American silk moth, Antheraea polyphemus Cramer 1775 (Lepidoptera: Saturniidae), native to North America, has potential significance in sericulture for food consumption and silk production. To date, the phylogenetic relationship and divergence time of A. polyphemus with its Asian relatives remain unknown. To end these issues, two mitochondrial genomes (mitogenomes) of A. polyphemus from the USA and Canada respectively were determined. The mitogenomes of A. polyphemus from the USA and Canada were 15,346 and 15,345 bp in size, respectively, with only two transitions and five indels. The two mitogenomes both encoded typical mitochondrial 37 genes. No tandem repeat elements were identified in the A+T-rich region of A. polyphemus. The mitogenome-based phylogenetic analyses supported the placement of A. polyphemus within the genus Antheraea, and revealed the presence of two clades for eight Antheraea species used: one included A. polyphemus, A. assamensis Helfer, A. formosana Sonan and the other contained A. mylitta Drury, A. frithi Bouvier, A. yamamai Guérin-Méneville, A. proylei Jolly, and A. pernyi Guérin-Méneville. Mitogenome-based divergence time estimation further suggested that the dispersal of A. polyphemus from Asia into North America might have occurred during the Miocene Epoch (18.18 million years ago) across the Berling land bridge. This study reports the mitogenome of A. polyphemus that provides new insights into the phylogenetic relationship among Antheraea species and the origin of A. polyphemus.

The interaction of helminth infections with type 2 diabetes (T2D) has been a major area of research in the past few years. This paper, therefore, focuses on the systematic review of the effects of helminthic infections on metabolism and immune regulation related to T2D, with mechanisms through which both direct and indirect effects are mediated. Specifically, the possible therapeutic role of helminths in T2D management, probably mediated through the modulation of host metabolic pathways and immune responses, is of special interest. This paper discusses the current possibilities for translating helminth therapy from basic laboratory research to clinical application, as well as existing and future challenges. Although preliminary studies suggest the potential for helminth therapy for T2D patients, their safety and efficacy still need to be confirmed by larger-scale clinical studies.

Against the proliferation of large language model (LLM) based Artificial Intelligence (AI) products such as ChatGPT and Gemini, and their increasing use in professional communication training, researchers, including applied linguists, have cautioned that these products (re)produce cultural stereotypes due to their training data. However, there is a limited understanding of how humans navigate the assumptions and biases present in the responses of these LLM-powered systems and the role humans play in perpetuating stereotypes during interactions with LLMs. In this article, we use Sequential-Categorial Analysis, which combines Conversation Analysis and Membership Categorization Analysis, to analyze simulated interactions between a human physiotherapist and three LLM-powered chatbot patients of Chinese, Australian, and Indian cultural backgrounds. Coupled with analysis of information elicited from LLM chatbots and the human physiotherapist after each interaction, we demonstrate that users of LLM-powered systems are highly susceptible to becoming interactionally entrenched in culturally essentialized narratives. We use the concepts of interactional instinct and interactional entrenchment to argue that whilst human–AI interaction may be instinctively prosocial, LLM users need to develop Critical Interactional Competence for human–AI interaction through appropriate and targeted training and intervention, especially when LLM-powered tools are used in professional communication training programs.

This study explored mental workload recognition methods for carrier-based aircraft pilots utilising multiple sensor physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and electroencephalogram (EEG) and photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98% and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.

Posttraumatic stress disorder (PTSD) is a heterogenous disorder with frequent diagnostic comorbidity. Research has deciphered this heterogeneity by identifying PTSD subtypes and their neural biomarkers. This review summarizes current approaches, symptom-based group-level and data-driven approaches, for generating PTSD subtypes, providing an overview of current PTSD subtypes and their neural correlates. Additionally, we systematically assessed studies to evaluate the influence of comorbidity on PTSD subtypes and the predictive utility of biotypes for treatment outcomes. Following the PRISMA guidelines, a systematic search was conducted to identify studies employing brain imaging techniques, including functional magnetic resonance imaging (fMRI), structural MRI, diffusion-weighted imaging (DWI), and electroencephalogram (EEG), to identify biomarkers of PTSD subtypes. Study quality was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We included 53 studies, with 44 studies using a symptom-based group-level approach, and nine studies using a data-driven approach. Findings suggest biomarkers across the default-mode network (DMN) and the salience network (SN) throughout multiple subtypes. However, only six studies considered comorbidity, and four studies tested the utility of biotypes in predicting treatment outcomes. These findings highlight the complexity of PTSD’s heterogeneity. Although symptom-based and data-driven methods have advanced our understanding of PTSD subtypes, challenges remain in addressing the impact of comorbidities and the limited validation of biotypes. Future studies with larger sample sizes, brain-based data-driven approaches, careful account for comorbidity, and rigorous validation strategies are needed to advance biologically grounded biotypes across mental disorders.

Cargo carrying by a spring connected chiral micro-swimmer in a square channel is numerical studied by the three-dimensional lattice Boltzmann method and a chiral squirmer model. The effects of the driving type (β), swimming Reynolds number (Rep), spin coefficient (ξ) and diameter ratio (S) on the changes of the cargo-carrying velocity, spring length and motion modes are investigated, respectively. Four kinds of interesting motion modes are observed. When the chirality is not considered, the optimal combination for maximising swimming velocity are the pusher–cargo and cargo–puller configurations when Rep = 0.1 ∼ 1. When Rep is enhanced, the swimming velocities of the pusher–cargo, puller–cargo and cargo–pusher are increased, while the velocity of the cargo–puller is gradually decreased. When considering the chirality, only the swimming velocity of cargo–pusher and cargo–puller keep an interesting increment, and the reverse motion mode for the pusher-cargo and puller-cargo is firstly found in the present work when ξ exceeds a certain value. The impact of S on the cargo-carrying behaviour is complex, three kinds of oscillatory trajectories will appear under different ξ and S. The swimming velocity is reduced and even zero velocity will be observed when S is large. This work reveals key factors on the movement of microorganisms, offering guidance for improving cargo-carrying capabilities.

We investigate the statistical properties of kinetic and thermal dissipation rates in two-dimensional/three-dimensional vertical convection of liquid metal ($Pr = 0.032$) within a square cavity. Two situations are specifically discussed: (i) classical vertical convection with no external forces and (ii) vertical magnetoconvection with a horizontal magnetic field. Through an analysis of dissipation fields and a reasonable approximation of buoyancy potential energy sourced from vertical heat flux, the issue of the ‘non-closure of the dissipation balance relation’, which has hindered the application of the GL theory in vertical convection, is partially resolved. The resulting asymptotic power laws are consistent with existing laminar scaling theories and even show certain advantages in validating simulations with large Prandtl number ($Pr$). Additionally, a full-parameter model and prefactors applicable to low-$Pr$ fluids are provided. The extension to magnetoconvection naturally introduces the approximate expression for total buoyancy potential energy and necessitates adjustments to the contributions of kinetic dissipation in both the bulk and boundary layer. The flow dimensionality and boundary layer thickness are key considerations in this analysis. The comprehension of Joule dissipation has been updated: the Lorentz force generates positive dissipation in the bulk by suppressing convection, while in the Hartmann layer, shaping the exponential boundary layer requires the fluid to perform positive work to accelerate, leading to negative dissipation. Finally, the proposed transport equations for magnetoconvection are supported by current direct numerical simulation (DNS) and literature data, and the applicability of the model is discussed.

Objectives/Goals: Mathematical models of airborne virus transmission lack supporting field and clinical data such as viral aerosol emission rates and airborne infectious doses. Here, we aim to measure inhalation exposure to influenza aerosols in a room shared with persons with community-acquired influenza and estimate the infectious dose via inhalation. Methods/Study Population: We recruited healthy volunteer recipients and influenza donors with polymerase chain reaction (PCR)-confirmed community-acquired infection. On admission to a hotel quarantine, recipients provided sera to determine baseline immunity to influenza virus, and donor infections were confirmed by quantitative real-time polymerase chain reaction. Donors and recipients were housed in separate rooms and interacted in an “event room” with controlled ventilation (0.2 – 0.5 air changes/hour) and relative humidity (20–40%). We collected ambient bioaerosol exposure samples using NIOSH BC-251 samplers. Donors provided exhaled breath samples collected by a Gesundheit-II (G-II). We analyzed aerosol samples using dPCR and fluorescent focus assays for influenza A and sera by hemagglutinin inhibition assay (HAI) against donor viruses and vaccine strains. Results/Anticipated Results: Among two cohorts (24b and 24c), we exposed 11 recipients (mean age: 36; 55% female) to 5 donors (mean age: 21; 80% female) infected with influenza A H1N1 or H3N2. Eight G-II and two NIOSH bioaerosol samples (1–4 µm and ≥4 µm) were PCR positive. We cultured virus from one G-II sample. Based on previous literature, we hypothesized that ~50% of immunologically naïve people (HAI Discussion/Significance of Impact: We demonstrated that it is feasible to recruit donors with community-acquired influenza and expose recipients to measurable virus quantities under controlled conditions. However, baseline immunity was high among volunteers. Our work sets the stage for designing studies with increased sample sizes comprising immunologically naïve volunteers.

The reflection of multiple incident shock waves that converge to a single point on the reflecting surface is studied in this paper. The number of the incident shocks, denoted $K$, is arbitrary. The interaction between the reflected shock of one incident shock and the other incident shocks may produce various possible configurations, such as type-I, type-II and type-IV shock interferences. The number of possible reflection configurations is shown to be an exponential function of ($K-1$) with base 2. The possibility of pre-, middle- and post-Mach reflections, which means Mach reflection occurs for the first, middle and last incident shock, is revealed through numerical simulation for $K=3$. For the particular case where the incident shocks are produced by equal variation of wedge surface deflection, the conventional von Neumann condition and detachment condition for the $k\mathrm{th}$ incident shock to have Mach reflection are derived. It is shown that the von Neumann condition for regular reflection is lowered and the detachment condition for Mach reflection is elevated as $k$ increases. The shock reflection patterns for $ K=1,2,\ldots ,10$ are obtained by numerical simulations. We observe a shock interaction train structure, where we have pre-Mach reflection followed by ($K-1$) type-I or type-II shock interferences. We also observe that the Mach stem height decreases with $K$ well above the von Neumann condition and becomes non-monotonic near the von Neumann condition.

Numerous supraglacial lakes form on the Greenland Ice Sheet (GrIS) during the summer, and accurately estimating their depth is crucial for understanding GrIS water storage. In this study, we estimate the depth of 35 representative GrIS supraglacial lakes using ICESat-2, Sentinel-2 imagery and ArcticDEM data. ICESat-2-derived lake depth is used to validate the performance of three remote sensing methods, namely empirical formula method (EFM), radiative transfer method (RTM) and depression topography method (DTM). EFM relies on ICESat-2-derived lake depth to construct empirical formulas, while RTM and DTM do not. The results show that (1) the green band EFM performs best; the DTM performs secondarily but tends to consistently underestimate depths; the green-band RTM has lower accuracy and overestimates depths, while the red-band RTM also has lower accuracy but underestimates depths. (2) Temporal changes of depression topography have limited impacts on the performance of DTM, whereas the uncertainties caused by lake shoreline height estimates should be considered. (3) The performance of RTM is significantly influenced by the spectral attenuation coefficient. We further identify the factors that affect spatiotemporal extrapolation of these methods and recommend prioritizing the use of the EFM when near-simultaneous ICESat-2 data are available; otherwise, DTM is recommended, yet an underestimation ratio should be used.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Knowledge of the critical periods of crop–weed competition is crucial for designing weed management strategies in cropping systems. In the Lower Yangtze Valley, China, field experiments were conducted in 2011 and 2012 to study the effect of interference from mixed natural weed populations on cotton growth and yield and to determine the critical period for weed control (CPWC) in direct-seeded cotton. Two treatments were applied: allowing weeds to infest the crop or keeping plots weed-free for increasing periods (0, 1, 2, 4, 6, 8, 10, 12, 14, and 20 wk) after crop emergence. The results show that mixed natural weed infestations led to 35- to 55-cm shorter cotton plants with stem diameters 10 to 13 mm smaller throughout the season, fitting well with modified Gompertz and logistic models, respectively. Season-long competition with weeds reduced the number of fruit branches per plant by 65% to 82%, decreasing boll number per plant by 86% to 96% and single boll weight by approximately 24%. Weed-free seed cotton yields ranged from 2,900 to 3,130 kg ha−1, while yield loss increased with the duration of weed infestation, reaching up to 83% to 96% compared with permanent weed-free plots. Modified Gompertz and logistic models were used to analyze the impact of increasing weed control duration and weed interference on relative seed cotton yield (percentage of season-long weed-free cotton), respectively. Based on a 5% yield loss threshold, the CPWC was found to be from 145 to 994 growing degree days (GDD), corresponding to 14 to 85 d after emergence (DAE). These findings emphasize the importance of implementing effective weed control measures from 14 to 85 DAE in the Lower Yangtze Valley to prevent crop losses exceeding a 5% yield loss threshold.