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Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.

ObjectivesTo build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM).MethodsIn this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors.ResultsThe radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis.ConclusionsRadiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features.Key Points• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts.• All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features.• Combing clinical factors with radiomics features did not benefit the prediction performance.

Background This study aimed to evaluate the significance of MRI-based radiomics model derived from high-resolution T2-weighted images (T2WIs) in predicting tumor pathological features of rectal cancer. Methods A total of 152 patients with rectal cancer who underwent surgery without any neoadjuvant therapy between March 2017 and September 2018 were included retrospectively. The patients were scanned using a 3-T magnetic resonance imaging, and high-resolution T2WIs were obtained. Lesions were delineated, and 1029 radiomics features were extracted. Least absolute shrinkage and selection operator was used to select features, and multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) were trained using fivefold cross-validation to build a prediction model. The diagnostic performance of the prediction models was assessed using the receiver operating characteristic curves. Results A total of 1029 features were extracted, and 15, 11, and 11 features were selected to predict the degree of differentiation, T stage, and N stage, respectively. The best performance of the radiomics model for the degree of differentiation, T stage, and N stage was obtained by SVM [area under the curve (AUC), 0.862; 95% confidence interval (CI), 0.750–0.967; sensitivity, 83.3%; specificity, 85.0%], MLP (AUC, 0.809; 95% CI, 0.690–0.905; sensitivity, 76.2%; specificity, 74.1%), and RF (AUC, 0.746; 95% CI, 0.622-0.872; sensitivity, 79.3%; specificity, 72.2%). Conclusion This study demonstrated that the high-resolution T2WI–based radiomics model could serve as pretreatment biomarkers in predicting pathological features of rectal cancer.

The response of tropical cyclone (TC) destructive potential to global warming is an open issue. A number of previous studies have ignored the effect of TC size change in the context of global warming, which resulted in a significant underestimation of the TC destructive potential. The lack of reliable and consistent historical data on TC size limits the confident estimation of the linkage between the observed trend in TC size and that in sea surface temperature (SST) under the background of global climate warming. A regional atmospheric model is used in the present study to investigate the response of TC size and TC destructive potential to increases in SST. The results show that a large-scale ocean warming can lead to not only TC intensification but also TC expansion. The TC size increase in response to the ocean warming is possibly attributed to the increase in atmospheric convective instability in the TC outer region below the middle troposphere, which facilitates the local development of grid-scale ascending motion, low-level convergence and the acceleration of tangential winds. The numerical results indicate that TCs will become stronger, larger, and unexpectedly more destructive under global warming.

Monitoring the horizontal distribution of PM2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O4) differential slant column densities (O4 dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O4 dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM2.5 pollution transport event was captured: PM2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM2.5 distributions.

Background Although many studies have been done to reveal artificial selection signatures in commercial and indigenous chickens, a limited number of genes have been linked to specific traits. To identify more trait-related artificial selection signatures and genes, we re-sequenced a total of 85 individuals of five indigenous chicken breeds with distinct traits from Yunnan Province, China. Results We found 30 million non-redundant single nucleotide variants and small indels (< 50 bp) in the indigenous chickens, of which 10 million were not seen in 60 broilers, 56 layers and 35 red jungle fowls (RJFs) that we compared with. The variants in each breed are enriched in non-coding regions, while those in coding regions are largely tolerant, suggesting that most variants might affect cis -regulatory sequences. Based on 27 million bi-allelic single nucleotide polymorphisms identified in the chickens, we found numerous selective sweeps and affected genes in each indigenous chicken breed and substantially larger numbers of selective sweeps and affected genes in the broilers and layers than previously reported using a rigorous statistical model. Consistent with the locations of the variants, the vast majority (~ 98.3%) of the identified selective sweeps overlap known quantitative trait loci (QTLs). Meanwhile, 74.2% known QTLs overlap our identified selective sweeps. We confirmed most of previously identified trait-related genes and identified many novel ones, some of which might be related to body size and high egg production traits. Using RT-qPCR, we validated differential expression of eight genes ( GHR, GHRHR, IGF2BP1, OVALX, ELF2, MGARP, NOCT, SLC25A15 ) that might be related to body size and high egg production traits in relevant tissues of relevant breeds. Conclusion We identify 30 million single nucleotide variants and small indels in the five indigenous chicken breeds, 10 million of which are novel. We predict substantially more selective sweeps and affected genes than previously reported in both indigenous and commercial breeds. These variants and affected genes are good candidates for further experimental investigations of genotype-phenotype relationships and practical applications in chicken breeding programs.

Rfn (formaldehyde/nitrogen dioxide) is a common indicator based on satellite observations used to classify ozone formation sensitivity. However, it may underestimate anthropogenic volatile organic compounds (VOCs) in heavily polluted cities when only formaldehyde (HCHO) is used in Rfn to measure VOCs, since it is mainly derived from natural sources worldwide. In this study, we used multiaxis differential optical absorption spectroscopy to acquire tropospheric observations of nitrogen dioxide (NO2), HCHO and glyoxal (CHOCHO) in Beijing from 1 April 2019 to 31 March 2020. Combined with VOCs detected simultaneously by gas chromatography—mass spectrometry and proton transfer reaction–time-of-flight/mass spectrometry near the ground, we evaluated the representativeness of HCHO column densities on total VOCs (TVOC) in equivalent propylene concentrations, which is called reactivity. The results showed that there were significant seasonal differences in the response of HCHO to TVOC reactivity, with fitting slopes of 2.3 (spring), 2.6 (summer), 2.9 (autumn) and 1.0 (winter) in the four seasons, respectively. Since CHOCHO can be used to partly characterize the contribution of anthropogenic VOC emissions and demonstrated a better response to TVOC reactivity in winter, with fitting slopes of 0.2 (spring), 0.2 (summer), 0.2 (autumn) and 0.5 (winter) in the four seasons, respectively, we introduced CHOCHO to construct a new indicator (HCHO + 6 × CHOCHO). The fitting slopes of the four seasons were more similar, being 3.2 (spring), 3.6 (summer), 4.0 (autumn) and 4.0 (winter). The ratio of the new indicator to NO2, Rmn ((HCHO + 6 × CHOCHO)/NO2), was used to reclassify the ozone formation sensitivity of urban areas in North China, revealing that it is a transition regime before 1300 LST (LST = UST + 8) and an NOx-limited regime afterwards. Rmn improved the sensitivity from the VOC-limited regime to the NOx-limited regime, enhancing the sensitivity of NOx and providing new robust support for ozone pollution prevention and control.

Studying the vertical and horizontal distribution of particulate matter at the hectometer scale in the atmosphere is essential for understanding its sources, transportation, and transmission and its impact on human health. In this study, a method was developed based on hyperspectral instrumentation to obtain both vertical and horizontal distributions of aerosol extinction by employing multiple azimuth angles, selecting optimized elevation angles, and reducing the acquisition time of individual spectra. This method employed observations from different azimuth angles to represent particulate matter concentrations in various directions. The correlation coefficient between the hyperspectral observations and in-situ measurement was 0.627. Observations indicated that the aerosol extinction profile followed an exponential decay, with most aerosols confined below 1 km, implying a likely origin from local near-surface emissions. The horizontal distribution indicated that the northeastern urban areas and the eastern rural areas were the primary regions with high concentrations of particulate matter. The observational evidence suggests the presence of two potential emission sources within the study area. Moreover, health risk results indicated that even within the same town, differences of particulate matter concentration and population density could lead to varying health exposure risks. For instance, in the 200° and 210° directions, which represent adjacent urban areas less than 1 km apart, the number of PM2.5-related illness cases in the 210° direction was 20.83% higher than that in the 200° direction.

Background Although multiple chicken genomes have been assembled and annotated, the numbers of protein-coding genes in chicken genomes and their variation among breeds are still uncertain due to the low quality of these genome assemblies and limited resources used in their gene annotations. To fill these gaps, we recently assembled genomes of four indigenous chicken breeds with distinct traits at chromosome-level. In this study, we annotated genes in each of these assembled genomes using a combination of RNA-seq- and homology-based approaches. Results We identified varying numbers (17,497–17,718) of protein-coding genes in the four indigenous chicken genomes, while recovering 51 of the 274 “missing” genes in birds in general, and 36 of the 174 “missing” genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in the four breeds, we found 571 ~ 627 protein-coding genes in each genome, which were missing in the annotations of the reference chicken genomes (GRCg6a and GRCg7b/w). After removing redundancy, we ended up with a total of 1,420 newly annotated genes (NAGs). The NAGs tend to be found in subtelomeric regions of macro-chromosomes (chr1 to chr5, plus chrZ) and middle chromosomes (chr6 to chr13, plus chrW), as well as in micro-chromosomes (chr14 to chr39) and unplaced contigs, where G/C contents are high. Moreover, the NAGs have elevated quadruplexes G frequencies, while both G/C contents and quadruplexes G frequencies in their surrounding regions are also high. The NAGs showed tissue-specific expression, and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. Most of the NAGs were also encoded in the reference chicken genomes, thus, these genomes might harbor more genes than previously thought. Conclusion The NAGs are widely distributed in wild, indigenous and commercial chickens, and they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

Background Melanin is an important antioxidant in food and has been used in medicine and cosmetology. Chicken meat with high melanin content from black-boned chickens have been considered a high nutritious food with potential medicinal properties. The molecular mechanism of melanogenesis of skeletal muscle in black-boned chickens remain poorly understood. This study investigated the biological gene-metabolite associations regulating the muscle melanogenesis pathways in Wuliangshan black-boned chickens with two normal boned chicken breeds as control. Results We identified 25 differentially expressed genes and 11 transcription factors in the melanogenesis pathways. High levels of the meat flavor compounds inosine monophosphate, hypoxanthine, lysophospholipid, hydroxyoctadecadienoic acid, and nicotinamide mononucleotide were found in Wuliangshan black-boned chickens. Conclusion Integrative analysis of transcriptomics and metabolomics revealed the dual physiological functions of the PDZK1 gene, involved in pigmentation and/or melanogenesis and regulating the phospholipid signaling processes in muscle of black boned chickens.