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Background Although the majority of Candida infections occur in the developing world, candidemia epidemiology is poorly understood in these countries. The aim of this study was to investigate the epidemiology of non-Candida albicans (non-C. albicans) candidemia among neonates at Liuzhou Maternity and Child Healthcare Hospital in China. Methods A retrospective review of all positive blood culture about Candida species in neonatal intensive care unit was conducted between January 2012 and November 2015. Information about demographics, risk factors and outcome of candidemia were collected. Univariate and multivariate logistic regression models were used to identify the risk factors associated with the development of non-C.albicans candidemia. Results The prevalence of candidemia in infants was 1.4%. Non-C.albicans was responsible for 56.5% of neonatal candidemia. The predisposing factors for development of non-C.albicans candidemia among infants included mechanical ventilation [odds ratio (OR), 95% confidence interval (95%CI) = 3.13, 1.07–9.14; P  = 0.037] and use of assisted reproductive technology (OR, 95%CI = 4.52, 1.39–14.77; P  = 0.012). The overall mortality rate of candidemia was 8.7% and non-C.albicans attributed to 83.3% of all mortalities. Conclusions Non-C.albicans species are the major cause of candidemia in local neonatal group. The study highlights the urgent needs to evaluate the possibility of development of non-C.albicans candidemia in neonates exposed to these risk factors and much emphasis must be laid on the early implementation of medical intervention to reduce the incidences of candidemia in neonates.

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVIsoil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVIsoil determining methods were compared for estimating FVC. The first method used an invariant NDVIsoil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVIsoil for each soil type. We quantified the influence of variations of NDVIsoil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVIsoil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVIsoil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVIsoil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVIsoil but also on other VIsoil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VIsoil and soil properties for predicting VIsoil.

Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the accuracy of biomass estimation. We developed models from SAR polarization indices, vegetation indices (VIs), and biophysical variables (BPVs) based on gaussian process regression (GPR) and random forest (RF) with feature optimization to retrieve maize biomass in Changchun, Jilin province, Northeastern China. Three new predictors from each type of remote sensing data were proposed based on the correlations to biomass measured in June, July, and August 2018. The results showed that a predictor combined by vertical-horizontal polarization (VV), vertical-horizontal polarization (VH), and the difference of VH and VV (VH-VV) derived from S-1 images of June, July, and August, respectively, with GPR and RF, provided a more accurate estimation of biomass (R2 = 0.81–0.83, RMSE = 0.40–0.41 kg/m2) than the models based on single SAR polarization indices or their combinations, or optimized features (R2 = 0.04–0.39, RMSE = 0.84–1.08 kg/m2). Among the S-2 VIs, the GPR model using a combination of ratio vegetation index (RVI) of June, normalized different infrared index (NDII) of July, and normalized difference vegetation index (NDVI) of August achieved a result with R2 = 0.83 and RMSE = 0.39 kg/m2, much better than single VIs or their combination, or optimized features (R2 of 0.31–0.77, RMSE of 0.47–0.87 kg/m2). A BPV predictor, combined with leaf chlorophyll content (CAB) in June, canopy water content (CWC) in July, and fractional vegetation cover (FCOVER) in August, with RF, also yielded the highest accuracy (R2 = 0.85, RMSE = 0.38 kg/m2) compared to that of single BPVs or their combinations, or optimized subset. Overall, the three combined predictors were found to be significant contributors to improving the estimation accuracy of biomass with GPR and RF methods. This study clearly sheds new insights on the application of S-1 and S-2 data on maize biomass modeling.

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.

Background Intramuscular fat (IMF) content is a crucial determinant of beef quality and a key indicator in cattle breeding and production. However, the molecular regulatory mechanisms governing IMF deposition remain poorly understood. Results This study preliminarily explored the molecular mechanisms underlying IMF deposition by integrating weighted gene co-expression network analysis (WGCNA) and competitive endogenous RNA (ceRNA) network analysis. Sequencing of longissimus dorsi muscle samples from crossbred Wagyu cattle with varying IMF deposition levels revealed 172 differentially expressed circular RNAs (circRNAs), which were subsequently annotated and used to construct regulatory networks. Protein-protein interaction (PPI) network analysis predicted possible several lipid metabolism-related genes, including EZH2 , AKT3 , APP and SMARCA5 . By combining the miRNA and mRNA data from our previous studies, we constructed circRNA-mRNA coexpression networks and circRNA-miRNA-mRNA regulatory networks. Functional enrichment analysis revealed that the identified circRNAs are involved primarily in lipid metabolism-related pathways, including phosphatidylinositol metabolism and the cGMP-PKG signaling pathway. Additionally, several circRNAs were predicted to function as molecular sponges based on coexpression patterns. Conclusion This study provides novel insights into the molecular mechanisms underlying IMF deposition in hybrid cattle and provides candidate regulatory mechanisms for further validation in selective breeding.

The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m−2·month−1 (30–80%) and 0.13–0.35 kg C·m−2·year−1 (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Background SARS-CoV-2 viremia is associated with disease severity and high risk for in-hospital mortality. However, the impact of SARS-CoV-2 viremia on long-term outcomes in hospitalized patients with COVID-19 is poorly understood. Methods We conducted a prospective cohort study and recruited a group of older adult patients with COVID-19 admitted to pulmonary intermediate care units of Peking University Third Hospital during December 2022 and January 2023. The plasma level of SARS-CoV-2 RNA was determined by a standardized RT-PCR technique, and SARS-CoV-2 RNAemia was defined as a plasma viral load ≥ 50 copies/ml. In-hospital and follow-up (180-day) outcome data were collected. Results A total of 101 patients with an average of 80.4 years were recruited, and 63.4% of them were severe or very severe cases. Twenty-eight patients (27.7%) had SARS-CoV-2 RNAemia, with a median viral RNA load of 422.1 [261.3, 1085.6] copies/ml. Patients with SARS-CoV-2 RNAemia were more likely to develop critical cases and had a higher incidence of sepsis. Accordingly, they had a higher 180-day mortality (57.1% vs. 19.7%, P  < 0.001), as well as in-hospital mortality (50.0% vs. 13.7%, P  < 0.001), independent of age, disease severity, sepsis, lymphocyte count and C-Reactive protein. In addition, the risk for 180-day mortality increased with the SARS-CoV-2 RNA load in plasma. Plasma cytokines, including IL-6, IL-8 and IL-10, were higher in patients with SARS-CoV-2 RNAemia. Conclusions Our study indicates that SARS-CoV-2 RNAemia serves as a useful biomarker for predicting mortality, especially long-term mortality, in older adult patients hospitalized in pulmonary intermediate care units. Trial registration Chinese Clinical Trial Registry website (No. ChiCTR2300067434).

The regulatory axis plays a vital role in interpreting the information exchange and interactions among mammal organs. In this study on feed efficiency, it was hypothesized that a rumen-liver-muscle-fat ( RLMF ) regulatory axis exists and scrutinized the flow of energy along the RLMF axis employing consensus network analysis from a spatial transcriptomic standpoint. Based on enrichment analysis and protein-protein interaction analysis of the consensus network and tissue-specific genes, it was discovered that carbohydrate metabolism, energy metabolism, immune and inflammatory responses were likely to be the biological processes that contribute most to feed efficiency variation on the RLMF regulatory axis. In addition, clusters of genes related to the electron respiratory chain, including ND (2,3,4,4L,5,6), NDUF (A13, A7, S6, B3, B6), COX (1,3), CYTB, UQCR11, ATP (6,8) , clusters of genes related to fatty acid metabolism including APO (A1, A2, A4, B, C3), ALB, FG (A, G) , as well as clusters of the ribosomal-related gene including RPL (8,18A,18,15,13, P1) , the RPS (23,27A,3A,4X) , and the PSM (A1-A7, B6, C1, C3, D2-D4, D8 D9, E1) could be the primary effector genes responsible for feed efficiency variation. The findings demonstrate that high feed efficiency cattle, through the synergistic action of the regulatory axis RLMF , may improve the efficiency of biological processes (carbohydrate metabolism, protein ubiquitination, and energy metabolism). Meanwhile, high feed efficiency cattle might enhance the ability to respond to immunity and inflammation, allowing nutrients to be efficiently distributed across these organs associated with digestion and absorption, energy-producing, and energy-storing organs. Elucidating the distribution of nutrients on the RLMF regulatory axis could facilitate an understanding of feed efficiency variation and achieve the study on its molecular regulation.

Background The prevalence and risk factors for persistent candidemia among very low birth weight infants are poorly understood. This study aimed to investigate the epidemiology of persistent candidemia over a 4-year period in a neonatal intensive care unit (NICU) in Liuzhou, China. Methods We retrospectively extracted demographic data, risk factors, microbiological results and outcomes of very low birth weight infants with candidemia in our hospital between January 2012 and November 2015. Persistent candidemia was defined as a positive blood culture for > 5 days. Logistic regression was used to identify risk factors associated with persistent candidemia. Results Of 48 neonates with candidemia, 28 had persistent candidemia. Both mechanical ventilation and intubation were significantly associated with increased rates of persistent candidemia ( P  = 0.044 and 0.004, respectively). The case fatality rate for the persistent candidemia group was 14.3%. Conclusion The rate of persistent candidemia was high among very low birth weight neonates. Mechanical ventilation and intubation were the major factors associated with the development of persistent candidemia. This study highlights the importance of intensive prevention and effective treatment among neonates with persistent candidemia.

IntroductionIdiopathic pulmonary fibrosis (IPF) is a rare, chronic and progressive lung disease with a significant impact on patients’ quality of life. While much research has focused on disease mechanisms and treatment efficacy, limited attention has been paid to the landscape, unmet needs, patient experiences and quality of life. Understanding these aspects through the patient journey is essential for developing patient-centred therapeutic strategies. Therefore, this study aims to explore the status of diagnosis, treatment, the unmet needs and patient experiences of IPF in China through an analysis of the patient journey using mixed methods research. The findings in this study provide valuable insights to guide drug development, optimise clinical decision-making and support health technology assessments.Methods and analysisAn exploratory sequential mixed methods design will be used in two phases. In the qualitative phase, 50 patients with IPF and 15 experienced physicians will be recruited to complete in-depth interviews. Patient journey and unmet medical needs will be the focus of data collection. Based on the findings of the qualitative study, a structured questionnaire will be developed for the subsequent quantitative study. Data will be collected from 245 patients with IPF to quantitatively analyse critical points in the patient journey, quality of life, unmet needs and treatment expectations. The integration of patient experience data into the drug/intervention development lifecycle in this mixed methods research will enhance the relevance of IPF interventions, optimising disease management strategies and improving patient health outcomes.Ethics and disseminationThe study has been approved by the Peking University Third Hospital Medical Science Research Ethics Committee (2024-188-02). Prior to the study, study information will be provided, and consent will be obtained. Findings in this study will be disseminated in peer-reviewed publications and conferences.Trial registration numberNCT06629623.