies and concurrent Mycobacterial infections, including Mycobacterial infections (MTB) and non-tuberculous mycobacteria (NTM). The study included cases treated at the Fourth People’s Hospital of Nanning, Guangxi, from 2018 to 2023. Data collected comprised symptoms, clinical signs, laboratory test results, imaging findings, and other relevant clinical information. Patients were also followed up to evaluate treatment responses and long-term therapeutic outcomes.Results: A total of 30 patients with MTB and NTM infections were analyzed. The majority presented with common symptoms, such as cough, sputum production, weight loss, extrapulmonary tuberculosis (TB), and a range of opportunistic infections. Laboratory and imaging studies revealed complex infection patterns and various pathological changes. Treatment primarily involved targeted anti-infective therapy combined with immunosupportive measures. However, frequent treatment relapses and side effects were observed, resulting in two deaths.Conclusion: Immune deficiency associated with positive anti-IFN-γ autoantibodies resembles the immunosuppression seen in advanced stages of human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS), rendering patients highly susceptible to opportunistic infections. These infections were predominantly caused by NTM, followed by MTB and Talaromyces marneffei (TM). This represents a novel immune deficiency syndrome that predisposes patients to a spectrum of opportunistic infections.Keywords: anti-interferon-gamma autoantibodies, Mycobacterium tuberculosis, nontuberculous mycobacteria, clinical characteristics, treatmen

Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes

A honeynet is a promising active cyber defense mechanism. It reveals the fundamental Indicators of Compromise (IoCs) by luring attackers to conduct adversarial behaviors in a controlled and monitored environment. The active interaction at the honeynet brings a high reward but also introduces high implementation costs and risks of adversarial honeynet exploitation. In this work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to characterize a stochastic transition and sojourn time of attackers in the honeynet and quantify the reward-risk trade-off. In particular, we design adaptive long-term engagement policies shown to be risk-averse, cost-effective, and time-efficient. Numerical results have demonstrated that our adaptive engagement policies can quickly attract attackers to the target honeypot and engage them for a sufficiently long period to obtain worthy threat information. Meanwhile, the penetration probability is kept at a low level. The results show that the expected utility is robust against attackers of a large range of persistence and intelligence. Finally, we apply reinforcement learning to the SMDP to solve the curse of modeling. Under a prudent choice of the learning rate and exploration policy, we achieve a quick and robust convergence of the optimal policy and value.Comment: The presentation can be found at https://youtu.be/GPKT3uJtXqk. arXiv admin note: text overlap with arXiv:1907.0139