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.

This study was designed to explore changes in soil bulk density (BD), soil organic carbon (SOC) content, SOC stocks, and soil labile organic carbon (C) fractions after 5 years of soil tillage management under the double-cropping rice system in southern of China. The experiment included four soil tillage treatments: rotary tillage with all crop residues removed as a control (RTO); conventional tillage with crop residues incorporation (CT); rotary tillage with crop residues incorporation (RT); and no-tillage with crop residues retention. Our results revealed that soil tillage combined with crop residue incorporation (CT and RT) significantly decreased BD at 0–20 cm soil layer compared to RTO treatment. SOC content and stocks were increased with the application of crop residues. Compared with RTO treatment, SOC content and stocks were increased by 16.8% and 9.8% in CT treatment, respectively. Soil non-labile C content and proportion of labile C were increased due to crop residue incorporation. Compared with RTO treatment, soil proportion of C mineralisation (Cmin), permanganate oxidisable C (KMnO4), particulate organic C (POC), and microbial biomass C (MBC) was increased by 196.1%, 41.4%, 31.4%, and 17.1% under CT treatment, respectively. These results were confirmed by the carbon management index, which was significantly increased under soil tillage with crop residue incorporation. Here, we demonstrated that soil tillage and crop residue incorporation can increase the pool of stable C at surface soil layer while increasing labile C content and proportion. In conclusion, conventional or rotary tillage combined with crop residue incorporation is a soil management able to improve nutrient cycling and soil quality in paddy fields in southern China.

Emission line galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN–MLP that combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN–MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN–MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, and Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, and Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

An actively controllable cascaded proton acceleration driven by a separate 0.8 picosecond (ps) laser is demonstrated in proof-of-principle experiments. MeV protons, initially driven by a femtosecond laser, are further accelerated and focused into a dot structure by an electromagnetic pulse (EMP) on the solenoid, which can be tuned into a ring structure by increasing the ps laser energy. An electrodynamics model is carried out to explain the experimental results and show that the dot-structured proton beam is formed when the outer part of the incident proton beam is optimally focused by the EMP force on the solenoid; otherwise, it is overfocused into a ring structure by a larger EMP. Such a separately controlled mechanism allows precise tuning of the proton beam structures for various applications, such as edge-enhanced proton radiography, proton therapy and pre-injection in traditional accelerators.

The ground effect phenomenon caused by helicopters in proximity to the ground results in helicopters experiencing a distinctive phenomenon known as brownout in a multiphase environment. However, the substantial computational volume associated with current numerical simulations conducted using the coupled CFD-DEM method restricts the scope of current studies on brownout to individual cases. Consequently, it is currently not feasible to make realistic predictions regarding the impact of rotor design parameters on brownout. In order to make full use of the conclusions of the existing theoretical studies and, at the same time, to save computational resources as much as possible, this paper proposes a novel approach of brownout prediction based on the analysis of the helicopter ground effect flow field eigen quantities in order to gain insight into the nature of the phenomenon of brownout. Firstly, a new approach for predicting helicopter brownout is constructed for the well-developed late-stage ground effect flow field. This is achieved by analysing the rotor flow field characteristics and combining the Greely-Iversen expression in particle dynamics to extract the eigen quantities of each region of the flow field. Secondly, the results of the flow field calculations at different heights are analysed using the aforementioned approach. The effectiveness of the approach is demonstrated by comparing the results with those of CFD-DEM calculations. Ultimately, the results of the numerical simulation of the flow field, when combined with the established prediction approach, allow for the prediction of the brownout phenomenon generated by multiple blade tip shape rotors with different design parameters. Furthermore, a comparative study of the influence of blade tip vortex strength on the development of brownout is conducted, which demonstrates that the rotors of the backswept blade tip have been observed to exert a certain positive effect on the inhibition of brownout, although this influence is limited. In contrast, the rotors of the anhedral blade tip have been seen to transport smaller but larger sand particles with greater efficiency and to re-enter the brownout cycle with greater directness. The rotors of the forward-swept blade tip have been found to cause larger sand particles to participate in the brownout, while simultaneously weakening the transport capacity, which has been resulted in a reduction in the overall degree of brownout.

Digital technology enables employees to communicate with each other via virtual platforms. Emoji, particularly smile emoji, has received significant attention in virtual communication research. Drawing upon expectancy violation theory, we propose that in digital communications with followers, leader smile emoji usage has a positive effect on follower satisfaction with leader through perceived leader intimacy. In addition, leader smile emoji usage has a negative effect on perceived leader effectiveness through decreased perception of leader power. We further propose that the effects of leader smile emoji usage hinge on follower power distance orientation such that the negative effects of leader smile emojis usage are more pronounced for followers with high versus low power distance orientation. An experiment and a field study supported our hypotheses. Our research sheds light on the benefits and potential pitfalls of smile emoji usage in leader–follower digital communication.

Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran’s I < 0.2, p > 0.05). The Bayesian spatiotemporal model’s Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

The cosmic 21 cm signal serves as a crucial probe for studying the evolutionary history of the Universe. However, detecting the 21 cm signal poses significant challenges due to its extremely faint nature. To mitigate the interference from the Earth’s radio frequency interference (RFI), the ground and the ionospheric effects, the Discovering the Sky at the Longest Wavelength (DSL) project will deploy a constellation of satellites in lunar orbit, with its high-frequency daughter satellite tasked with detecting the global 21 cm signal from cosmic dawn and reionization era (CD/EoR). We intend to employ the vari-zeroth-order polynomial (VZOP) for foreground fitting and subtracting. We have studied the effect of thermal noise, thermal radiation from the Moon, the lunar reflection, anisotropic frequency-dependent beam, inaccurate antenna beam pattern, and RFI contamination. We discovered that the RFI contamination can significantly affect the fitting process and thus prevent us from detecting the signal. Therefore, experimenting on the far side of the moon is crucial. We also discovered that using VZOP together with DSL, after 1080 orbits around the Moon, which takes about 103 days, we can successfully detect the CD/EoR 21 cm signal.

Major depressive disorder (MDD) and coronary heart disease (CHD) can both cause significant morbidity and mortality. The association of MDD and CHD has long been identified, but the mechanisms still require further investigation. Seven mRNA microarray datasets containing samples from patients with MDD and CHD were downloaded from Gene Expression Omnibus. Combined matrixes of MDD and CAD were constructed for subsequent analysis. Differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on shared DEGs were conducted to identify pivotal pathways. A protein-protein network was also applied to further investigate the functional interaction. Results showed that 24 overlapping genes were identified. Enrichment analysis indicated that the shared genes are mainly associated with immune function and ribosome biogenesis. The functional interactions of shared genes were also demonstrated by PPI network analysis. In addition, three hub genes including MMP9, S100A8, and RETN were identified. Our results indicate that MDD and CHD have a genetic association. Genes relevant to immune function, especially IL-17 signalling pathway may be involved in the pathogenesis of MDD and CHD.

The vitamin K (VK) levels vary greatly among different populations and in different regions. Currently, there is a lack of reference intervals for VK levels in healthy individuals, The aim of this study is to establish and validate the reference intervals of serum vitamin K1 (VK1) and vitamin K2 (VK2, specifically including menaquinone-4 (MK4) and menaquinone-7 (MK7)) levels in some healthy populations in Beijing. Serum VK1, MK4, and MK7 were firstly measured by high-performance liquid chromatography and mass spectrometry in 434 subjects. The reference intervals for three indicators were established by calculating the data of 2.5 and 97.5 percentiles. Finally, preliminary clinical validation was conducted on 60 apparent healthy individuals undergoing physical examination. In the young, middle-aged, and elderly groups, the reference intervals of VK1 were 0.180 ng/mL ∼ 1.494 ng/mL, 0.247 ng/mL ∼ 1.446 ng/mL, and 0.167 ng/mL ∼ 1.445 ng/mL, respectively. The reference intervals of MK4 were 0.009 ng/mL ∼ 0.115 ng/mL, 0.002 ng/mL ∼ 0.103 ng/mL, and 0.003 ng/mL ∼ 0.106 ng/mL, respectively. The reference intervals of MK7 were 0.169 ng/mL ∼ 0.881 ng/mL, 0.238 ng/mL ∼ 0.936 ng/mL, and 0.213 ng/mL ∼ 1.012 ng/mL, respectively. The reference intervals had been validated by the samples of healthy individuals for physical examination. In conclusion, the reference intervals of VK established in this study with different age groups have certain clinical applicability, providing data support for further multicentre studies.

The rapid and efficient removal of weeds is currently a research hotspot. With the integration of robotics and automation technology into agricultural production, intelligent field-weeding robots have emerged. An overview of the development status of weeding robots based on bibliometric and scientific mapping methods is presented. Two key technologies of weeding robots are summarized, and the research progress of precision-spraying weeding robots, mechanical weeding robots, and thermal weeding robots with laser devices, categorized by weeding method, is reviewed. Finally, a summary and an outlook on the future development trends of intelligent field-weeding robots are provided, aiming to offer a reference for further promoting the development of weeding robots.

A novel theoretical model for bubble dynamics is established that simultaneously accounts for the liquid compressibility, phase transition, oscillation, migration, ambient flow field, etc. The bubble dynamics equations are presented in a unified and concise mathematical form, with clear physical meanings and extensibility. The bubble oscillation equation can be simplified to the Keller–Miksis equation by neglecting the effects of phase transition and bubble migration. The present theoretical model effectively captures the experimental results for bubbles generated in free fields, near free surfaces, adjacent to rigid walls, and in the vicinity of other bubbles. Based on the present theory, we explore the effect of the bubble content by changing the vapour proportion inside the cavitation bubble for an initial high-pressure bubble. It is found that the energy loss of the bubble shows a consistent increase with increasing Mach number and initial vapour proportion. However, the radiated pressure peak by the bubble at the collapse stage increases with decreasing Mach number and increasing vapour proportion. The energy analyses of the bubble reveal that the presence of vapour inside the bubble not only directly contributes to the energy loss of the bubble through phase transition but also intensifies the bubble collapse, which leads to greater radiation of energy into the surrounding flow field due to the fluid compressibility.

Oncomelania hupensis (O. hupensis), the sole intermediate host of Schistosoma japonicum, greatly influence the prevalence and distribution of schistosomiasis japonica. The distribution area of O. hupensis has remained extensive for numerous years. This study aimed to establish a valid agent-based model of snail density and further explore the environmental conditions suitable for snail breeding. A marshland with O. hupensis was selected as a study site in Dongting Lake Region, and snail surveys were monthly conducted from 2007 to 2016. Combined with the data from historical literature, an agent-based model of snail density was constructed in NetLogo 6.2.0 and validated with the collected survey data. BehaviorSpace was used to identify the optimal ranges of soil temperature, pH, soil water content, and vegetation coverage for snail growth, development and reproduction. An agent-based model of snail density was constructed and showed a strong agreement with the monthly average snail density from the field surveys. As soil temperature increased, the snail density initially rose before declining, reaching its peak at around 21°C. There were similar variation patterns for other environmental factors. The findings from the model suggested that the optimum ranges of soil temperature, pH, soil water content and vegetation coverage were 19°C to 23 °C, 6.4 to 7.6, 42% to 75%, and 70% to 93%, respectively. A valid agent-based model of snail density was constructed, providing more objective information about the optimum ranges of environmental factors for snail growth, development and reproduction.

Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice (Oryza sativa L.) fields has been increasing, likely due to changing paddy-farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil-flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, nonstructural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchymal fibers, with lignin content also increasing. After 90 d, the lignified sclerenchymal fibers and lignin contents were 4.0 and 1.1 times higher than those in the no soil-flooding treatment. Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil-flooding conditions by adopting a low-oxygen quiescence strategy, which involves morphological adaptations through the development of lignified sclerenchymal fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.

Sjögren's syndrome (SS) is a chronic autoimmune disease caused by immune system disorders. The main clinical manifestations of SS are dry mouth and eyes caused by the destruction of exocrine glands, such as the salivary and lacrimal glands, and systemic manifestations, such as interstitial pneumonia, interstitial nephritis and vasculitis. The pathogenesis of this condition is complex. However, this has not been fully elucidated. Treatment mainly consists of glucocorticoids, disease-modifying antirheumatic drugs and biological agents, which can only control inflammation but not repair the tissue. Therefore, identifying methods to regulate immune disorders and repair damaged tissues is imperative. Cell therapy involves the transplantation of autologous or allogeneic normal or bioengineered cells into the body of a patient to replace damaged cells or achieve a stronger immunomodulatory capacity to cure diseases, mainly including stem cell therapy and immune cell therapy. Cell therapy can reduce inflammation, relieve symptoms and promote tissue repair and regeneration of exocrine glands such as the salivary glands. It has broad application prospects and may become a new treatment strategy for patients with SS. However, there are various challenges in cell preparation, culture, storage and transportation. This article reviews the research status and prospects of cell therapies for SS.

Characterised by the extensive use of obsidian, a blade-based tool inventory and microblade technology, the late Upper Palaeolithic lithic assemblages of the Changbaishan Mountains are associated with the increasingly cold climatic conditions of Marine Isotope Stage 2, yet most remain poorly dated. Here, the authors present new radiocarbon dates associated with evolving blade and microblade toolkits at Helong Dadong, north-east China. At 27 300–24 100 BP, the lower cultural layers contain some of the earliest microblade technology in north-east Asia and highlight the importance of the Changbaishan Mountains in understanding changing hunter-gatherer lifeways in this region during MIS 2.

To evaluate the variations in COVID-19 case fatality rates (CFRs) across different regions and waves, and the impact of public health interventions, social and economic characteristics, and demographic factors on COVID-19 CFRs, we collected data from 30 countries with the highest incidence rate in three waves. We summarized the CFRs of different countries and continents in each wave through meta-analysis. Spearman’s correlation and multiple linear regression were employed to estimate the correlation between influencing factors and reduction rates of CFRs. Significant differences in CFRs were observed among different regions during the three waves (P < 0.001). An association was found between the changes in fully vaccinated rates (rs = 0.41), population density (rs = 0.43), the proportion of individuals over 65 years old (rs = 0.43), and the reduction rates of case fatality rate. Compared to Wave 1, the reduction rates in Wave 2 were associated with population density (β = 0.19, 95%CI: 0.05–0.33) and smoking rates (β = −4.66, 95%CI: −8.98 – −0.33), while in Wave 3 it was associated with booster vaccine rates (β = 0.60, 95%CI: 0.11–1.09) and hospital beds per thousand people (β = 4.15, 95%CI: 1.41–6.89). These findings suggest that the COVID-19 CFRs varied across different countries and waves, and promoting booster vaccinations, increasing hospital bed capacity, and implementing tobacco control measures can help reduce CFRs.

Power scaling in conventional broad-area (BA) lasers often leads to the operation of higher-order lateral modes, resulting in a multiple-lobe far-field profile with large divergence. Here, we report an advanced sawtooth waveguide (ASW) structure integrated onto a wide ridge waveguide. It strategically enhances the loss difference between higher-order modes and the fundamental mode, thereby facilitating high-power narrow-beam emission. Both optical simulations and experimental results illustrate the significant increase in additional scattering loss of the higher-order modes. The optimized ASW lasers achieve an impressive output power of 1.1 W at 4.6 A at room temperature, accompanied by a minimal full width at half maximum lateral divergence angle of 4.91°. Notably, the far-field divergence is reduced from 19.61° to 11.39° at the saturation current, showcasing a remarkable 42% improvement compared to conventional BA lasers. Moreover, the current dependence of divergence has been effectively improved by 38%, further confirming the consistent and effective lateral mode control capability offered by our design.