Altered effective connectivity on rapid automatized naming deficits in Chinese children with developmental dyslexia: An rs-fMRI study with Ganger causality analysis.

Rapid Automatized Naming (RAN) is the core defect of developmental dyslexia (DD), requiring collaboration among brain areas to complete. However, it's still unclear which effective connectivity (EC) among brain areas are crucial for RAN deficits in Chinses children with DD. The current study aims to explore the EC among brain areas related to RAN deficits in Chinese children with DD. We recruited 36 Chinese children with DD and 64 typically developing (TD) children aged 8-12 to complete resting-state functional magnetic resonance imaging (rs-fMRI) scan. Granger causality analysis (GCA) was employed to analysis the EC among brain areas related to RAN, and to calculate the relationship between EC and RAN scores. Compared to TD group, the DD group exhibited significantly decreased EC from left precentral gyrus (PG) to right precuneus, left anterior cingulate and paracingulate gyrus (ACG), left calcarine and right angular, from left middle frontal gyrus (MFG) to left calcarine. Additionally, the DD group showed increased EC from right cuneus to left inferior frontal gyrus triangular part (IFGtri). The EC from left PG to left ACG was positively correlated with letters-RAN score. The results showed Chinese children with DD had both defect and compensatory mechanisms for their RAN deficits. The decreased EC output from left PG may be the core problem of the RAN deficits, which may influence the integration of visual-spatial information, attention, memory retrieval, and speech motor in speech production. The current study has important clinic implications for establishing intervention measures targeted brain.

Integrating Experimental and Computational Analyses for Mechanical Characterization of Titanium Carbide/Aluminum Metal Matrix Composites.

This study investigates the mechanical properties of titanium carbide/aluminum metal matrix composites (AMMCs) using both experimental and computational methods. Through accumulative roll bonding (ARB) and cryorolling (CR) processes, AA1050 alloy surfaces were reinforced with TiCp particles to create the Al-TiCp composite. The experimental analysis shows significant improvements in tensile strength, yield strength, elastic modulus, and hardness. The finite element analysis (FEA) simulations, particularly the microstructural modeling of RVE-1 (the experimental case model), align closely with the experimental results observed through scanning electron microscopy (SEM). This validation underscores the accuracy of the computational models in predicting the mechanical behavior under identical experimental conditions. The simulated elastic modulus deviates by 5.49% from the experimental value, while the tensile strength shows a 6.81% difference. Additionally, the simulated yield strength indicates a 2.85% deviation. The simulation data provide insights into the microstructural behavior, stress distribution, and particle-matrix interactions, facilitating the design optimization for enhanced performance. The study also explores the influence of particle shapes and sizes through Representative Volume Element (RVE) models, highlighting nuanced effects on stress-strain behavior. The microstructural evolution is examined via transmission electron microscopy (TEM), revealing insights regarding grain refinement. These findings demonstrate the potential of Al-TiCp composites for lightweight applications.

DNA Methylation Analysis of Growth Differences between Upright and Inverted Cuttings of Populus yunnanensis.

DNA methylation is an important mechanism for epigenetic modifications that have been shown to be associated with responses to plant development. Previous studies found that inverted Populus yunnanensis cuttings were still viable and could develop into complete plants. However, the growth status of inverted cuttings was weaker than that of upright cuttings, and the sprouting time of inverted cuttings was later than that of upright cuttings. There is currently no research on DNA methylation patterns in inverted cuttings of Populus yunnanensis. In this study, we detected genome-wide methylation patterns of stem tips of Populus yunnanensis at the early growth stage and the rapid growth stage by Oxford Nanopore Technologies (ONT) methylation sequencing. We found that the methylation levels of CpG, CHG, CHH, and 6mA were 41.34%, 33.79%, 17.27%, and 12.90%, respectively, in the genome of inverted poplar cuttings, while the methylation levels of the four methylation types were higher in the genome of upright poplar cuttings than in inverted cuttings, 41.90%, 34.57%, 18.09%, and 14.11%, suggesting important roles for DNA methylation in poplar cells. In all comparison groups, CpG-type methylation genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were annotated to pathways associated with carbon metabolism, ribosome biogenesis in eukaryotes, glycolysis/gluconeogenesis, pyruvate metabolism, and mRNA detection pathways, suggesting that different biological processes are activated in upright and inverted cuttings. The results show that methylation genes are commonly present in the poplar genome, but only a few of them are involved in the regulation of expression in the growth and development of inverted cuttings. From this, we screened the DET2 gene for significant differences in methylation levels in upright or inverted cuttings. The DET2 gene is a key gene in the Brassinolide (BRs) synthesis pathway, and BRs have an important influence on the growth and development process of plants. These results provide important clues for studying DNA methylation patterns in P. yunnanensis.

Development of a micro-combined heat and power powered by an opposed-piston engine in building applications.

Residential homes and light commercial buildings usually require substantial heat and electricity simultaneously. A combined heat and power system enables more efficient and environmentally friendly energy usage than that achieved when heat and electricity are produced in separate processes. However, due to financial and space constraints, residential and light commercial buildings often limit the use of traditional large-scale industrial equipment. Here we develop a micro-combined heat and power system powered by an opposed-piston engine to simultaneously generate electricity and provide heat to residential homes or light commercial buildings. The developed prototype attains the maximum AC electrical efficiency of 35.2%. The electrical efficiency breaks the typical upper boundary of 30% for micro-combined heat and power systems using small internal combustion engines (i.e., <10 kW). Moreover, the developed prototype enables maximum combined electrical and thermal efficiencies greater than 93%. The prototype is optimally designed for natural gas but can also run renewable biogas and hydrogen, supporting the transition from current conventional fossil fuels to zero carbon emissions in the future. The analysis of the unit's decarbonization and cost-saving potential indicate that, except for specific locations, the developed prototype might excel in achieving decarbonization and cost savings primarily in US northern and middle climate zones.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

Transcriptional regulation of the yersiniabactin receptor fyuA gene by the ferric uptake regulator in Klebsiella pneumoniae NTUH-K2044.

The ferric uptake regulator (Fur) is a global regulator that influences the expression of virulence genes in Klebsiella pneumoniae. Bioinformatics analysis suggests Fur may involve in iron acquisition via the identified regulatory box upstream of the yersiniabactin receptor gene fyuA. To observe the impact of the gene fyuA on the virulence of K. pneumoniae, the gene fyuA knockout strain and complementation strain were constructed and then conducted a series of phenotypic experiments including chrome azurol S (CAS) detection, crystal violet staining, and wax moth virulence experiment. To examine the regulatory relationship between Fur and the gene fyuA, green fluorescent protein (GFP) reporter gene fusion assay, real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR), gel migration assay (EMSA), and DNase I footprinting assay were used to clarify the regulatory mechanism of Fur on fyuA. CAS detection revealed that the gene fyuA could affect the generation of iron carriers in K. pneumoniae. Crystal violet staining experiment showed that fyuA could positively influence biofilm formation. Wax moth virulence experiment indicated that the deletion of the fyuA could weaken bacterial virulence. GFP reporter gene fusion experiment and RT-qPCR analysis revealed that Fur negatively regulated the expression of fyuA in iron-sufficient environment. EMSA experiment demonstrated that Fur could directly bind to the promoter region of fyuA, and DNase I footprinting assay further identified the specific binding site sequences. The study showed that Fur negatively regulated the transcriptional expression of fyuA by binding to upstream of the gene promoter region, and then affected the virulence of K. pneumoniae.

Evaluating the Significance of Fasting C-peptide in Conjunction with the Insulin Resistance Index for Assessing Hepatic Fibrosis in Patients with Type 2 Diabetes Mellitus and Nonalcoholic Fatty Liver Disease.

Background: Non-alcoholic fatty liver disease (NAFLD) has reached pandemic proportions globally, particularly affecting individuals with type 2 diabetes mellitus (T2DM). Objective: Our study aims to elucidate the diagnostic value of fasting C-peptide in combination with insulin resistance for assessing hepatic fibrosis in patients with T2DM and comorbid NAFLD. Design: This was a retrospective study. Setting: The study was conducted at the Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine. Participants: The research involved 76 type 2 diabetes mellitus patients with nonalcoholic fatty liver disease, diagnosed at our hospital from April 2020 to October 2022. Patients were categorized into the non-progressive hepatic fibrosis group (n = 64) and progressive hepatic fibrosis group (n = 12) based on fibrosis-4 value. Interventions: General data, systolic/diastolic blood pressure, fasting plasma glucose, fasting C-peptide, fasting insulin, glycosylated hemoglobin, uric acid, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, aspartate transaminase, alanine transaminase, and γ-glutamyl transferase were collected. Insulin resistance was calculated using a designated formula. Primary Outcomes Measures: The predictive impact of fasting C-peptide in combination with insulin resistance was evaluated through receiver operating characteristic curves. Results: The age, body mass index, fasting C-peptide, fasting insulin, aspartate transaminase, and insulin resistance showed a significant increase in the progressive hepatic fibrosis group compared to the non-progressive group (P = .006, P = .014, P < .001, P < .001, P = .004, and P = .021). The combination's sensitivity demonstrated an elevation compared to fasting C-peptide or insulin resistance alone (P = .005). Conclusions: Fasting C-peptide in combination with insulin resistance proves to have a substantial predictive impact on hepatic fibrosis in type 2 diabetes mellitus patients with nonalcoholic fatty liver disease, holding valuable clinical diagnostic potential.

Dietary intake and gastrointestinal symptoms are altered in children with Autism Spectrum Disorder: the relative contribution of autism-linked traits.

BACKGROUND: Dietary and gastrointestinal (GI) problems have been frequently reported in autism spectrum disorder (ASD). However, the relative contributions of autism-linked traits to dietary and GI problems in children with ASD are poorly understood. This study firstly compared the dietary intake and GI symptoms between children with ASD and typically developing children (TDC), and then quantified the relative contributions of autism-linked traits to dietary intake, and relative contributions of autism-linked traits and dietary intake to GI symptoms within the ASD group. METHODS: A sample of 121 children with ASD and 121 age-matched TDC were eligible for this study. The dietary intake indicators included food groups intakes, food variety, and diet quality. The autism-linked traits included ASD symptom severity, restricted repetitive behaviors (RRBs), sensory profiles, mealtime behaviors, and their subtypes. Linear mixed-effects models and mixed-effects logistic regression models were used to estimate the relative contributions. RESULTS: Children with ASD had poorer diets with fewer vegetables/fruits, less variety of food, a higher degree of inadequate/unbalanced dietary intake, and more severe constipation/total GI symptoms than age-matched TDC. Within the ASD group, compulsive behavior (a subtype of RRBs) and taste/smell sensitivity were the only traits associated with lower vegetables and fruit consumption, respectively. Self-injurious behavior (a subtype of RRBs) was the only contributing trait to less variety of food. Limited variety (a subtype of mealtime behavior problems) and ASD symptom severity were the primary and secondary contributors to inadequate dietary intake, respectively. ASD symptom severity and limited variety were the primary and secondary contributors to unbalanced dietary intake, respectively. Notably, unbalanced dietary intake was a significant independent factor associated with constipation/total GI symptoms, and autism-linked traits manifested no contributions. CONCLUSIONS: ASD symptom severity and unbalanced diets were the most important contributors to unbalanced dietary intake and GI symptoms, respectively. Our findings highlight that ASD symptom severity and unbalanced diets could provide the largest benefits for the dietary and GI problems of ASD if they were targeted for early detection and optimal treatment.

Prodigiosin as an Antibiofilm Agent against the Bacterial Biofilm-Associated Infection of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is known to generate bacterial biofilms that increase antibiotic resistance. With the increase of multi-drug resistance in recent years, the formulation of a new therapeutic strategy has seemed urgent. Preliminary findings show that Prodigiosin (PG), derived from chromium-resistant Serratia marcescens, exhibited efficient anti-biofilm activity against Staphylococcus aureus. However, its anti-biofilm activity against P. aeruginosa remains largely unexplored. The anti-biofilm activity of PG against three clinical single drug-resistant P. aeruginosa was evaluated using crystal violet staining, and the viability of biofilms and planktonic cells were also assessed. A model of chronic lung infection was constructed to test the in vivo antibiofilm activity of PG. The results showed that PG inhibited biofilm formation and effectively inhibited the production of pyocyanin and extracellular polysaccharides in vitro, as well as moderated the expression of interleukins (IL-1ß, IL-6, IL-10) and tumor necrosis factor (TNF-α) in vivo, which might be attributed to the downregulation of biofilm-related genes such as algA, pelA, and pslM. These findings suggest that PG could be a potential treatment for drug-resistant P aeruginosa and chronic biofilm infections.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

Effect of Fiber-Rich Diet and Rope Skipping on Memory, Executive Function, and Gut Microbiota in Young Adults: A Randomized Controlled Trial.

SCOPE: To investigate the effects of fiber-rich diets (FDs), rope skipping (RS), and the combination of these two interventions (fiber-rich diet with rope skipping [FD-RS]) on memory, executive function in young adults, and to explore their relationship with gut microbiota. MATERIALS AND RESULTS: The study is a 12-week parallel-design randomized controlled trial in which 120 undergraduates (19 ± 1 years) are randomized to FD (fiber ≥ 20 g day-1 ), RS (3 × 2000 times per week), FD-RS or control group (n = 30 per group). Memory and executive function are assessed by scales, and stool samples are collected at baseline and after the intervention. FD group and FD-RS group show fewer prospective and retrospective subjective memory impairments than the control group, but there is no significant difference between FD-RS and the intervention alone (FD or RS). No obvious change in executive function is observed throughout the trial. In terms of the gut microbiota, the α-diversity does not increase, but the microbial community evenness improves after the RS and FD intervention. Additionally, the relative abundance of phylum Firmicutes and genera Faecalibacterium, Eubacterium_coprostanoligenes_group in the RS group and NK4A214_group in the FD group significantly increase. In the RS group, a correlation is found between the increase in microbial evenness and the improvement in retrospective memory. CONCLUSION: The FD and FD-RS have beneficial effects on memory in young adults. Meanwhile, FD and RS can improve the microbial evenness and increase several beneficial genera of phylum Firmicutes.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

BACKGROUND: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PURPOSE: This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. METHODS: A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). RESULTS: The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis. CONCLUSIONS: The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Risk-based detection as a cost-effective strategy to reduce foodborne illness due to salmonella.

Background: Salmonella, a widespread pathogen, poses a significant threat to global food safety, leading to foodborne diseases and substantial economic losses. The timely and accurate detection of foodborne pathogens is pivotal for averting food contamination and outbreaks across the food production chain. This study assesses the cost-effectiveness of traditional culture-based methods versus risk-based approaches, incorporating polymerase chain reaction (PCR) for Salmonella detection. Methods: We employed a stochastic scenario tree model to simulate scenarios based on the sampling inspection plan for raw aquatic products conducted by the Guangzhou Center for Disease Control and Prevention from 2018 to 2020. Various detection methods (culture or PCR) were applied to these aquatic products based on their categorized risk level. Sensitivity values were derived from published data, and incremental cost-effectiveness ratios were used to compare the different scenarios against the traditional culture method. Results: A total of 360 samples were collected for analysis. The cost of culture-based detection alone amounted to 125,423.20 Chinese Yuan (CNY) and yielded nine instances of positive Salmonella detections. The risk-based detection strategy, which combined the more sensitive PCR method with high-risk sample characteristics, while employing the culture method for the remaining combinations, imposed a total cost of 128,775.83 CNY and yielded ten positive detections. This approach cost approximately 3391.74 CNY per additional positive sample detected compared to the culture method alone. Meanwhile, PCR-only detection imposed a total cost of 62,960.03 CNY. Conclusions: In comparison to traditional culture-based methods, both the risk-based detection strategy and the PCR-only approach demonstrated superior capabilities with respect to detecting contaminated aquatic products. Implementing risk-based detection strategies can enhance cost-effectiveness, not only ensuring food safety but also reducing the incidence and economic burden of foodborne diseases.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Multi-omics analyses demonstrate the modulating role of gut microbiota on the associations of unbalanced dietary intake with gastrointestinal symptoms in children with autism spectrum disorder.

Our previous work revealed that unbalanced dietary intake was an important independent factor associated with constipation and gastrointestinal (GI) symptoms in children with autism spectrum disorder (ASD). Growing evidence has shown the alterations in the gut microbiota and gut microbiota-derived metabolites in ASD. However, how the altered microbiota might affect the associations between unbalanced diets and GI symptoms in ASD remains unknown. We analyzed microbiome and metabolomics data in 90 ASD and 90 typically developing (TD) children based on 16S rRNA and untargeted metabolomics, together with dietary intake and GI symptoms assessment. We found that there existed 11 altered gut microbiota (FDR-corrected P-value <0.05) and 397 altered metabolites (P-value <0.05) in children with ASD compared with TD children. Among the 11 altered microbiota, the Turicibacter, Coprococcus 1, and Lachnospiraceae FCS020 group were positively correlated with constipation (FDR-corrected P-value <0.25). The Eggerthellaceae was positively correlated with total GI symptoms (FDR-corrected P-value <0.25). More importantly, three increased microbiota including Turicibacter, Coprococcus 1, and Eggerthellaceae positively modulated the associations of unbalanced dietary intake with constipation and total GI symptoms, and the decreased Clostridium sp. BR31 negatively modulated their associations in ASD children (P-value <0.05). Together, the altered microbiota strengthens the relationship between unbalanced dietary intake and GI symptoms. Among the altered metabolites, ten metabolites derived from microbiota (Turicibacter, Coprococcus 1, Eggerthellaceae, and Clostridium sp. BR31) were screened out, enriched in eight metabolic pathways, and were identified to correlate with constipation and total GI symptoms in ASD children (FDR-corrected P-value <0.25). These metabolomics findings further support the modulating role of gut microbiota on the associations of unbalanced dietary intake with GI symptoms. Collectively, our research provides insights into the relationship between diet, the gut microbiota, and GI symptoms in children with ASD.

How Do Heterogeneous Networks Affect a Firm's Innovation Performance? A Research Analysis Based on Clustering and Classification.

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry-University-Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.

High-fiber diet and rope-skipping benefit cardiometabolic health and modulate gut microbiota in young adults: A randomized controlled trial.

Previous studies have shown that high intake of dietary fiber (DF) and efficient levels of physical activity are beneficial for cardiometabolic health in middle-aged and elderly populations with cardiometabolic disease. However, evidence from young adults with low cardiometabolic risk is lacking. This study aimed to investigate the effects of various interventions including a high-fiber (HF) diet and the rope-skipping (RS) exercise on cardiometabolic risk factors (CRFs) and the composition of the gut microbiota in young adults. A 12-week parallel-designed randomized controlled trial was conducted in undergraduates (n = 96), who were randomly assigned to the HF group (≥20 g/d DF), the RS group (2000 jumps/week), and the control (CON) group. Among the 84 people who completed the trial, measurements of anthropometric characteristics, biochemical parameters, and gut microbiota were taken at the beginning and end of the intervention. After the intervention, the RS exercise led to a significant decrease in the heart rate and triglyceride levels compared to the CON group (all P < 0.05), but there was no significant difference in CRFs between the HF and CON groups. When compared to baseline, the 12-week HF diet intervention resulted in an increase in fat-free mass, and a decrease in the percentage of body fat and waist circumference (all P < 0.05). With regard to gut microbiota alterations after intervention, we found that compared with the CON group, the relative abundance of Lactobacillus decreased significantly in both the HF group and the RS group, Muribaculaceae decreased in the RS group, and Eubacterium_coprostanoligenes_group decreased in the HF group (all P < 0.05). Finally, shifts in 7 metabolic pathways were detected in the RS group using predictive functional profiling, while only one pathway was altered in the HF group (all P < 0.05). In conclusion, the RS exercise improved body composition compared to the CON group in young adults, while the HF diet just enhanced CRFs in contrast to baseline. Furthermore, both RS and HF interventions altered Lactobacillus and various other gut microbiota. The results indicated that the HF diet and RS exercise could partly benefit cardiometabolic health and modulate gut microbiota in young adults. Trial registration: ClinicalTrials.gov, NCT04834687.