Previous studies highlighted the health benefits of coffee and tea, but they only focused on the comparisons between different consumptions. Consequently, the association estimate lacked a clear interpretation, as the substitution of beverages and distribution of doses were not explicitly prescribed. We focused on the ‘relative association’ to ascertain the optimal consumption strategy (including total intake and optimal allocation strategy) for coffee, tea and plain water associated with decreased mortality. Self-reported coffee, tea and plain water intake were used from the UK Biobank. Within a compositional data analysis framework, a multivariate Cox model was used to assess the relative associations after adjusting for a range of potential confounders. The lower mortality risk was observed with at least approximately 7–8 drinks/d of total consumption. When the total intake > 4 drinks/d, substituting plain water with coffee or tea was linked to reduced mortality; nevertheless, the benefit was not seen for ≤ 4 drinks/d. Besides, a balanced consumption of coffee and tea (roughly a ratio of 2:3) associated with the lowest hazard ratios of 0·55 (95 % CI 0·47, 0·64) for all-cause mortality, 0·59 (95 % CI 0·48, 0·72) for cancer mortality, 0·69 (95 % CI 0·49, 0·99) for CVD mortality, 0·28 (95 % CI 0·15, 0·52) for respiratory disease mortality and 0·35 (95 % CI 0·15, 0·82) for digestive disease mortality than other combinations. These results highlight the importance of the rational combination of coffee, tea and plain water, with particular emphasis on ensuring adequate total intake, offering more comprehensive and explicit guidance for individuals.

High gain greater than 106 is crucial for the preamplifiers of joule-class high-energy lasers. In this work, we present a specially designed compact amplifier using 0.5%Nd,5%Gd:SrF2 and 0.5%Nd,5%Y:SrF2 crystals. The irregular crystal shape enhances the gain length of the laser beam and helps suppress parasitic oscillations. The amplified spontaneous emission (ASE) induced by the high gain is analyzed through ray tracing. The balance between gain and ASE is estimated via numerical simulation. The gain spectral characteristics of the two-stage two-pass amplifier are examined, demonstrating the advantages of using different crystals, with bandwidths up to 8 nm and gains over 106. In addition, the temperature and stress distributions in the Nd,Gd:SrF2 crystal are simulated. This work is expected to contribute to the development of high-peak-power ($\ge$terawatt-class) high-energy (joule-class) laser devices.

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.

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.

Regenerative involution is crucial for renewing the mammary gland and maximizing milk production. However, the temporal profiles indicators of oxidative status during this phase are still unclear. In this study, Experiment 1 aimed to investigate the dynamic changes in indicators of oxidative status in plasma during regenerative involution. The dairy goats were dried off at 8 weeks (wk) before kidding (−8 wk, n = 14) or −12 wk (n = 6). The blood samples taken at −8, −7, −6, −5, −4, −3, −2, −1 wk, on the day for kidding (0 wk) and the first week after kidding (+1 wk, milk production 1.28 ± 0.31 kg per day). Experiment 2 aimed to investigate the dynamic changes in indicators of oxidative status in mammary cells. Seven selected goats were biopsied for tissue collection and cell isolation at −8, −4, −1, +1 wk (milk production 1.28 ± 0.31 kg per day), respectively. Plasma analysis in Experiment 1 showed an increase in reactive oxygen species (ROS) levels, peaking at −4 wk (P < 0.01). No significant differences were observed between the dry-off treatments (P = 0.36). The activity of superoxide dismutase (SOD) in plasma remained stable from −7 wk to the first week after kidding (+1 wk), while glutathione peroxidase (GSH-Px) activity peaked at −4 wk. An increased catalase activity was observed at +1 wk (P < 0.01), indicating its response to lactation. In Experiment 2, an increase in ROS levels in isolated mammary cells was observed at −4 wk, while SOD, GSH-Px, and malondialdehyde levels in tissue homogenates rose around kidding (P < 0.01). The dynamic change of the oxidative status suggests that targeted antioxidant strategies would be helpful for regenerative involution of mammary gland in ruminants.