Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations.

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19. IMPORTANCE: A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM.

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

Preparation of Anti-Zearalenone IgY and Development of an Indirect Competitive ELISA Method for the Measurement of Zearalenone in Post-Fermented Tea.

Post-fermented tea (PFT) is one of the most commonly consumed beverages worldwide. Rapid microbial growth and significant changes in the microbial composition of PFT during processing and storage pose a potential risk of contamination with mycotoxins such as zearalenone (ZEN). Screening for ZEN contamination in a simple, rapid, and inexpensive manner is required to ensure that PFT is safe for consumption. To monitor ZEN in PFT, ZEN was conjugated with bovine serum albumin to prepare egg yolk immunoglobulins (IgY). A specific indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) based on IgY was developed and validated. ZEN was extracted with acetonitrile and water (50:50, v/v) containing 5% acetic acid and purified using a mixture of primary and secondary amines and graphitized carbon black to remove matrix interference from the PFT samples. Under optimal conditions, the linear range of this assay was 13.8-508.9 ng mL-1, the limit of detection was 9.3 ng mL-1, and the half-maximal inhibitory concentration was 83.8 ng mL-1. Cross-reactivity was negligible, and the assay was specific for ZEN-related molecules. The recovery rate of ZEN in the control blanks of PFT samples spiked with a defined concentration of ZEN of 89.5% to 98.0%. The recovery and accuracy of the method were qualified for PFT matrices. No significant differences were evident between the results of the actual PFT samples analyzed by high-performance liquid chromatography and ic-ELISA. The collective data indicate that the developed ic-ELISA can be used for the rapid and simple detection of ZEN in PFT products.

Multisite Mutation of the Escherichia coli cAMP Receptor Protein: Enhancing Xylitol Biosynthesis by Activating Xylose Catabolism and Improving Strain Tolerance.

The bioproduction of xylitol from hemicellulose hydrolysate has good potential for industrial development. However, xylitol productivity has always been limited due to corncob hydrolysate toxicity and glucose catabolic repression. To address these challenges, this work selected the S83 and S128 amino acid residues of the cyclic AMP receptor protein (CRP) as the modification target. By introducing multisite mutation in CRP, this approach successfully enhanced xylose catabolism and improved the strain's tolerance to corncob hydrolysate. The resulting mutant strain, designated as CPH (CRP S83H-S128P), underwent fermentation in a 20 L bioreactor with semicontinuous feeding of corncob hydrolysate. Remarkably, xylitol yield and xylitol productivity for 41 h fermentation were 175 and 4.32 g/L/h, respectively. Therefore, multisite CRP mutation was demonstrated as an efficient global regulatory strategy to effectively improve xylitol productivity from lime-pretreated corncob hydrolysates.

Dynamic ultrasound molecular-targeted imaging of senescence in evaluation of lapatinib resistance in HER2-positive breast cancer.

BACKGROUND: Prolonged treatment of HER2+ breast cancer with lapatinib (LAP) causes cellular senescence and acquired drug resistance, which often associating with poor prognosis for patients. We aim to explore the correlation between cellular senescence and LAP resistance in HER2+ breast cancer, screen for molecular marker of reversible senescence, and construct targeted nanobubbles for ultrasound molecular imaging to dynamically evaluate LAP resistance. METHODS AND RESULTS: In this study, we established a new cellular model of reversible cellular senescence using LAP and HER2+ breast cancer cells and found that reversible senescence contributed to LAP resistance in HER2+ breast cancer. Then, we identified ecto-5'-nucleotidase (NT5E) as a marker of reversible senescence in HER2+ breast cancer. Based on this, we constructed NT5E-targeted nanobubbles (NT5E-FITC-NBs) as a new molecular imaging modality which could both target reversible senescent cells and be used for ultrasound imaging. NT5E-FITC-NBs showed excellent physical and imaging characteristics. As an ultrasound contrast agent, NT5E-FITC-NBs could accurately identify reversible senescent cells both in vitro and in vivo. CONCLUSIONS: Our data demonstrate that cellular senescence-based ultrasound-targeted imaging can identify reversible senescence and evaluate LAP resistance effectively in HER2+ breast cancer cells, which has the potential to improve cancer treatment outcomes by altering therapeutic strategies ahead of aggressive recurrences.

B-Type Fumonisins in Post-Fermented Tea: Occurrence and Consumer Dietary Exposure in Guangxi, China.

Post-fermented tea (PFT), a commonly consumed beverage worldwide, is characterized by the rapid growth of its microbial groups and the substantial changes they undergo. Consequently, PFT may contain mycotoxins such as B-type fumonisins (FBs). This study aimed to assess the intake of FBs through the consumption of PFT among consumers in Guangxi, China. A novel quantitative method using high-performance liquid chromatography-mass spectrometry was used to determine the FB concentration in PFT products. Additionally, a PFT consumption survey was conducted using a face-to-face questionnaire, recording their body weight and PFT consumption patterns based on a three-day dietary recall method. Finally, hazard index was calculated to estimate the health risk of FBs from the consumption of PFT products in Guangxi. The results revealed that the occurrence of FBs in PFT was 20% (24/120), with a concentration ranging from 2.14 to 18.28 µg/kg. The results of the survey showed that the average daily consumption of PFT by consumers was 9.19 ± 11.14 g. The deterministic risk assessment revealed that only 0.026% of the provisional maximum tolerable daily intake of FBs was consumed through PFT, indicating that FB contamination in PFT is not a public health risk.

Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.

Background: Thyroid cancer is the most common malignancy in the endocrine system, with its early manifestation being the presence of thyroid nodules. With the advantages of convenience, noninvasiveness, and a lack of radiation, ultrasound is currently the first-line screening tool for the clinical diagnosis of thyroid nodules. The use of artificial intelligence to assist diagnosis is an emerging technology. This paper proposes the use optical neural networks for potential application in the auxiliary diagnosis of thyroid nodules. Methods: Ultrasound images obtained from January 2013 to December 2018 at the Institute and Hospital of Oncology, Tianjin Medical University, were included in a dataset. Patients who consecutively underwent thyroid ultrasound diagnosis and follow-up procedures were included. We developed an all-optical diffraction neural network to assist in the diagnosis of thyroid nodules. The network is composed of 5 diffraction layers and 1 detection plane. The input image is placed 10 mm away from the first diffraction layer. The input of the diffractive neural network is light at a wavelength of 632.8 nm, and the output of this network is determined by the amplitude and light intensity obtained from the detection region. Results: The all-optical neural network was used to assist in the diagnosis of thyroid nodules. In the classification task of benign and malignant thyroid nodules, the accuracy of classification on the test set was 97.79%, with an area under the curve value of 99.8%. In the task of detecting thyroid nodules, we first trained the model to determine whether any nodules were present and achieved an accuracy of 84.92% on the test set. Conclusions: Our study demonstrates the potential of all-optical neural networks in the field of medical image processing. The performance of the models based on optical neural networks is comparable to other widely used network models in the field of image classification.

Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study.

BACKGROUND: Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC. METHODS: In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness. RESULTS: The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin­US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870). CONCLUSION: Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.

Identification and validation of an epithelial-mesenchymal transition-related lncRNA pairs prognostic model for gastric cancer.

Background: Gastric cancer (GC) is a common malignancy. A mounting body of evidence has demonstrated the correlation between GC prognosis and epithelial-mesenchymal transition (EMT)-related biomarkers. This research constructed an available model using EMT-related long noncoding RNA (lncRNA) pairs to predict the survival for GC patients. Methods: The transcriptome data along with clinical information on GC samples were derived from The Cancer Genome Atlas (TCGA). Differentially expressed EMT-related lncRNAs were acquired and paired. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied to filter lncRNA pairs, and the risk model was built to investigate its effect on the prognosis of GC patients. Then, the areas under the receiver operating characteristic curves (AUCs) were calculated and the cutoff point for distinguishing low- or high-risk GC patients was identified. And the predictive ability of this model was tested in the GSE62254. Furthermore, the model was evaluated from the perspectives of survival time, clinicopathological parameters, infiltration of immunocytes, and functional enrichment analysis. Results: The risk model was built by using the identified twenty EMT-related lncRNA pairs, and it was not necessary to know the specific expression level of each lncRNA. Survival analysis pointed out that GC patients with high risk had poorer outcomes. Additionally, this model could be an independent prognostic variable for GC patients. The accuracy of the model was also verified in the testing set. Conclusions: The new predictive model constructed here is composed of EMT-related lncRNA pairs, with reliable prognostic values, and can be utilized to predict the survival of GC.

The role of BHLHE40 in clinical features and prognosis value of PDAC by comprehensive analysis and in vitro validation.

Pancreatic ductal adenocarcinoma (PDAC) is the leading cause of cancer-related mortality, primarily due to the abundance of cancer-associated fibroblasts (CAFs), depleted effector T cells, and increased tumor cell stemness; hence, there is an urgent need for efficient biomarkers with prognostic and therapeutic potential. Here, we identified BHLHE40 as a promising target for PDAC through comprehensive analysis and weighted gene coexpression network analysis of RNA sequencing data and public databases, taking into account the unique characteristics of PDAC such as cancer-associated fibroblasts, infiltration of effector T cells, and tumor cell stemness. Additionally, we developed a prognostic risk model based on BHLHE40 and three other candidate genes (ITGA2, ITGA3, and ADAM9) to predict outcomes in PDAC patients. Furthermore, we found that the overexpression of BHLHE40 was significantly associated with T stage, lymph node metastasis, and American Joint Committee on Cancer (AJCC) stage in a cohort of 61 PDAC patients. Moreover, elevated expression levels of BHLHE40 were validated to promote epithelial-mesenchymal transition (EMT) and stemness-related proteins in BXPC3 cell lines. Compared to the parent cells, BXPC3 cells with BHLHE40 overexpression showed resistance to anti-tumor immunity when co-cultured with CD8+ T cells. In summary, these findings suggest that BHLHE40 is a highly effective biomarker for predicting prognosis in PDAC and holds great promise as a target for cancer therapy.

Corporate financialization, financing constraints, and innovation efficiency-Empirical evidence based on listed Chinese pharmaceutical companies.

The relationship between financialization and innovation has become a common focus of academic attention. This paper analyzes the influence of corporate financialization on innovation efficiency based on balanced panel data of listed Chinese pharmaceutical companies from 2015 to 2020. Also, it examines the relationship between corporate financialization and innovation efficiency under different levels of financing constraints and the moderating mechanisms that exist. The results of the study show that corporate financialization negatively affects innovation efficiency and that this effect has a lag; corporate financialization hurts innovation efficiency across the different regions and firm nature, with a less inhibiting effect for eastern firms and non-state-owned firms; further tests of the mechanism of action show that there is a non-linear negative relationship between corporate financialization and innovation efficiency. And the inhibition of corporate financialization on innovation efficiency decreases as the level of financing constraints rises. Based on the above findings, this study provides warnings and recommendations for pharmaceutical companies to finance their innovative activities through financialization.

The effect of mobile phone task and age on gait: A systematic review and meta-analysis.

Objective: Mobile phone usage while performing postural-locomotor tasks is everyday activity across persons of all ages in various environmental contexts and health conditions. However, it is also an important factor contributing to accidents. To lower the risk of pedestrian accidents, this meta-analysis aimed to examine how mobile phones affect pedestrian gait and identify how mobile phone tasks and participant age affect gait differently. Methods: Electronic database searches were performed in The Cochrane Library, PubMed, and Medline. Two examiners evaluated the eligibility and quality of included studies using the Downs and Black checklist. The mean differences (MD) or standardized mean differences (SMD) were calculated for each outcome. Subgroup analyses were used to compare the differential effects of mobile phone task and participant age on gait. Results: Among 22 eligible studies, 592 participants in 10 countries were analyzed in this meta-analysis. The overall meta-analysis showed that using a mobile phone significantly decreased pedestrian gait velocity (SMD = -1.45; 95% CI: -1.66 to -1.24; p < 0.00001; I2 = 66%), step length (SMD = -1.01; 95% CI: -1.43 to -0.59; p < 0.00001; I2 = 82%), and stride length (SMD = -0.9; 95% CI: -1.19 to -0.60; p < 0.00001; I2 = 79%), significantly increased pedestrian step time (SMD = 0.77; 95% CI: 0.45 to 1.08; p < 0.00001; I2 = 78%), stride time (SMD = 0.87; 95% CI: 0.69 to 1.06; p < 0.00001; I2 = 24%), step width (SMD = 0.79; 95% CI: 0.34 to 1.24; p = 0.0006. I2 = 75%), double support time (SMD = 1.09; 95% CI: 0.86 to 1.31; p < 0.00001; I2 = 42%), and double support (%gait cycle, %GC) (MD = 2.32; 95% CI: 1.75 to 2.88; p < 0.00001; I2 = 26%). Conclusion: In summary, the effects of mobile phone tasks and participant age on gait were inconsistent. Our study found that resource-intensive tasks (texting and reading) significantly reduced gait velocity, and step time; however, small resource-intensive tasks (calling, talking, and dialing) did not affect these outcomes. In contrast to young adults, step length and step time were not affected by mobile phone use in older adults. Tips: Pedestrians should consider using a mobile phone in their daily lives according to the application scenarios (walking environment, the complexity of mobile phone tasks, pedestrians' task processing abilities, etc.) as appropriate to avoid dangerous accidents. Systematic Review Registration: identifier CRD42022358963.

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study.

Objective: Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM. Materials and methods: In this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients. Results: Multivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability. Conclusion: Our proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.

ATTransUNet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation.

Recently, researchers have introduced Transformer into medical image segmentation networks to encode long-range dependency, which makes up for the deficiencies of convolutional neural networks (CNNs) in global context modeling, and thus improves segmentation performance. However, in Transformer, due to the heavy computational burden of paired attention modeling between redundant visual tokens, the efficiency of Transformer needs to be further improved. Therefore, in this paper, we propose ATTransUNet, a Transformer enhanced hybrid architecture based on the adaptive token for ultrasound and histopathology image segmentation. In the encoding stage of the ATTransUNet, we introduced an Adaptive Token Extraction Module (ATEM), which can mine a few important visual tokens in the image for self-attention modeling, thus reducing the complexity of the model and improving the segmentation accuracy. In addition, in the decoding stage, we introduce a Selective Feature Reinforcement Module (SFRM) to reinforce the representation of and attention to key tissues or pathological features. The proposed ATTransUNet is evaluated on the basis of three medical image segmentation datasets. The results show that ATTransUNet achieves the best segmentation performance compared with the previous state-of-the-art models, and the proposed method is also competitive in terms of the network parameters and computation.

Piezoelectric Properties of 0-3 Composite Films Based on Novel Molecular Piezoelectric Material (ATHP)2PbBr4.

Since their discovery, ferroelectric materials have shown excellent dielectric responses, pyroelectricity, piezoelectricity, electro-optical effects, nonlinear optical effects, etc. They are a class of functional materials with broad application prospects. Traditional pure inorganic piezoelectric materials have better piezoelectricity but higher rigidity; pure organic piezoelectric materials have better flexibility but havetoo small a piezoelectric coefficient. The material composite, on the other hand, can combine the advantages of both, so that it has both flexibility and a high piezoelectric coefficient. In this paper, a new molecular piezoelectric material (C5H11NO)2PbBr4 with a high Curie temperature Tc and a large piezoelectric voltage constant g33, referred to as (ATHP)2PbBr4, was used to prepare a 0-3 type piezoelectric composite film by compounding with an organic polymer material polyvinylidene fluoride (PVDF), and its ferroelectricity was investigated. The results show that the 0-3 type (ATHP)2PbBr4 piezoelectric composite film has good ferroelectricity and piezoelectricity, and the calculated piezoelectric voltage constant g33 after polarization is about 358.6 × 10-3 Vm/N, which is higher than that of PVDF material, and is important for the fabrication of high-performance piezoelectric sensors.

New Strategy for Optimizing the Properties of Copper Halide Organic-Inorganic Hybrid Lighting-emitting Materials.

Copper(I) halide organic-inorganic hybrid luminescent materials have many advantages, such as diverse structure, facile synthesis, high luminescent efficiency, tunable optical performance, etc., and show a broad application prospect in energy-saving lighting, display and other fields. However, compared with commercial rare-earth-metal-based phosphors, the reported hybrids generally suffer from poor stability and low luminescent efficiency, which are the bottleneck problem of their practical application. With the aim of developing high-performance organic-inorganic hybrid luminescent materials, a new synthesis strategy has been reported. This strategy can systematically design and synthesis copper(I) halide ionic hybrid structures by combining the covalent bonding and ionic bonding between inorganic and organic components into one structure, and use their synergistic effect to optimizing their properties. This design method is expected to develop high-performance organic-inorganic hybrid luminescent materials, promote the in-depth understanding of this field, and provide new ideas for the optimization of other types of hybrid materials.

The Effects of NAA on the Tuberous Root Yield and Quality of Rehmannia glutinosa and Its Regulatory Mechanism by Transcriptome and Metabolome Profiling.

Naphthylacetic acid (NAA) was used to increase the tuberous root yield of Rehmannia glutinosa, but the differences between its NAA-treated and control tuberous roots (NT and CG) and the regulatory mechanism of NAA effect remain unclear. In order to investigate them, NTs and CGs were used as materials, and both yield-related indices were measured; the metabolomics and transcriptomics were used to capture differentially accumulated metabolites (DAM) and to validate them via mining differentially expressed genes (DEGs), respectively. The effects of NAA treatment: increased NT mass per plant by 21.14%, through increasing the number of roots and increasing the mean root diameter; increased catalpol content by 1.2234% (p < 0.05); up-regulated 11DAMs and 596DEGs; and down-regulated 18 DAMs and 517DEGs. In particular, we discovered that NAA regulated its DAMs and biomass via 10 common metabolic pathways, and that the number of NAA-down-regulated DAMs was more than that of NAA-up-regulated DAMs in its tuberous root. Furthermore, HPLC validated the changes of several DAMs and 15 DEGs (4CL, ARF, CCoAOMT, ARGOS, etc.) associated with the yield increase and DAMs were verified by RT-qPCR. This study provided some valuable resources, such as tuberous root indices, key genes, and DAMs of Rehmannia glutinosa in response to NAA for distinguishing the CGs from NTs, and novel insights into the regulatory mechanism of NAA effects on both at the transcriptomic and metabolomic levels, so it will lay a theoretical foundation for NAA-regulated plant yield and quality, and provide references for prohibiting the uses of NAA as a swelling agent in medicinal tuber plants in China.

The Impact of the Consistency Evaluation Policy of Generic Drugs on R&D Investment Intensity of Pharmaceutical Companies-An Empirical Study Based on the Difference-in-Differences Model.

In 2016, China began to execute the consistency evaluation policy of generic drugs. Many scholars believed that the policy would stimulate pharmaceutical firms to increase R&D investment with a theoretical perspective, but few have conducted empirical studies. Therefore, we conduct a difference-in-differences (DID) model and use panel data of 111 A-share listed pharmaceutical firms from 2012 to 2020 to empirically study the impact of the consistency evaluation policy of generic drugs on pharmaceutical firms' R&D investment intensity. The result shows that the policy has a significant positive impact on the R&D investment intensity of firms with chemical generics, robust under the test for parallel trend test, placebo test, and the propensity score matching and difference-in-differences (PSM-DID) test. In addition, we further analyzed the impact of this policy on the R&D intensity of pharmaceutical firms according to the heterogeneity of enterprise's operational nature, regional distribution and profitability. From the perspective of time changes and the average effect, the R&D investment intensity of private pharmaceutical firms is more affected by the policy than state-owned enterprises; the R&D investment intensity of pharmaceutical firms in the eastern region is more affected by this policy than those in the central and the western; the R&D investment intensity of high-profitability pharmaceutical firms is more affected by the policy than those with low-profitability. The consistency evaluation policy is still being implemented, and its impact on pharmaceutical firms needs to be studied from different empirical research perspectives in the future.

Local large temperature difference and ultra-wideband photothermoelectric response of the silver nanostructure film/carbon nanotube film heterostructure.

Photothermoelectric materials have important applications in many fields. Here, we joined a silver nanostructure film and a carbon nanotube film by van der Waals force to form a heterojunction, which shows excellent photothermal and photoelectric conversion properties. The local temperature difference and the output photovoltage increase rapidly when the heterojunction is irradiated by lasers with wavelengths ranging from ultraviolet to terahertz. The maximum temperature difference reaches 215.9 K, which is significantly higher than that of other photothermoelectric materials reported in the literature. The photothermal and photoelectric responsivity depend on the wavelength of lasers, which are 175~601 K W-1 and 9.35~40.4 mV W-1, respectively. We demonstrate that light absorption of the carbon nanotube is enhanced by local surface plasmons, and the output photovoltage is dominated by Seebeck effect. The proposed heterostructure can be used as high-efficiency sensitive photothermal materials or as ultra-wideband fast-response photoelectric materials.