Grain albumin content is affected by 1BL/1RS translocation and alters the processing quality of wheat (Triticum aestivum L.).

Grain albumin is highly nutritious and closely related to the processing quality of wheat. However, few studies have explored the grain albumin content (GAC) in wheat. This study aims to uncover quantitative trait loci (QTLs) linked to wheat GAC by analyzing a doubled haploid (DH) population derived from common wheat cultivars ShanNong23 and ZhouMai17. We detected six QTLs controlling GAC on chromosomes 1B, 5A, and 6D, with individual QTL explaining 5.78% to 22.29% of the GAC variation. The effect of QGac.cau-1B.1 on GAC is attributed to the presence of the 1BL/1RS translocation, indicating that the 1BL/1RS translocation increase of GAC compared with the non-1BL/1RS translocation lines. The higher GAC observed in 1BL/1RS lines could be primarily attributed to the increased accumulation of omega-secalin, omega-gliadin, low molecular weight glutenin subunit and ribosomal protein content. Additionally, we also found that the SDS-sedimentation value of whole wheat flour was decreased by adding albumin solution. These results advance our understanding of the genetic basis of GAC and offer novel perspectives for enhancing wheat quality through genetic enhancements.

Tagging large CNV blocks in wheat boosts digitalization of germplasm resources by ultra-low-coverage sequencing.

BACKGROUND: The massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources. RESULTS: We develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb ( http://wheat.cau.edu.cn/WheatCNVb/ ), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties. CONCLUSIONS: The CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.

Optical design and fabrication of a multi-channel imaging spectrometer for combustion flame monitoring.

We design and construct a broadband integrated multi-channel imaging spectrometer (MCIS) from visible light to near-infrared. This system can directly obtain spectral images that conform to the consistent visual habits of the human eyes through a single exposure of the detector. The genetic algorithm is used to calculate system parameters to minimize pixel waste between spectral channels, achieving nearly 100% utilization of detector pixels. The field stop suppresses stray light in the system. This device is used for imaging an optical-resolution target, an object, and a furnace to verify the basic principles of the system. The results indicate that the system can effectively utilize detectors to monitor high-temperature objects in the visible to near-infrared wavelength range.

Enhanced Dissolution and Bioavailability of Curcumin Nanocrystals Prepared by Hot Melt Extrusion Technology.

Purpose: Curcumin nanocrystals (Cur-NCs) were prepared by hot melt extrusion (HME) technology to improve the dissolution and bioavailability of curcumin (Cur). Methods: Cur-NCs with different drug-carrier ratios were prepared by one-step extrusion process with Eudragit® EPO (EEP) as the carrier. The dispersed size and solid state of Cur in extruded samples were characterized by dynamic light scattering (DLS), scanning electron microscope (SEM), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). The thermal stability of Cur was analyzed by thermogravimetric analysis (TGA) and high performance liquid chromatography (HPLC). Dissolution and pharmacokinetics were studied to evaluate the improvement of dissolution and absorption of Cur by nano-preparation. Results: Cur-NCs with particle sizes in the range of 50~150 nm were successfully prepared by using drug-carrier ratios of 1:1, 2:1 and 4:1, and the crystal form of Cur was Form 1 both before and after HME. The extrudate powders showed very efficient dissolution with the cumulative dissolution percentage of 80% in less than 2 min, and the intrinsic dissolution rates of them were 13.68 ± 1.20 mg/min/cm2, 11.78 ± 0.57 mg/min/cm2 and 4.35 ± 0.20 mg/min/cm2, respectively, whereas that of pure Cur was only 0.04 ± 0.00 mg/min/cm2. The TGA data demonstrated that the degradation temperature of Cur was about 250 °C, while the HPLC results showed Cur was degraded when extruded at the temperature over 150 °C. Pharmacokinetic experiment showed a significant improvement in the absorption of Cur. The Cmax of Cur in the Cur-NC group was 1.68 times that of pure Cur group, and the Cmax and area under the curve (AUC0-∞) of metabolites were 2.79 and 4.07 times compared with pure Cur group. Conclusion: Cur-NCs can be prepared by HME technology in one step, which significantly improves the dissolution and bioavailability of Cur. Such a novel method for preparing insoluble drug nanocrystals has broad application prospects.

DNA Molecular Glue Assisted Bacterial Conjugative Transfer.

Bacterial conjugation, a commonly used method to horizontally transfer functional genes from donor to recipient strains, plays an important role in the genetic manipulation of bacteria for basic research and industrial production. Successful conjugation depends on the donor-recipient cell recognition and a tight mating junction formation. However, the efficiency of conjugative transfer is usually very low. In this work, we developed a new technique that employed DNA molecule "glue" to increase the match frequency and the interaction stability between the donor and recipient cells. We used two E. coli strains, ETZ and BL21, as a model system, and modified them with the complementary ssDNA oligonucleotides by click chemistry. The conjugation efficiency of the modified bacteria was improved more than 4 times from 10% to 46%. This technique is simple and generalizable as it only relies on the active amino groups on the bacterial surface. It is expected to have broad applications in constructing engineered bacteria.

A tree-based corpus annotated with Cyber-Syndrome, symptoms, and acupoints.

Prolonged and over-excessive interaction with cyberspace poses a threat to people's health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people's health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

A k-mer-based pangenome approach for cataloging seed-storage-protein genes in wheat to facilitate genotype-to-phenotype prediction and improvement of end-use quality.

Wheat is a staple food for more than 35% of the world's population, with wheat flour used to make hundreds of baked goods. Superior end-use quality is a major breeding target; however, improving it is especially time-consuming and expensive. Furthermore, genes encoding seed-storage proteins (SSPs) form multi-gene families and are repetitive, with gaps commonplace in several genome assemblies. To overcome these barriers and efficiently identify superior wheat SSP alleles, we developed "PanSK" (Pan-SSP k-mer) for genotype-to-phenotype prediction based on an SSP-based pangenome resource. PanSK uses 29-mer sequences that represent each SSP gene at the pangenomic level to reveal untapped diversity across landraces and modern cultivars. Genome-wide association studies with k-mers identified 23 SSP genes associated with end-use quality that represent novel targets for improvement. We evaluated the effect of rye secalin genes on end-use quality and found that removal of ω-secalins from 1BL/1RS wheat translocation lines is associated with enhanced end-use quality. Finally, using machine-learning-based prediction inspired by PanSK, we predicted the quality phenotypes with high accuracy from genotypes alone. This study provides an effective approach for genome design based on SSP genes, enabling the breeding of wheat varieties with superior processing capabilities and improved end-use quality.

VPAC2 Receptor Signaling Promotes Growth and Immunosuppression in Pancreatic Cancer.

Pancreatic cancer (PDAC) harbors a complex tumor microenvironment (TME), and crosstalk between cells in the TME can contribute to drug resistance and relapse. Vasoactive intestinal peptide (VIP) is overexpressed in PDAC, and VIP receptors expressed on T cells are a targetable pathway that sensitizes PDAC to immunotherapy. In this study, we showed that pancreatic cancer cells engage in autocrine VIP signaling through VIP receptor 2 (VPAC2). High co-expression of VIP with VPAC2 correlated with reduced relapse-free survival in PDAC patients. VPAC2 activation in PDAC cells upregulated piwi-like RNA-mediated gene silencing 2 (Piwil2), which stimulated cancer cell clonogenic growth. In addition, VPAC2 signaling increased expression of TGF-ß1 to inhibit T cell function. Loss of VPAC2 on PDAC cells led to reduced tumor growth and increased sensitivity to anti-PD1 immunotherapy in mouse models of PDAC. Overall, these findings expand our understanding of the role of VIP/VPAC2 signaling in PDAC and provide the rationale for developing potent VPAC2-specific antagonists for treating PDAC patients.

Innovative computational tools provide new insights into the polyploid wheat genome.

Bread wheat (Triticum aestivum) is an important crop and serves as a significant source of protein and calories for humans, worldwide. Nevertheless, its large and allopolyploid genome poses constraints on genetic improvement. The complex reticulate evolutionary history and the intricacy of genomic resources make the deciphering of the functional genome considerably more challenging. Recently, we have developed a comprehensive list of versatile computational tools with the integration of statistical models for dissecting the polyploid wheat genome. Here, we summarize the methodological innovations and applications of these tools and databases. A series of step-by-step examples illustrates how these tools can be utilized for dissecting wheat germplasm resources and unveiling functional genes associated with important agronomic traits. Furthermore, we outline future perspectives on new advanced tools and databases, taking into consideration the unique features of bread wheat, to accelerate genomic-assisted wheat breeding.

Glucagon-like peptide-1 analogs: Miracle drugs are blooming?

Glucagon-like peptide-1 (GLP-1), secreted by L cells in the small intestine, assumes a central role in managing type 2 diabetes mellitus (T2DM) and obesity. Its influence on insulin secretion and gastric emptying positions it as a therapeutic linchpin. However, the limited applicability of native GLP-1 stems from its short half-life, primarily due to glomerular filtration and the inactivating effect of dipeptidyl peptidase-IV (DPP-IV). To address this, various structural modification strategies have been developed to extend GLP-1's half-life. Despite the commendable efficacy displayed by current GLP-1 receptor agonists, inherent limitations persist. A paradigm shift emerges with the advent of unimolecular multi-agonists, such as the recently introduced tirzepatide, wherein GLP-1 is ingeniously combined with other gastrointestinal hormones. This novel approach has captured the spotlight within the diabetes and obesity research community. This review summarizes the physiological functions of GLP-1, systematically explores diverse structural modifications, delves into the realm of unimolecular multi-agonists, and provides a nuanced portrayal of the developmental prospects that lie ahead for GLP-1 analogs.

Preparation of 3D flexible SERS substrates by mixing gold nanorods in hydrogels for the detection of malachite green and crystal violet.

A simple and cost-effective fabrication method of gold nanorods (AuNRs) nanoparticles hybridized with polyvinyl alcohol hydrogel (AuNR/PVA) for SERS substrate is described. The AuNR/PVA achieves the control of inter-particle nanogap by modulating the density of gold nanorods, and inter-particle nanogap by the spatial deformation of the hydrogel, and the reduction of the gap between the AuNRs deposited on hydrogel makes the SERS enhancement. In addition, the AuNR/PVA substrate maintains high SERS activity after more than 100 cycles of bending and storage in air for 30 days, and the substrate possesses high sensitivity and high reproducibility. Combining a flexible and transparent surface-enhanced Raman spectroscopy (SERS) substrate for in situ detection with a small portable Raman can be applied to scenarios such as environmental detection and hazardous materials detection. The substrate showed excellent SERS activity against malachite green (MG) and crystal violet (CV) with limits of detection of 1.18 × 10-13 M and 7.17 × 10-12 M, respectively. The usability of the proposed SERS substrate was demonstrated by detecting the above contaminants in aquatic water. This work not only utilizes a cost-effective method for mass production but also provides a reliable and convenient platform for the preparation of other noble metal flexible substrates.

Prognostic factors in surgically treated tongue squamous cell carcinoma in stage T1-2N0-1M0: A retrospective analysis.

PURPOSE: The study aimed to retrospectively identify the prognostic factors of surgically treated primary tongue squamous cell carcinoma (TSCC) cases and assess the benefits of surgical neck lymph node dissection (LND) in early-stage cancer. METHODS: Patients with primary TSCC with pT1-2N0-1M0 stage without distant metastasis who were treated with surgery during 2014-2016 at Xiangya Hospital, Central South University were included. Univariate and multivariate Cox models were constructed to explore prognostic factors of overall survival (OS), disease-free survival (DFS), and local recurrence-free survival (LRFS). Sub-group analysis was used to assess the effect of adjuvant therapy and the prognostic value of LND for the early-stage patients. RESULTS: In total, 440 patients met the inclusion criteria. During the follow-up period, the 5-year OS, DFS, were 84.4% and 70.0%, respectively. Univariate analysis showed that TNM stage, lymphovascular invasion (LVI), and/or perineural invasion (PNI), pathological differentiation, etc. were significant predictors of OS and DFS. Multivariate analysis showed that TNM stage and the degree of pathological differentiation were independent prognostic factors for all outcomes. Besides, the number of cervical LND could independently predict both DFS and LRFS while LVI/PNI were associated with DFS. And high-quality neck LND (≥30) significantly improved DFS and LRFS for patients of pT1cN0M0 or stage I as compared to those without LND. CONCLUSIONS: TNM stage and pathological differentiation were crucial prognostic factors for postoperative patients with TSCC. Notably, high-quality cervical LND was beneficial for the improvement of DFS and LRFS for patients of pT1cN0M0 or stage I.

Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke.

This study aimed to create machine learning models for predicting early neurological deterioration and risk classification in acute ischemic stroke (AIS) before intravenous thrombolysis (IVT). The study included 704 AIS patients categorized into END and non-END groups. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the best predictors from clinical indicators, leading to the creation of Model 1. Univariate and multivariate logistic regression analyses identified independent predictive factors for END from inflammatory cell ratios. These factors were combined with clinical indicators, forming Model 2. Receiver operating characteristic (ROC) curves assessed the models' predictive performance. Key variables for Model 1 included the NIHSS score, systolic blood pressure, and lymphocyte percentage. Neutrophil-to-Lymphocyte ratio, Platelet-to-Neutrophil ratio, and Platelet-to-Lymphocyte ratio independently predicted END. Model 1 exhibited moderate predictive ability (AUC 0.721 in training, AUC 0.635 in test). Model 2, which integrated clinical indicators and inflammatory cell ratios, demonstrated strong performance in both training (AUC 0.862) and test (AUC 0.816). Machine learning models, combining clinical indicators and inflammatory cell ratios before IVT, accurately predict END and associated risk in AIS.

Preparation of SERS base membrane with cellulose compound dopamine and determination of hypochlorite.

Flexible silver substrates were made by in situ reduction of silver nanoparticles in bacterial cellulose membranes using the unique advantage of dopamine. Subsequently, we modified the substrate with 4-mercaptophenol (4-MP), a molecule capable of specifically recognizing ClO-, and its corresponding SERS signal changes with the concentration of hypochlorite, thus allowing the quantitative detection of ClO- content. The method showed a negative linear correlation (R2 = 0.9567) with the SERS intensity at 1077 cm-1 over the concentration range 0.5-100 µM, and the detection limit was 0.15 µM. The RSD of the SERS intensity at 1077 cm-1 under five batches was 4.2%, which proved the good reproducibility of P-BCM-Ag NP-MP. Finally, the P-BCM-Ag NPs were used for the detection of hypochlorite in cell contents, artificial urine, and clinical serum samples, utilizing spike experiments in all three environments. The recoveries were in the range 90-110% indicating the accuracy of the method for the detection of hypochlorite and validating the promising application of this assay for practical detection in intricate biological samples.

[Establishment and evaluation of a predictive model for early neurological deterioration after intravenous thrombolysis in acute ischemic stroke based on machine learning].

OBJECTIVE: To establish a machine learning model to predict the risk of early neurological deterioration (END) based on the clinical and laboratory data of patients with acute ischemic stroke (AIS) before intravenous thrombolysis. METHODS: The clinical data of AIS patients who received intravenous thrombolytic with recombinant tissue plasminogen activator (rt-PA) at the Stroke Center of the First Hospital of Qinhuangdao City from January 2019 to July 2022 were retrospectively analyzed. Patients were divided into END group and non-END group according to whether END appeared after intravenous thrombolytic. Clinical data of patients at admission were collected, including demographic characteristics, clinical evaluation, comorbidification, drug use history, laboratory tests, etc. Univariate and multivariate Logistic regression analysis were performed to screen out the independent predictors of the END of AIS patients after intravenous thrombolytic. The study subjects were randomly divided into a training set and a test set in a 7 : 3 ratio. Four machine learning prediction models, including Logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF), were established based on independent predictors. The receiver operator characteristic curve (ROC curve) was used to evaluate the predictive ability of each model in END. RESULTS: A total of 704 patients were enrolled, of whom 99 were identified as END and 605 as non-END. Univariate and multivariate Logistic regression analysis was used to screen out the National Institutes of Health stroke scale [NIHSS, odds ratio (OR) = 1.049, 95% confidence interval (95%CI) was 1.015-1.082, P = 0.004], systolic blood pressure (OR = 1.013, 95%CI was 1.004-1.022, P = 0.004), lymphocyte percentage (LYM%, OR = 0.903, 95%CI was 0.853-0.953, P < 0.001), platelet to lymphocyte ratio (PLR, OR = 1.007, 95%CI was 1.002-1.014, P = 0.013) were the independent predictors of END in AIS patients after intravenous thrombolysis. The area under the curve (AUC) of LR, KNN, SVM, and RF machine learning models in the test dataset were 0.789 (95%CI was 0.675-0.902), 0.797 (95%CI was 0.685-0.910), 0.851 (95%CI was 0.751-0.952) and 0.809 (95%CI was 0.699-0.919), respectively. The RF model had the highest sensitivity (95.7%). The accuracy (0.736), specificity (72.0%) and AUC of SVM model were the highest, and its overall prediction ability was better than the other three models. CONCLUSIONS: Machine learning models have a potential role in early predicting the risk of END after intravenous thrombolysis in AIS patients, and can provide help in clinical decision-making for intravenous thrombolysis.

Hyperbranched Polymer Dots with Strong Absorption and High Fluorescence Quantum Yield for In Vivo NIR-II Imaging.

In order to improve the fluorescence quantum yield (QY) of NIR-II-emitting nanoparticles, D-A-D fluorophores are typically linked to intramolecular rotatable units to reduce aggregation-induced quenching. However, incorporating such units often leads to a twisted molecular backbone, which affects the coupling within the D-A-D unit and, as a result, lowers the absorption. Here, we overcome this limitation by cross-linking the NIR-II fluorophores to form a 2D polymer network, which simultaneously achieves a high QY by well-controlled fluorophore separation and strong absorption by restricting intramolecular distortion. Using the strategy, we developed polymer dots with the highest NIR-II single-particle brightness among reported D-A-D-based nanoparticles and applied them for imaging of hindlimb vasculatures and tumors as well as fluorescence-guided tumor resection. The high brightness of the polymer dots offered exceptional image quality and excellent surgical results, showing a promising performance for these applications.

Design of off-axis aspheric four-mirror non-axial mechanical zoom optical system with large relative aperture.

A large relative aperture is essential to improve the spatial resolution of zoom systems. To overcome the limitations of the existing off-axis reflective mechanical zoom system with a low zoom rate and a small relative aperture, this paper proposes a non-axis movement method for increasing the degrees of freedom. On the basis of nodal aberration theory, passive eccentricity is changed into active eccentricity to achieve wave aberration balance in the multiple structures of the zoom imaging system. An off-axis aspherical four-mirror non-axial mechanical zoom optical system is designed and fabricated. The prototype has been successfully processed and assembled with the help of computer-aided alignment technology. The prototype's F-number is 4 and zoom ratio is 4.57:1. Experimental results verify the feasibility of the proposed method.

Research Progress of SERS Sensors Based on Hydrogen Peroxide and Related Substances.

Hydrogen peroxide (H2O2) has an important role in living organisms, and its detection is of great importance in medical, chemical, and food safety applications. This review provides a comparison of different types of Surface-enhanced Raman scattering (SERS) sensors for H2O2 and related substances with respect to their detection limits, which are of interest due to high sensitivity compared to conventional sensors. According to the latest research report, this review focuses on the sensing mechanism of different sensors and summarizes the linear range, detection limits, and cellular applications of new SERS sensors, and discusses the limitations in vivo and future prospects of SERS technology for the detection of H2O2.

Optimized Charge Storage in Aza-Based Covalent Organic Frameworks by Tuning Electrolyte Proton Activity.

Proton activity in electrolytes plays a crucial role in deciding the electrochemical performance of aqueous batteries. On the one hand, it can influence the capacity and rate performance of host materials because of the high redox activity of protons. On the other hand, it can also cause a severe hydrogen evolution reaction (HER) when the protons are aggregated near the electrode/electrolyte interface. The HER dramatically limits the potential window and the cycling stability of the electrodes. Therefore, it is critical to clarify the impact of electrolyte proton activity on the battery macro-electrochemical performance. In this work, using an aza-based covalent organic framework (COF) as a representative host material, we studied the effect of electrolyte proton activity on the potential window, storage capacity, rate performance, and cycle stability in various electrolytes. A tradeoff relationship between proton redox reactions and the HER in the COF host is revealed by utilizing various in situ and ex situ characterizations. Moreover, the origin of proton activity in near-neutral electrolytes is discussed in detail and is confirmed to be related to the hydrated water molecules in the first solvation shell. A detailed analysis of the charge storage process in the COFs is presented. These understandings can be of importance for utilizing the electrolyte proton activity to build high-energy aqueous batteries.