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Abstract The biopharmaceutical sector is of considerable interest during the COVID-19 pandemic. This study aims to investigate the biopharmaceutical sector using the Shenwan Industry Classification and provides insights into investment strategies. We combine factor and cluster analyses to reduce data dimensions and detect their latent similarities. Specifically, the biopharmaceutical sector is divided into six categories based on second-level industry classification. It is observed that medical devices, medical services, biological products, and chemical pharmaceuticals maintained their upward tendency, while Chinese medicine and pharmaceutical commerce declined slightly. We also develop optimal investment strategies using various metrics for different investor types.

Abstract An order of addition experiment is an experiment to study how the order of addition of components affect the results, with the objective of predicting and determining the optimal order of addition of components. Order of addition experiment are also commonly used in the drug combination therapy, where experimenting with all drugs combinations is unaffordable. To solve this problem, we constructed a new design table, two-level component factorial design table (TLCF), which combine the component orthogonal array design table and the two-level partial factorial design table by matrix product. TLCF can explore the order and dosage effect of components on the results and can greatly reduce the number of experiments. We also prove that the relative D-efficiency of the TLCF can reach 100% and solve an explicit expression for the D-efficiency of the full design. In the simulation experiment, we compare the D-efficiency of the TLCF with the random design table to prove the superiority of TLCF. Finally, we give a treatment plan for the combination of three drugs for glioblastoma based on the TLCF, which provides a new perspective for the precision treatment of patients.

Myogenous temporomandibular disorder (M-TMD) is one of the main subtypes of temporomandibular disorder (TMD) and typically manifests as masticatory myofascial pain; the incidence of TMD has been increasing annually in recent years. Botulinum toxin type A (BTX-A) is a potent neurotoxin produced by Clostridium botulinum. BTX-A inhibits the release of acetylcholine from the presynaptic membrane, thereby blocking neuromuscular junction signaling. The noncosmetic application of BTX-A in the oral and maxillofacial regions is a prominent research topic. In recent years, an increasing number of studies have focused on the application of BTX-A in the treatment of M-TMD. The results of a literature review revealed that an appropriate dose (10-50 U unilaterally) of BTX-A administered in a single injection into the masticatory muscles can effectively treat myalgia over a period of 3-6 months. Common adverse effects, such as masticatory weakness and facial paralysis, are transient and can be avoided by standardized injection techniques. However, there is a lack of standardized guidelines for injection techniques in clinical practice.

Abstract Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.

Abstract Background The emerging of the C2 isthmus screw fixation technique is gaining popularity in the setting of atlantoaxial dislocation or other conditions requiring fixation of C2. However, the biomechanical stability of this fixation is poorly understood. Purpose To compare and elucidate the biomechanical stability of C2 pedicle screw (C2PS), C2 isthmus screw (C2IS) and C2 short isthmus screw (C2SIS) fixation techniques in atlantoaxial dislocation (AAD). Method A three-dimensional finite element model (FEM) from occiput to C3 was established and validated from a healthy male volunteer. Three FEMs, C1 pedicle screw (PS)-C2PS, C1PS-C2IS, C1PS-C2SIS were also constructed. The range of motion (ROM) and the maximum von Mises stress under flexion, extension, lateral bending and axial rotation loading were analyzed and compared. The pullout strength of the three fixations for C2 was also evaluated. Result C1PS-C2IS model showed the greatest decrease in ROM with flexion, extension, lateral bending and axial rotation. C1PS-C2PS model showed the least ROM reduction under all loading conditions than both C2IS and C2SIS. The C1PS-C2PS model had the largest von Mises stress on the screw under all directions followed by C1PS-C2SIS, and lastly the C1PS-C2IS. Under axial rotation and lateral bending loading, the three models showed the maximum and minimum von Mises stress on the screw respectively. The stress of the three models was mainly located in the connection of the screw and rod. Overall, the maximum screw pullout strength for C2PS, C2IS and C2SIS were 729.41N, 816.62N, 640.54N respectively. Conclusion In patients with atlantoaxial dislocations, the C2IS fixation provided comparable stability, with no significant stress concentration. Furthermore, the C2IS had sufficient pullout strength when compared with C2PS and C2SIS. C2 isthmus screw fixation may be a biomechanically favourable option in cases with AAD. However, future clinical trials are necessary for the evaluation of the clinical outcomes of this technique.

Abstract Background Ossification of the posterior longitudinal ligament (OPLL), an emerging heterotopic ossification disease, causes spinal cord compression, resulting in motor and sensory dysfunction. The etiology of OPLL remains unclear but may involve integrin αVβ3 regulating the process of osteogenesis and angiogenesis. In this study, we focused on the role of integrin αVβ3 in OPLL and explored the underlying mechanism by which the c(RGDyk) peptide acts as a potent and selective integrin αVβ3 inhibitor to inhibit osteogenesis and angiogenesis in OPLL. Methods OPLL or control ligament samples were collected in surgery. For OPLL samples, RNA-sequencing results revealed activation of the integrin family, particularly integrin αVβ3. Integrin αVβ3 expression was detected by qPCR, Western blotting, and immunohistochemical analysis. Fluorescence microscopy was used to observe the targeted inhibition of integrin αVβ3 by the c(RGDyk) peptide on ligaments fibroblasts (LFs) derived from patients with OPLL and endothelial cells (ECs). The effect of c(RGDyk) peptide on the ossification of pathogenic LFs was detected using qPCR, Western blotting. Alkaline phosphatase staining or alizarin red staining were used to test the osteogenic capability. The effect of the c(RGDyk) peptide on angiogenesis was determined by EC migration and tube formation assays. The effects of the c(RGDyk) peptide on heterotopic bone formation were evaluated by micro-CT, histological, immunohistochemical, and immunofluorescence analysis in vivo. Results The results indicated that after being treated with c(RGDyk), the osteogenic differentiation of LFs was significantly decreased. Moreover, the c(RGDyk) peptide inhibited the migration of ECs and thus prevented the nutritional support required for osteogenesis. Furthermore, the c(RGDyk) peptide inhibited ectopic bone formation in mice. Mechanistic analysis revealed that c(RGDyk) peptide could inhibit osteogenesis and angiogenesis in OPLL by targeting integrin αVβ3 and regulating the FAK/ERK pathway. Conclusions Therefore, the integrin αVβ3 appears to be an emerging therapeutic target for OPLL, and the c(RGDyk) peptide has dual inhibitory effects that may be valuable for the new therapeutic strategy of OPLL.

Abstract Cuproptosis-related genes (CRGs) are important for tumor development. However, the functions of CRGs across cancers remain obscure. We performed a pan-cancer investigation to reveal the roles of CRGs across cancers. In an analysis of 26 cancers, 12 CRGs were differentially expressed, and those CRGs were found to have prognostic value across different cancer types. The expression of CRGs exhibited varied among tumors of 6 immune subtypes and were significantly correlated with the 16 sensitivities of drugs. The expression of CRGs were highly correlated with immunological subtype and tumor microenvironment (TME) of prostate cancer. We also established CRGs-related prognostic signatures that closely correlated with prognosis and drug sensitivity of prostate cancer patients. Single-cell RNA-seq revealed that several CRGs were enriched in the cancer cells. Finally, an in vitro experiment showed that elesclomol, a cuproptosis inducer, targets ferredoxin 1 and suppress cell viability in prostate cancer cells. In conclusion, we carried out a comprehensive investigation for determining CRGs in differential expression, prognosis, immunological subtype, TME, and cancer treatment sensitivity across 26 malignancies; and validated the results in prostate cancer. Our research improves pan-cancer knowledge of CRGs and identifies more effective immunotherapy strategies.

Abstract Laser-inscribed graphene (LIG), initially developed for graphene supercapacitors, has found widespread use in sensor research and development, particularly as a platform for low-cost electrochemical sensing. However, batch-to-batch variation in LIG fabrication introduces uncertainty that cannot be adequately tracked during manufacturing process, limiting scalability. Therefore, there is an urgent need for robust quality control (QC) methodologies to identify and select similar and functional LIG electrodes for sensor fabrication. For the first time, we have developed a statistical workflow and an open-source hierarchical clustering tool for QC analysis in LIG electrode fabrication. The QC process was challenged with multi-operator cyclic voltammetry (CV) data for bare and metalized LIG. As a proof of concept, we employed the developed QC process for laboratory-scale manufacturing of LIG-based biosensors. The study demonstrates that our QC process can rapidly identify similar LIG electrodes from large batches (n ≥ 36) of electrodes, leading to a reduction in biosensor measurement variation by approximately 13% compared to the control group without QC. The statistical workflow and open-source code presented here provide a versatile toolkit for clustering analysis, opening a pathway toward scalable manufacturing of LIG electrodes in sensing. In addition, we establish a data repository for further study of LIG variation.

Background: Paraquat (PQ) is a widely used herbicide that poisons human by accident or intentional ingestion. PQ poisoning causes systemic inflammatory response syndrome (SIRS) resulting in acute lung injury (ALI) with an extremely high mortality rate. Blood trematode Schistosoma japonicum-produced cystatin (Sj-Cys) is a strong immunomodulatory protein that has been experimentally used to treat inflammation related diseases. In this study, Sj-Cys recombinant protein (rSj-Cys) was used to treat PQ-induced lung injury and the immunological mechanism underlying the therapeutic effect was investigated. Methods: PQ-induced acute lung injury mouse model was established by intraperitoneally injection of 20 mg/kg of paraquat. The poisoned mice were treated with rSj-Cys and the survival rate was observed up to 7 days compared with the group without treatment. The pathological changes of PQ-induced lung injury were observed by examining the histochemical sections of affected lung tissue and the wet to dry ratio of lung as a parameter for inflammation and edema. The levels of the inflammation related cytokines IL-6 and TNF-α and regulatory cytokines IL-10 and TGF-β were measured in sera and in affected lung tissue using ELISA and their mRNA levels in lung tissue using RT-PCR. The macrophages expressing iNOS were determined as M1 and those expressing Arg-1 as M2 macrophages. The effect of rSj-Cys on the transformation of inflammatory M1 to regulatory M2 macrophages was measured in affected lung tissue in vivo (EKISA and RT-PCR) and in MH-S cell line in vitro (flow cytometry). The expression levels of TLR2 and MyD88 in affected lung tissue were also measured to determine their role in the therapy of rSj-Cys on PQ-induced lung injury. Result: We identified that treatment with rSj-Cys significantly improved the survival rate of mice with PQ-induced lung injury from 30 % (untreated) to 80 %, reduced the pathological damage of poisoning lung tissue, associated with significantly reduced levels of proinflammatory cytokines (IL-6 from 1490 to 590 pg/ml, TNF-α from 260 to 150 pg/ml) and increased regulatory cytokines (IL-10 from360 to 550 pg/ml, and TGF-β from 220 to 410 pg/ml) in both sera (proteins) and affected lung tissue (proteins and mRNAs). The polarization of macrophages from M1to M2 type was found to be involved in the therapeutic effect of rSj-Cys on the PQ-induced acute lung injury, possibly through inhibiting TLR2/MyD88 signaling pathway. Conclusions: Our study demonstrated the therapeutic effect of rSj-Cys on PQ poisoning caused acute lung injury by inducing M2 macrophage polarization through inhibiting TLR2/MyD88 signaling pathway. The finding in this study provides an alternative approach for the treatment of PQ poisoning and other inflammatory diseases.

Tetracycline has received a great deal of interest for the harmful effects of substance abuse on ecosystems and humanity. The effects of different processes on the degradation of tetracycline were compared, with dual-frequency ultrasound (DFUS) in combination with peroxymonosulfate (PMS) being the most effective for the tetracycline degradation. Free radical scavenging experiments showed that O2∙-, SO4∙- and •OH were the main reactive radicals in the degradation of tetracycline. According to the major intermediates of tetracycline degradation identified, three possible degradation pathways were proposed, which are of significance for translational studies of tetracycline degradation. Notably, these intermediates were found to be significantly less toxicity. The number of active bubbles in the degradation vessel was calculated using a semi-empirical formula, and a higher value of 1.44 × 108 L-1s−1 of bubbles was obtained when using dual-frequency ultrasound at 20 kHz (210 W/L) and 80 kHz (85.4 W/L). Therefore, compared to 20 kHz, although the yield of strong oxidizing substances from individual active bubbles decreased slightly, a significant increment of the number of active bubbles still resulted in a higher synergistic effect, and the combination of DFUS and PMS should be effective in promoting the generation of reactive free radicals and mass transfer processes within the degradation vessel, which provides a method for efficient removal of tetracycline from wastewater.

Abstract Background and objective Dyslipidemia is significantly more common in those with concurrent chronic kidney disease (CKD) and chronic heart failure (CHF). Sacubitril/valsartan has showcased its influence on both cardiac and renal functions, extending its influence to the modulation of lipid metabolism pathways. This study aimed to examine how sacubitril/valsartan affects lipid metabolism within the context of CKD and CHF. Methods This study adopted a retrospective design, focusing on a single center and involving participants who were subjected to treatment with sacubitril/valsartan and valsartan. The investigation assessed the treatment duration, with a particular emphasis on recording blood lipid indicators, including triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A (ApoA), and apolipoprotein B (ApoB). Furthermore, cardiac and renal functions, blood pressure, potassium levels, and other factors influencing the blood lipids were analyzed in both groups at identical time points. Results After 16 weeks of observation, the sacubitril/valsartan group exhibited lower TG levels compared to the valsartan group. Noteworthy was the fact that individuals undergoing sacubitril/valsartan treatment experienced an average reduction of 0.84 mmol/L in TG levels, in stark contrast to the valsartan group, which registered a decline of 0.27 mmol/L (P

Abstract Background Current research on autophagy is mainly focused on intervertebral disc tissues and cells, while there is few on human peripheral blood sample. therefore, this study constructed a diagnostic model to identify autophagy-related markers of intervertebral disc degeneration (IVDD). Methods GSE150408 and GSE124272 datasets were acquired from the Gene Expression Omnibus database, and differential expression analysis was performed. The IVDD-autophagy genes were obtained using Weighted Gene Coexpression Network Analysis, and a diagnostic model was constructed and validated, followed by Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Meanwhile, miRNA–gene and transcription factor–gene interaction networks were constructed. In addition, drug-gene interactions and target genes of methylprednisolone and glucosamine were analyzed. Results A total of 1,776 differentially expressed genes were identified between IVDD and control samples, and the composition of the four immune cell types was significantly different between the IVDD and control samples. The Meturquoise and Mebrown modules were significantly related to immune cells, with significant differences between the control and IVDD samples. A diagnostic model was constructed using five key IVDD-autophagy genes. The area under the curve values of the model in the training and validation datasets were 0.907 and 0.984, respectively. The enrichment scores of the two pathways were significantly different between the IVDD and healthy groups. Eight pathways in the IVDD and healthy groups had significant differences. A total of 16 miRNAs and 3 transcription factors were predicted to be of great value. In total, 84 significantly related drugs were screened for five key IVDD-autophagy genes in the diagnostic model, and three common autophagy-related target genes of methylprednisolone and glucosamine were predicted. Conclusion This study constructs a reliable autophagy-related diagnostic model that is strongly related to the immune microenvironment of IVD. Autophagy-related genes, including PHF23, RAB24, STAT3, TOMM5, and DNAJB9, may participate in IVDD pathogenesis. In addition, methylprednisolone and glucosamine may exert therapeutic effects on IVDD by targeting CTSD, VEGFA, and BAX genes through apoptosis, as well as the sphingolipid and AGE-RAGE signaling pathways in diabetic complications.

Abstract Background Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance. Results In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction. Conclusions Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts.

Amidst the rapid expansion of the global economy, the demand for energy has escalated. The depletion of traditional energy sources coupled with environmental pollution concerns has catalyzed a shift towards the development and utilization of clean, renewable energy. Biogas, as a renewable energy source, provides diverse applications and holds the potential to alleviate energy shortages. Recently, biogas dry reforming technology has garnered substantial attention as a significant pathway for renewable energy utilization, particularly in the development and optimization of catalysts. Contemporary research predominantly focuses on enhancing the activity and stability of catalysts, with particular emphasis on their resistance to coking and sintering. This review delineates the classification of biogas dry reforming catalysts, their catalytic activity, and issues related to carbon deposition, contrasting biogas dry reforming with traditional dry reforming in catalyst design. It synthesizes numerous studies from recent years aimed at mitigating carbon deposition during the biogas dry reforming process and boosting catalytic activity via active components, carriers, and promoters in both precious and non-precious metal catalysts. Furthermore, it discusses the current challenges of biogas dry reforming technology and outlines prospective future development trends. This discussion provides an in-depth understanding of biogas dry reforming technology and catalyst design, offering insights and recommendations for future research and industrial applications.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN’s use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyperparameters—the learning rate and batch size of the online learning phase—highlights their roles in optimizing the learning process and enhancing the framework’s overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

Laser-inscribed graphene (LIG) is an emerging material for micro-electronic applications and is being used to develop supercapacitors, soft actuators, triboelectric generators, and sensors. The fabrication technique is simple, yet the batch-to-batch variation of LIG quality is not well documented in the literature. In this study, we conduct experiments to characterize batch-to-batch variation in the manufacturing of LIG electrodes for applications in electrochemical sensing. Numerous batches of 36 LIG electrodes were synthesized using a CO2 laser system on polyimide film. The LIG material was characterized using goniometry, stereomicroscopy, open circuit potentiometry, and cyclic voltammetry. Hydrophobicity and electrochemical screening (cyclic voltammetry) indicate that LIG electrode batch-to-batch variation is less than 5% when using a commercial reference and counter electrode. Metallization of LIG led to a significant increase in peak current and specific capacitance (area between anodic/cathodic curve). However, batch-to-batch variation increased to approximately 30%. Two different platinum electrodeposition techniques were studied, including galvanostatic and frequency-modulated electrodeposition. The study shows that formation of metallized LIG electrodes with high specific capacitance and peak current may come at the expense of high batch variability. This design tradeoff has not been discussed in the literature and is an important consideration if scaling sensor designs for mass use is desired. This study provides important insight into the variation of LIG material properties for scalable development of LIG sensors. Additional studies are needed to understand the underlying mechanism(s) of this variability so that strategies to improve the repeatability may be developed for improving quality control. The dataset from this study is available via an open access repository.

Objectives Type 2 diabetes mellitus (T2DM) commonly complicates kidney stone disease (KSD). Our objective is to investigate the variations in the urinary microbiota between individuals with KSD alone and those with KSD plus T2DM. This exploration could have implications for disease diagnosis and treatment strategies. Methods During lithotripsy, a ureterscope was employed, and 1 mL of urine was collected from the renal pelvis after bladder disinfection. Sequencing targeting the V3–V4 hypervariable region was performed using the 16S rRNA and Illumina Novaseq platform. Results The Shannon index showed a significant decrease in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.041). Principal Coordinate Analysis (PCoA) demonstrated a distinct bacterial community in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.027). The abundance of Sphingomonas, Corynebacterium, and Lactobacillus was significantly higher in the KSD plus T2DM group than in the KSD-only group (false discovery rate < 0.05). Furthermore, Enhydrobacter, Chryseobacterium, and Allobaculum were positively correlated with fasting blood glucose and HbA1c values (P < 0.05). Conclusions The urinary microbiota in the renal pelvis exhibits differences between patients with KSD plus T2DM and those with KSD alone. Further studies employing animal models are necessary to validate these distinctions, potentially paving the way for therapeutic developments based on the urinary microbiota.

Purpose: Treatment of triple-negative breast cancer (TNBC) remains challenging. Intermittent fasting (IF) has emerged as a promising approach to improve metabolic health of various metabolic disorders. Clinical studies indicate IF is essential for TNBC progression. However, the molecular mechanisms underlying metabolic remodeling in regulating IF and TNBC progression are still unclear. Methods: In this study, we utilized a robust mouse model of TNBC and exposed subjects to a high-fat diet (HFD) with IF to explore its impact on the metabolic reprogramming linked to cancer progression. To identify crucial serum metabolites and signaling events, we utilized targeted metabolomics and RNA sequencing (RNA-seq). Furthermore, we conducted immunoblotting, real-time quantitative polymerase chain reaction (RT-qPCR), cell migration assays, lentivirus-mediated Mmp9 overexpression, and Mmp9 inhibitor experiments to elucidate the role of decanoylcarnitine/Mmp9 in TNBC cell migration. Results: Our observations indicate that IF exerts notable inhibitory effects on both the proliferation and cancer metastasis. Utilizing targeted metabolomics and RNA-seq, we initially identified pivotal serum metabolites and signaling events in the progression of TNBC. Among the 349 serum metabolites identified, decanoylcarnitine was picked out to inhibit TNBC cell proliferation and migration. RNA-seq analysis of TNBC cells treated with decanoylcarnitine revealed its suppressive effects on extracellular matrix-related protein components, with a notable reduction observed in Mmp9. Further investigations confirmed that decanoylcarnitine could inhibit Mmp9 expression in TNBC cells, primary tumors, lung, and liver metastasis tissues. Mmp9 overexpression abolished the inhibitory effect of decanoylcarnitine on cell migration. Conclusion: This study pioneers the exploration of IF intervention and the role of decanoylcarnitine/Mmp9 in the progression of TNBC in obese mice, enhancing our comprehension of the potential roles of various dietary patterns in the process of cancer treatment.

Abstract Background Dairy cows are susceptible to postpartum systemic oxidative stress (OS), which leads to significant production loss and metabolic disorders. The gut microbiota has been linked to host health and stress levels. However, to what extent the gut microbiota is associated with postpartum OS remains unknown. In this study, the contribution of the fecal microbiota to postpartum systemic OS and its underlying mechanisms were investigated by integrating 16S rRNA gene sequencing, metagenomics, and metabolomics in postpartum dairy cattle and by transplanting fecal microbiota from cattle to mice. Results A strong link was found between fecal microbial composition and postpartum OS, with an explainability of 43.1%. A total of 17 significantly differential bacterial genera and 19 species were identified between cows with high (HOS) and low OS (LOS). Among them, 9 genera and 16 species showed significant negative correlations with OS, and Marasmitruncus and Ruminococcus_sp._CAG:724 had the strongest correlations. The microbial functional analysis showed that the fecal microbial metabolism of glutamine, glutamate, glycine, and cysteine involved in glutathione synthesis was lower in HOS cows. Moreover, 58 significantly different metabolites were identified between HOS and LOS cows, and of these metabolites, 19 were produced from microbiota or cometabolism of microbiota and host. Furthermore, these microbial metabolites were enriched in the metabolism of glutamine, glutamate, glycine, and cysteine. The mice gavaged with HOS fecal microbiota had significantly higher OS and lower plasma glutathione peroxidase and glutathione content than those orally administered saline or LOS fecal microbiota. Conclusions Integrated results suggest that the fecal microbiota is responsible for OS and that lower glutathione production plays a causative role in HOS. These findings provide novel insights into the mechanisms of postpartum OS and potential regulatory strategies to alleviate OS in dairy cows. Video Abstract

Abstract Disk-like domain receptor 1 (DDR1) is a crucial regulator of pro-inflammatory mediators and matrix-degrading enzymes. Although mounting evidence supports a vital role for DDR1 in the tumorigenesis of some cancers, no pan-cancer analysis of DDR1 has been reported. Therefore, we aimed to explore the prognostic value of DDR1 in 33 cancer types and investigate its potential immune function. We used a range of bioinformatics approaches to explore the potential carcinogenic role of DDR1 in multiple cancers. We found that DDR1 was expressed at high levels in most cancers. DDR1 expression was positively or negatively associated with prognosis in different cancers. DDR1 expression was significantly associated with DNA methylation in 8 cancers, while there was a correlation between DDR1 expression and RNA methylation-related genes and mismatch repair gene in most cancers. Furthermore, DDR1 expression was significantly associated with microsatellite instability in 6 cancers and tumor mutation burden in 11 cancers. In addition, DDR1 expression was also significantly correlated with immune cell infiltration, tumor microenvironment, immune-related genes, and drug resistance in various cancers. In conclusion, DDR1 can serve as a potential therapeutic target and prognostic marker for various malignancies due to its vital role in tumorigenesis and tumor immunity.

Abstract In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators.

Abstract Background Postpartum dairy cows experiencing excessive lipolysis are prone to severe immunosuppression. Despite the extensive understanding of the gut microbial regulation of host immunity and metabolism, its role during excessive lipolysis in cows is largely unknown. Herein, we investigated the potential links between the gut microbiome and postpartum immunosuppression in periparturient dairy cows with excessive lipolysis using single immune cell transcriptome, 16S amplicon sequencing, metagenomics, and targeted metabolomics. Results The use of single-cell RNA sequencing identified 26 clusters that were annotated to 10 different immune cell types. Enrichment of functions of these clusters revealed a downregulation of functions in immune cells isolated from a cow with excessive lipolysis compared to a cow with low/normal lipolysis. The results of metagenomic sequencing and targeted metabolome analysis together revealed that secondary bile acid (SBA) biosynthesis was significantly activated in the cows with excessive lipolysis. Moreover, the relative abundance of gut Bacteroides sp. OF04 − 15BH, Paraprevotella clara, Paraprevotella xylaniphila, and Treponema sp. JC4 was mainly associated with SBA synthesis. The use of an integrated analysis showed that the reduction of plasma glycolithocholic acid and taurolithocholic acid could contribute to the immunosuppression of monocytes (CD14+MON) during excessive lipolysis by decreasing the expression of GPBAR1. Conclusions Our results suggest that alterations in the gut microbiota and their functions related to SBA synthesis suppressed the functions of monocytes during excessive lipolysis in transition dairy cows. Therefore, we concluded that altered microbial SBA synthesis during excessive lipolysis could lead to postpartum immunosuppression in transition cows. Video Abstract

Abstract Purpose To analyze risk factors of titanium mesh cage (TMC) subsidence in single-level anterior cervical corpectomy and fusion (ACCF) for cervical ossification of the posterior longitudinal ligament (OPLL). Methods TMC subsidence is defined as the reduction of the adjacent vertebral bodies by ≥ 2 mm. Patients with cervical OPLL who were treated with single-level ACCF between January 2019 and May 2021 were retrospectively analyzed in two groups: patients with TMC subsidence as Group S and patients with no TMC subsidence as Group N during the one-year follow-up period. The degree of distraction of surgical segment and correction of the cervical curvature was measured to analyze their relationship with TMC subsidence. Results A total of 128 patients were included in Group S, and 138 patients were included in Group N. There was no significant difference in patient demographics and complications between the two groups. The degree of distraction in Group S was significantly higher than that in Group N (11.4% ± 7.6% vs. 4.7% ± 9.7%, P

Abstract Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients.

Abstract Background Ossification of the posterior longitudinal ligament (OPLL) is a disabling disease whose pathogenesis is still unclear, and there are no effective cures or prevention methods. Exosomal miRNA plays an important role in the osteogenesis of ectopic bone. Therefore, we focused on the downregulation of miR-140-5p in OPLL cell-derived exosomes to explore the mechanism by which exosomal miR-140-5p inhibits osteogenesis in OPLL. Results Exosomes were isolated by differential centrifugation and identified by transmission electron microscopy, nanoparticle tracking analysis, and exosomal markers. Exosomal RNA was extracted to perform miRNA sequencing and disclose the differentially expressed miRNAs, among which miR-140-5p was significantly downregulated. Confocal microscopy was used to trace the exosomal miR-140-5p delivered from OPLL cells to human mesenchymal stem cells (hMSCs). In vitro, we verified that exosomal miR-140-5p inhibited the osteoblast differentiation of hMSCs by targeting IGF1R and suppressing the phosphorylation of the IRS1/PI3K/Akt/mTOR pathway. In vivo, we verified that exosomal miR-140-5p inhibited ectopic bone formation in mice as assessed by micro-CT and immunohistochemistry. Conclusions We found that exosomal miR-140-5p could inhibit the osteogenic differentiation of hMSCs by targeting IGF1R and regulating the mTOR pathway, prompting a further potential means of drug treatment and a possible target for molecular therapy of OPLL.

Abstract Background The association between oral dysbiosis and chronic kidney disease (CKD) has gained increasing attention in recent years. Diabetes and hypertension are the most common conditions in CKD. However, a case–control study with matched confounding variables on the salivary microbiome in CKD and the influence of diabetes and hypertension on the microbiome has never been reported. Methods In our study, we compared the salivary microbiome profile between patients with CKD and healthy controls (HC) using 16S ribosomal DNA sequencing and examine its association with diabetes, hypertension, and immunity. Results We observed that the bacterial community was skewed in the saliva of CKD, with increased Lautropia and Pseudomonas, and decreased Actinomyces, Prevotella, Prevotella 7, and Trichococcus. No difference in the bacterial community between the CKD patients complicated with and without diabetes, and between those with and without hypertension. Prevotella 7 declined in CKD patients with/without hypertension with respect to HC, while Pseudomonas increased in CKD patients with/without hypertension. Pseudomonas was negatively associated with immunoglobin G in CKD patients. Both CKD patients with positive and negative antistreptolysin O had declined Prevotella 7 and Trichococcus compared to HC, whereas increased Pseudomonas. Conclusions Our study identifies a distinct bacterial saliva microbiome in CKD patients characterized by alteration in composition. We unravel here that the co-occurrence diseases of diabetes and hypertension are not associated with specific bacterial alterations, suggesting that bacterial dysbiosis in saliva plays a role in renal damage regardless of the occurrence of diabetes and hypertension.

BackgroundTo establish antibiotic preregimes and administration routes for studies on urinary microbiota.Methods and materialsAntibiotics for enteritis (Abx-enteritis) and UTIs (Abx-UTI) were administered via gavage and/or urinary catheterisation (UC) for 1 and/or 2 weeks. The effects of these Abx on the urinary microbiota of rats were examined via 16S rRNA sequencing and urine culture, including anaerobic and aerobic culture. Additionally, the safety of the Abx was examined.ResultsAbx-enteritis/Abx-UTI (0.5 g/L and 1 g/L) administered via gavage did not alter the microbial community and bacterial diversity in the urine of rats (FDR > 0.05); however, Abx-UTI (1 g/L) administered via UC for 1 and 2 weeks altered the urinary microbial community (FDR < 0.05). Rats administered Abx-UTI (1 g/L) via UC for 1 week demonstrated a distinct urinary microbiota in culture. Abx-enteritis/Abx-UTI administered via gavage disrupted the microbial community and reduced bacterial diversity in the faeces of rats (FDR < 0.05), and Abx-UTI administered via UC for 2 weeks (FDR < 0.05) altered the fecal microbiota. Abx-UTI (1 g/L) administered via UC did not alter safety considerations. In addition, we noticed that UC did not induce infections and injuries to the bladder and kidney tissues.ConclusionsAdministration of Abx-UTI via UC for 1 week can be considered a pre-treatment option while investigating the urinary microbiota.

Increasing the efficiency and sustainability of cattle production is an effective way to produce valuable animal proteins for a growing human population. Genetics and nutrition are the 2 major research topics in selecting cattle with beneficial phenotypes and developing genetic potentials for improved performance. There is an inextricable link between genetics and nutrition, which urgently requires researchers to uncover the underlying molecular mechanisms to optimize cattle production. Feedomics integrates a range of omic techniques to reveal the mechanisms at different molecular levels related to animal production and health, which can provide novel insights into the relationships of genes and nutrition/nutrients. In this review, we summarized the applications of feedomics techniques to reveal the effect of genetic elements on the response to nutrition and investigate how nutrients affect the functional genome of cattle from the perspective of both nutrigenetics and nutrigenomics. We highlighted the roles of rumen microbiome in the interactions between host genes and nutrition. Herein, we discuss the importance of feedomics in cattle nutrition research, with a view to ensure that cattle exhibit the best production traits for human consumption from both genetic and nutritional aspects.

With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performance of a cross-database, the proposed framework improves a meta-learning network in two ways: it enhances the model’s feature extraction ability for detecting targets by combining the RGB domain and frequency domain information of the image and enhances the model’s generalization ability for detecting targets by assigning meta weights to channels on the feature map. The proposed MCW framework solves the problems of poor detection performance and insufficient data compression resistance of the algorithm for samples generated by unknown algorithms. The experiment was set in a zero-shot scenario and few-shot scenario, simulating the deepfake detection environment in real situations. We selected nine detection algorithms as comparative algorithms. The experimental results show that the MCW framework outperforms other algorithms in cross-algorithm detection and cross-dataset detection. The MCW framework demonstrates its ability to generalize and resist compression with low-quality training images and across different generation algorithm scenarios, and it has better fine-tuning potential in few-shot learning scenarios.

Background Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors. Therefore, it is important to accurately select a subset of feature genes with high contributions to cancer classification. Methods In this article, a new three-stage hybrid gene selection method is proposed that combines a variance filter, extremely randomized tree and Harris Hawks (VEH). In the first stage, we evaluated each gene in the dataset through the variance filter and selected the feature genes that meet the variance threshold. In the second stage, we use extremely randomized tree to further eliminate irrelevant genes. Finally, we used the Harris Hawks algorithm to select the gene subset from the previous two stages to obtain the optimal feature gene subset. Results We evaluated the proposed method using three different classifiers on eight published microarray gene expression datasets. The results showed a 100% classification accuracy for VEH in gastric cancer, acute lymphoblastic leukemia and ovarian cancer, and an average classification accuracy of 95.33% across a variety of other cancers. Compared with other advanced feature selection algorithms, VEH has obvious advantages when measured by many evaluation criteria.

RNA modification is a key regulatory mechanism involved in tumorigenesis, tumor progression, and the immune response. However, the potential role of RNA modification "writer" genes in the immune microenvironment of gliomas and their effect on the response to immunotherapy remains unclear. The purpose of this study was to evaluate the role of RNA modification "writer" gene in the prognosis and immunotherapy response of low-grade glioma (LGG). The consensus non-negative matrix factorization (CNMF) method was used to identify different RNA modification subtypes. We used a novel eigengene screening method, the variable neighborhood learning Harris Hawks optimizer (VNLHHO), to screen for eigengenes among the RNA modification subtypes. We constructed a principal components analysis score(PCA_score)-based prognostic prediction model and validated it using an independent cohort. We also analyzed the association between PCA_score and the immune and molecular features of LGG. The results suggested that LGG can be divided into two different RNA modification-based subtypes with distinct prognostic and molecular features. High PCA_score was significantly associated with a poor prognosis in LGG and was an independent prognostic factor. A nomogram containing PCA_score and clinical features was constructed, and it showed a significant predictive value. PCA_score was negatively correlated with tumor purity and the abundance of CD4+ T cells in LGG patients. LGG patients with high PCA_score had lower Tumor Immune Dysfunction and Exclusion scores and showed an immunotherapy response. In conclusion, we report a novel RNA modification-based prognostic model for LGG that lays the foundation for evaluating LGG prognosis and developing more effective therapeutic strategies for these tumors.

As a kind of urban neighborhood with strong internal heterogeneity, mixed-housing neighborhoods have attracted wide attention from scholars in recent years. Strengthening community ties in mixed-housing neighborhoods is of great significance for increasing neighborhood social capital, cultivating a sense of community, and promoting sustainable development of the neighborhood. The neighborhood activities of residents are an important factor in promoting community ties. However, different housing groups in mixed-housing neighborhoods may have differentiated or even segregated overall daily activities, which may impact their neighborhood activities and call for differentiated planning strategies. In this study, we conduct an empirical study in Fuzhou, China, to identify the spatiotemporal-behavior-based microsegregation and differentiated community ties between residents of different types of housing. The data were collected in 2021 and included residents’ activity diary data and questionnaire data about neighborhood interaction and community ties. Through an analysis of the daily overall activity space and activities within the neighborhood areas, the spatiotemporal-behavior-based social segregation of various housing groups is depicted. Furthermore, a multigroup structural equation modeling method was used to analyze the relationships among residents’ spatiotemporal behaviors, neighborhood interactions, and community ties, and the heterogeneous influence effects across housing groups. The results show that the more residents’ activity spaces overlap with the neighborhood area, the more out-of-home time they spend within the neighborhood, and that the more types of activities are conducted within the neighborhood area, the stronger their community ties are. In addition, neighborhood interaction played a linkage role in the relationships of residents’ spatiotemporal behaviors and community ties. Our research aims to further the understanding of microsegregation at the neighborhood level and provide references for the development of mixed-housing neighborhoods and urban land use.

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an extreme dependence structure. Due to the bond’s low cashflow contingencies and the CAT bond’s high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce the moral hazard and increase the bond’s attractiveness with a lower trigger likelihood, displaying the determinants of the wiped-off coupon and principal by both the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulting from the potential catastrophic disaster might be associated with heavy-tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by a POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct an empirical analysis of pricing a three-event rainstorm CAT bond based on the resulting losses due to rainstorms in China during 2006–2020. Monte Carlo simulations are carried out to analyze the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators.

Abstract Background Three-dimensional reduction plays a vital role in surgical reduction of irreversible atlantoaxial dislocation (IAAD). However, the most commonly used combination of C1 pedicle screw (PS) or lateral mass screw (LMS) and C2 PS or isthmus screw often fails to achieve satisfactory reduction at one time. The difficulty is usually caused by short anteroposterior and vertical distance between heads of C1 and C2 screws, which lack enough space for reduction operation. The objective of this study is to describe a three-dimensional reduction method with a modified C2 isthmus screw and to illustrate its advantage and effectiveness for IAAD. Methods Twelve patients with IAAD underwent reduction and fixation with modified C2 isthmus screw combined with C1 PS or LMS, fusion with autologous bone graft. The insertion point was lateral to the intersection of caudal edge of C2 lamina and lateral mass, with a trajectory towards C2 isthmus, via lateral mass. The three-dimensional reduction was achieved through pulling and distracting. Radiographic evaluation included anteroposterior and direct distance between different insertion points, the occipitoaxial angle (O-C2A), clivus-canal angle (CCA) and cervicomedullary angle (CMA). Clinical outcomes evaluation included the Japanese Orthopaedic Association (JOA) score, Visual analog scale (VAS) and Neck Disability Index (NDI). Results All the patients maintained effective reduction during the follow-up. The anteroposterior and direct distance was significantly higher in modified C2 isthmus screw than C2 PS whether combined with C1 PS or LMS (P

A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.

FTY720 and IMMH002, prodrugs for sphingosine-1-phosphate receptor 1 (S1P1) agonists, show inadequate and inconsistent levels of phosphorylation in humans compared to that in rats. In this study, FTY720 or IMMH002 analogues (21–24) were designed and synthesized with modified head pieces to improve the biotransformation of the prodrugs to the active phosphorylated forms. Target compounds were synthesized via a convergent route using the key and optically pure building block 9, which was first synthesized via asymmetrically catalyzed amination. The phosphorylation rates of these analogues in rat or human blood were compared. The new methyl-substituted analogue compound 21 showed higher phosphorylation rates in both rats and humans than the parent compound, whereas compound 23 showed improvements in rats, but not in humans. In pharmacokinetics studies of rats, compounds 21 and 23 both had higher levels of phosphorylation than FTY720 and IMMH002. Thus, our study not only yielded new compounds with therapeutic potential, but also showed species differences between rats and humans in response to the structural modifications, which might be useful for predicting the biotransformation behavior and efficacy of this class of prodrugs in the clinic.

Lane keeping control needs to achieve smooth steering operation while ensuring safety-no lane departures and maintain small lateral displacement. In this paper, we solve this challenge by developing a preview lane keeping control supervised by a safeguard controller. The preview control utilizes both tracking errors and future lane curvatures to generate the optimal steering commands. The safeguard controller is then designed to guarantee bounded tracking errors. It supervises the preview control and intervenes if and only if the tracking error is approaching the safety boundary. Both algorithms have analytical control laws and thus require little on-line computations. The integrated system is compared with a model predictive control (MPC) design in terms of both tracking performance and computing efficiency. We implemented the proposed controls on a self-driving vehicle platform and tested on open roads and in the Mcity test facility.

Compared with traditional halogen headlights, LED has obvious advantages in energy consumption, photoelectric efficiency, luminous flux, and life span. However, the thermal management of LEDs is usually limited by space. This paper proposes a novel design of a heat pipes assisted compact heat sink (HPACHS) for LEDs to reduce the LED junction temperature. The thickness of the double-sided LED can be reduced to 1.5 mm since there is no multilayer structure, and the double-sided LED headlight can emit more concentrated light. The results show that HPACHS can cool the 34 W LED substrate to 100.32 °C at an ambient temperature of 26 °C, with a cooling fan power of only 0.267 W and a thermal resistance from the LED base to the environment of 2.17 °C/W. The minimum thermal resistance of HPACHS is 1.86 °C/W, with a heat load of 18 W. The heat sink is 71 mm long and the maximum outer diameter is 38 mm. Therefore, as an efficient and high-power LED cooling device, this compact active radiator has sufficient application prospects and can be applied to the thermal management of automobile headlights.

The Solar collectors and cogeneration units have gained increasing attention in research recently, while being developed and deployed rapidly. However, due to the instability of the solar energy, the heat flux input of solar collectors is unstable potentially causing an adverse effect on the cogeneration unit. This paper presents the mathematical models for two cogeneration units: (a) the thermal series U-shaped segmented unidirectional heat pipe array cogeneration unit with thermoelectric generator (TEG-TSUSUHP) and (b) the thermal parallel U-shaped segmented unidirectional heat pipe array cogeneration unit with thermoelectric generator (TEG-TPUSUHP). The proposed models have been validated by a series of experiments. Further, the cogeneration unit is then optimized using the proposed models. The results show that the heat loss rate of the TEG-TSUSUHP cogeneration unit is lower than that of the TEG-TPUSUHP cogeneration unit, and the lowest heat loss rate is 15.56%. Moreover, the cogeneration unit designed in this work has good thermal insulation performance with a high thermal storage efficiency of up to 80.53%. Therefore, the U-shape segmented unidirectional heat pipe array cogeneration units with thermoelectric generator (TEG-USUHP) proposed in this paper can promote the development of solar cogeneration units significantly.

Biofortification is a new agricultural intervention to alleviate hidden hunger in China and even the world. Exploring the impact of farmers′ adoption of biofortification on return on investment (ROI), which is calculated from farmers′ production net income and profitability, can provide empirical support for the development of sustainable agriculture. This paper examines the impact of the adoption of biofortification on ROI using cross-sectional data from a survey of farmers in China. An endogenous switching regression model that accounts for selection bias was used in the research. The empirical results revealed that the adoption of biofortification has a positive and statistically significant impact on ROI. A heterogeneity analysis also revealed that comparatively high annual income adopters and female adopters tend to benefit more from the adoption of biofortification than middle- and low-income adopters and male adopters.