Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set = 2977, test set = 1277). There were 2783 men and 1471 women (median age = 61 years). RM was identified in 754 (17·7 %) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable performance to predict RM in the test set (AUC = 0·924, 95 % CI = 0·904, 0·944) and an external validation set (n 798, AUC = 0·909, 95 % CI = 0·876, 0·943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all P < 0·05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimise patient outcomes in multidisciplinary cancer care.

Overnutrition during before and pregnancy can cause maternal obesity and raise the risk of maternal metabolic diseases during pregnancy, and in offspring. Lentinus edodes may prevent or reduce obesity. This study aimed to to assess Lentinus edodes fermented products effects on insulin sensitivity, glucose and lipid metabolism in maternal and offspring, and explore its action mechanism. A model of overnutrition during pregnancy and lactation was developed using a 60 % kcal high-fat diet in C57BL6/J female mice. Fermented Lentinus edodes (FLE) was added to the diet at concentrations of 1 %, 3 %, and 5 %. The results demonstrated that FLE to the gestation diet significantly reduced serum insulin levels and homeostatic model assessment for insulin resistance (HOMA-IR) in pregnant mice. FLE can regulate maternal lipid metabolism and reduce fat deposition. Meanwhile, the hepatic phosphoinositide-3-kinase-protein kinase (PI3K/AKT) signaling pathway was significantly activated in the maternal mice. There is a significant negative correlation between maternal FLE supplementation doses and offspring body fat percentage and visceral fat content. Furthermore, FLE supplementation significantly increased offspring weaning litter weight, significantly reduced fasting glucose level, serum insulin level, HOMA-IR and serum glucose level, significantly activated liver PI3K/AKT signaling pathway in offspring, and upregulated the expression of liver lipolytic genes adipose triglyceride lipase, hormone-sensitive lipase and carnitine palmitoyltransferase 1 mRNA. Overall, FLE supplementation can regulate maternal lipid metabolism and reduce fat deposition during pregnancy and lactation, and it may improve insulin sensitivity in pregnant mothers and offspring at weaning through activation of the PI3K/AKT signaling pathway.

Under the coupling effect of node position deviation, joint clearance and wear factors, the complex landing gear retraction mechanism suffers from low kinematic accuracy, slow retraction performance and shortened reliable life. Addressing these issues, a time-dependent reliability analysis and optimisation design method for the kinematic accuracy of the retraction mechanism is proposed, considering the uncertainty of node position deviation, initial clearance, and dynamic multi-joint wear. Initially, a wear prediction model and a dynamic model of the retraction mechanism considering node position deviation and joint clearance are established to analyse their influence on retraction accuracy and joint wear depth. Subsequent retraction testing under various working conditions is conducted to ascertain the critical failure condition and validate the simulation model. The time-dependent kinematic accuracy reliability model, accounting for the dynamic evolution of wear clearance, is then established to assess reliability variation with retraction cycles. Finally, the reliability optimisation design focusing on hole-axis matching accuracy aims to strike a balance between accuracy cost and reliability, thereby enhancing performance and prolonging operational life.

Bovine mastitis harms milk quality and cattle health. Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP) are well-known milk-derived bioactive peptides with anti-inflammatory activity. However, the impact of VPP and IPP on mastitis remain unknown. This study aimed to investigate the anti-inflammatory effects and the underlying mechanisms of VPP and IPP in lipopolysaccharide (LPS)-induced inflammation. When cells were treated with LPS (1 µg/mL) for 24 h, the protein levels of pro-inflammatory factors (tumor necrosis factor-α (TNF-α), interleukin(IL)-1β and IL-6)) and chemokine (monocyte chemotactic protein-1 (MCP-1)) were markedly increased, and the protein level of anti-inflammatory cytokine (IL-10) was reduced. Both VPP and IPP with concentrations of 50 and 100 µM reversed these phenomena and further inhibited the protein expression of β-casein induced by LPS. In a mouse mastitis model, different concentrations of VPP and IPP (300, 600 µM/kg) pretreatment alleviated histopathological lesions in the mammary gland and suppressed the mRNA expression of TNFα, IL1β, and IL6 induced by LPS. VPP and IPP also maintained the integrity of the blood–milk barrier in mice. RNA-seq analyses indicated that enriched phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase (MAPK) signaling pathways likely contribute to the changes observed (P < 0.05 and |log2 fold change (FC)| ≥ 1). Notably, fibronectin was identified as an important hub by protein–protein interaction (PPI) analysis and molecular docking combined with molecular dynamics simulation. In summary, VPP and IPP exerted a protective effect on LPS-induced inflammation by regulating PI3K/AKT signaling pathway via fibronectin.

Little is known about the association between iodine nutrition status and bone health. The present study aimed to explore the connection between iodine nutrition status, bone metabolism parameters, and bone disease (osteopenia and osteoporosis). A cross-sectional survey was conducted involving 391, 395, and 421 adults from iodine fortification areas (IFA), iodine adequate areas (IAA), and iodine excess areas (IEA) of China. Iodine nutrition status, bone metabolism parameters and BMD were measured. Our results showed that, in IEA, the urine iodine concentrations (UIC) and serum iodine concentrations (SIC) were significantly higher than in IAA. BMD and Ca2+ levels were significantly different under different iodine nutrition levels and the BMD were negatively correlated with UIC and SIC. Univariate linear regression showed that gender, age, BMI, menopausal status, smoking status, alcohol consumption, UIC, SIC, free thyroxine, TSH, and alkaline phosphatase were associated with BMD. The prevalence of osteopenia was significantly increased in IEA, UIC ≥ 300 µg/l and SIC > 90 µg/l groups. UIC ≥ 300 µg/l and SIC > 90 µg/l were risk factors for BMD T value < –1·0 sd. In conclusion, excess iodine can not only lead to changes in bone metabolism parameters and BMD, but is also a risk factor for osteopenia and osteoporosis.

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.

Skin-friction drag reduction (DR) in a turbulent boundary layer (TBL) using plasma-generated streamwise vortices (PGSVs) is governed by plasma-induced spanwise wall-jet velocity $W$, the distance $L$ between the positive electrodes of two adjacent plasma actuators (PAs) and the friction Reynolds number $Re_\tau$. It is found experimentally that DR increases logarithmically with the growing maximum spanwise mean velocity $\overline {W}_{max}^+$ but decreases with rising $L^+$ and $Re_\tau$, where superscript ‘+’ denotes normalization by the inner scales. It is further found from theoretical and empirical scaling analyses that the dimensionless drag variation $\Delta F = g_1 (\overline {W}_{max}^+, L^+, {Re_\tau })$ may be reduced to $\Delta F = g_2 (\xi )$, where $g_1$ and $g_2$ are different functions and the scaling factor $\xi = [k_{2} \log _{10} (k_{1} \overline {W}_{max }^{+} ) ] / (L^{+} Re_{\tau } )$ ($k_{2}$ and $k_{1}$ are constants) is physically the circulation of the PGSVs. Discussion is conducted based on $\Delta F = g_2 (\xi )$, which provides important insight into the physics of TBL control based on PAs.

The betatron radiation source features a micrometer-scale source size, a femtosecond-scale pulse duration, milliradian-level divergence angles and a broad spectrum exceeding tens of keV. It is conducive to the high-contrast imaging of minute structures and for investigating interdisciplinary ultrafast processes. In this study, we present a betatron X-ray source derived from a high-charge, high-energy electron beam through a laser wakefield accelerator driven by the 1 PW/0.1 Hz laser system at the Shanghai Superintense Ultrafast Laser Facility (SULF). The critical energy of the betatron X-ray source is 22 ± 5 keV. The maximum X-ray flux reaches up to 4 × 109 photons for each shot in the spectral range of 5–30 keV. Correspondingly, the experiment demonstrates a peak brightness of 1.0 × 1023 photons·s−1·mm−2·mrad−2·0.1%BW−1, comparable to those demonstrated by third-generation synchrotron light sources. In addition, the imaging capability of the betatron X-ray source is validated. This study lays the foundation for future imaging applications.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

The ubiquitous marine radiocarbon reservoir effect (MRE) constrains the construction of reliable chronologies for marine sediments and the further comparison of paleoclimate records. Different reference values were suggested from various archives. However, it remains unclear how climate and MREs interact. Here we studied two pre-bomb corals from the Hainan Island and Xisha Island in the northern South China Sea (SCS), to examine the relationship between MRE and regional climate change. We find that the MRE from east of Hainan Island is mainly modulated by the Southern Asian Summer Monsoon-induced precipitation (with 11.4% contributed to seawater), rather than wind induced upwelling. In contrast, in the relatively open seawater of Xisha Island, the MRE is dominated by the East Asian Winter Monsoon, with relatively more negative (lower) ΔR values associated with high wind speeds, implying horizontal transport of seawater. The average SCS ΔR value relative to the Marine20 curve is –161±39 14C years. Our finding highlights the essential role of monsoon in regulating the MRE in the northern SCS, in particularly the tight bond between east Asian winter monsoon and regional MRE.

Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model.

Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.

The high-power narrow-linewidth fiber laser has become the most widely used high-power laser source nowadays. Further breakthroughs of the output power depend on comprehensive optimization of stimulated Brillouin scattering (SBS), stimulated Raman scattering (SRS) and transverse mode instability (TMI). In this work, we aim to further surpass the power record of all-fiberized and narrow-linewidth fiber amplifiers with near-diffraction-limited (NDL) beam quality. SBS is suppressed by white-noise-signal modulation of a single-frequency seed. In particular, the refractive index of the large-mode-area active fiber in the main amplifier is controlled and fabricated, which could simultaneously increase the effective mode field area of the fundamental mode and the loss coefficient of higher-order modes for balancing SRS and TMI. Subsequent experimental measurements demonstrate a 7.03 kW narrow-linewidth fiber laser with a signal-to-noise ratio of 31.4 dB and beam quality factors of Mx2 = 1.26, My2 = 1.25. To the best of our knowledge, this is the highest reported power with NDL beam quality based on a directly laser-diode-pumped and all-fiberized format, especially with narrow-linewidth spectral emission.

Several novel anthropometric indices, including paediatric body adiposity index (BAIp) and triponderal mass index (TMI), have emerged as potential tools for estimating body fat in preschool children. However, their comparative validity and accuracy, particularly when compared with established indicators such as BMI, have not been thoroughly investigated. This cross-sectional study enrolled 2869 preschoolers aged 3–6 years in Wuhan, China. The non-parametric Bland–Altman analysis was employed to evaluate the agreement between BMI, BAIp and TMI with percentage of body fat (PBF), determined by bioelectrical impedance analysis (BIA), serving as the reference measure of adiposity. Additionally, receiver operating characteristic curve analysis was conducted to assess the effectiveness of BMI, BAIp and TMI in screening for obesity. BAIp demonstrated the least bias in estimating PBF, showing discrepancies of 3·64 % (95 % CI 3·40 %, 4·12 %) in boys and 3·95 % (95 % CI 3·79 %, 4·23 %) in girls. Conversely, BMI underestimated PBF by 3·89 % (95 % CI 3·70 %, 4·37 %) in boys and 4·81 % (95 % CI 4·59 %, 5·09 %) in girls, while TMI also underestimated PBF by 5·15 % (95 % CI 4·90 %, 5·52 %) in boys and 5·68 % (95 % CI 5·30 %, 5·91 %) in girls. BAIp exhibited the highest AUC values (AUC = 0·867–0·996) in boys, whereas in girls, there was no statistically significant difference between BMI (AUC = 0·936, 95 % CI 0·921, 0·948) and BAIp (AUC = 0·901, 95 % CI 0·883, 0·916) in girls (P = 0·054). In summary, when considering the identification of obesity, BAIp shows promise as a screening tool for both boys and girls.