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Abstract The aim of this study is to assess the effectiveness of conventional and two additional functional markers derived from standard cardiac magnetic resonance (CMR) images in detecting the occurrence of late gadolinium enhancement (LGE) in patients with secondary cardiac amyloidosis (CA) related to multiple myeloma (MM). This study retrospectively included 32 patients with preserved ejection fraction (EF) who had MM-CA diagnosed consecutively. Conventional left ventricular (LV) function markers and two additional functional markers, namely myocardial contraction fraction (MCF) and LV long-axis strain (LAS), were obtained using commercial cardiac post-processing software. Logistic regression analyses and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive performances. (1) There were no notable distinctions in clinical features between the LGE+ and LGE− groups, with the exception of a reduced systolic blood pressure in the former (105.60 ± 18.85 mmHg vs. 124.50 ± 20.95 mmHg, P = 0.022). (2) Patients with MM-CA presented with intractable heart failure with preserved ejection fraction (HFpEF). The LVEF in the LGE+ group exhibited a greater reduction (54.27%, IQR 51.59–58.39%) in comparison to the LGE− group (P

Abstract African American (AA) women are twice as likely to develop triple-negative breast cancer (TNBC) as women of European descent. Additionally, AA women with TNBC present a much more aggressive disease course than their European American (EA) counterparts. Thus, there is an unmet clinical need to identify race-specific biomarkers and improve survival outcomes in AA patients with TNBC. The minus-end directed microtubule motor protein kinesin family member C1 (KIFC1) promotes centrosome clustering and chromosomal instability and is often overexpressed in TNBC. Previous findings suggest that KIFC1 plays a role in cell proliferation and migration in TNBC cells from AAs and that the levels of nuclear KIFC1 (nKIFC1) are particularly high in AA patients with TNBC. The nuclear localization of KIFC1 in interphase may underlie its previously unrecognized race-specific association. In this study, we found that in TNBC cells derived from AAs, nKIFC1 interacted with the tumor suppressor myosin heavy chain 9 (MYH9) over EA cells. Treatment of AA TNBC cells with commercial inhibitors of KIFC1 and MYH9 disrupted the interaction between KIFC1 and MYH9. To characterize the racial differences in the KIFC1-MYH9-MYC axis in TNBC, we established homozygous KIFC1 knockout (KO) TNBC cell lines. KIFC1 KO significantly inhibited proliferation, migration, and invasion in AA TNBC cells but not in EA TNBC cells. RNA sequencing analysis showed significant downregulation of genes involved in cell migration, invasion, and metastasis upon KIFC1 KO in TNBC cell lines from AAs compared to those from EAs. These data indicate that mechanistically, the role of nKIFC1 in driving TNBC progression and metastasis is stronger in AA patients than in EA patients, and that KIFC1 may be a critical therapeutic target for AA patients with TNBC. Graphical abstract

IntroductionProtein O-GlcNAcylation is a dynamic post-translational modification involved in major cellular processes and associated with many human diseases. Bioinformatic prediction of O-GlcNAc sites before experimental validation is a challenge task in O-GlcNAc research. Recent advancements in deep learning algorithms and the availability of O-GlcNAc proteomics data present an opportunity to improve O-GlcNAc site prediction.ObjectivesThis study aims to develop a deep learning-based tool to improve O-GlcNAcylation site prediction.MethodsWe construct an annotated unbalanced O-GlcNAcylation data set and propose a new deep learning framework, DeepO-GlcNAc, using Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with attention mechanism.ResultsThe ablation study confirms that the additional model components in DeepO-GlcNAc, such as attention mechanisms and LSTM, contribute positively to improving prediction performance. Our model demonstrates strong robustness across five cross-species datasets, excluding humans. We also compare our model with three external predictors using an independent dataset. Our results demonstrated that DeepO-GlcNAc outperforms the external predictors, achieving an accuracy of 92%, an average precision of 72%, a MCC of 0.60, and an AUC of 92% in ROC analysis. Moreover, we have implemented DeepO-GlcNAc as a web server to facilitate further investigation and usage by the scientific community.ConclusionOur work demonstrates the feasibility of utilizing deep learning for O-GlcNAc site prediction and provides a novel tool for O-GlcNAc investigation.

Abstract To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model’s ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model’s volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.

Inflammatory bowel diseases (IBD) etiology is multifactorial. Luminal microRNAs (miRNAs) have been suspected to play a role in the promotion of chronic inflammation, but the extent to which fecal miRNAs are interacting with the intestinal ecosystem in a way that contribute to diseases, including IBD, remains unknown. Here, fecal let-7b and miR-21 were found elevated, associated with inflammation, and correlating with multiple bacteria in IBD patients and IL-10–/– mice, model of spontaneous colitis. Using an in vitro microbiota modeling system, we revealed that these two miRNAs can directly modify the composition and function of complex human microbiota, increasing their proinflammatory potential. In vivo investigations revealed that luminal increase of let-7b drastically alters the intestinal microbiota and enhances macrophages’ associated proinflammatory cytokines (TNF, IL-6, and IL-1β). Such proinflammatory effects are resilient and dependent on the bacterial presence. Moreover, we identified that besides impairing the intestinal barrier function, miR-21 increases myeloperoxidase and antimicrobial peptides secretion, causing intestinal dysbiosis. More importantly, in vivo inhibition of let-7b and miR-21 with anti-miRNAs significantly improved the intestinal mucosal barrier function and promoted a healthier host-microbiota interaction in the intestinal lining, which altogether conferred protection against colitis. In summary, we provide evidence of the functional significance of fecal miRNAs in host-microbiota communication, highlighting their therapeutic potential in intestinal inflammation and dysbiosis-related conditions, such as IBD.

Background and Aims: The overexpression of glial cell-derived neurotrophic factor (GDNF) in the liver and adipose tissues offers strong protection against high-fat diet (HFD)-induced obesity in mice. We hypothesize that sustainably enhancing GDNF expression in the liver may provide a therapeutic effect that can prevent the progression of HFD-induced obesity in mice. Methods: Expression lentivector encoding mouse GDNF (GDNF(pDNA) or empty vector (pDNA, control) were encapsulated in lipid nanoparticles (LNPs) using the thin-film hydration method. Mice were fed with regular diet (RD) or HFD for 20 weeks prior to injection and the GDNF and control vector-loaded LNPs were administered by intravenous (IV) injection to mice once weekly for 5 weeks. Changes in body weight were monitored and mice tissues were collected and imaged for fluorescence using an IVIS in vivo imaging system. Post-treatment abdominal fat weight, colon length, and spleen weight were obtained. GDNF protein levels in the liver and serum were quantified by enzyme-linked immunosorbent assay, while liver AKT serine/threonine kinase and AMP-activated protein kinase phosphorylation levels were evaluated by Western blotting. Results: IV-injected GDNF(pDNA)-loaded LNPs targeted the liver and remained in there for up to 15 days postinjection. A single injection of GDNF(pDNA)-loaded LNPs significantly increased GDNF expression for 7 days and consequently increased the levels of phosphorylated AKT serine/threonine kinase and AMP-activated protein kinase. Once weekly injections of GDNF(pDNA)-loaded LNPs for 5 weeks slowed increase in body weight, reduced abdominal fat, and modulated the gut microbiota toward a healthier composition in HFD-fed mice. Conclusion: GDNF(pDNA)-loaded LNPs could potentially be developed as a therapeutic strategy to reverse weight gain in obese patients.

In a previous study, rice bread was prepared using a combination of rice–wheat mixed flour. To investigate the impact of the partial adoption of extruded rice flour (ERF) on mixed flour (MF) and mixed dough (MD), the effects of adding ERF on the pasting, mixing characteristics, texture, and water retention of the MF and MD were examined by a rapid visco analyzer (RVA), Mixolab, texture profile analysis (TPA), and a low-field nuclear magnetic resonance analyzer (LF-NMR). The PV, TV, BD, FV, and SV of the MF declined as the incorporated amount of ERF increased. There was no significant difference in the PT at the 5–15% addition level (p < 0.05), but it showed an increasing trend at the 20–30% level (p < 0.05). The incorporation of ERF led to a significant increase in the water absorption (WA) of the MD, while the DT, ST, C2, C3, C4, and C5 exhibited a declining trend. The texture analysis revealed a significant decrease in the dough hardness with the addition of ERF, with a 55% reduction in the hardness of the 30% improved mixed dough (IMD), and the cohesiveness increased significantly (p < 0.05). The IMD was mainly composed of weakly bound water. The content of weakly bound water increased with the ERF amount.

Lipid nanoparticle (LNP)-based drug delivery systems (DDSs) are widely recognized for their ability to enhance efficient and precise delivery of therapeutic agents, including nucleic acids like DNA and mRNA. Despite this acknowledgment, there is a notable knowledge gap regarding the systemic biodistribution and organ accumulation of these nanoparticles. The ability to track LNPs in vivo is crucial for understanding their fate within biological systems. Fluorescent labeling of LNPs facilitates real-time tracking, quantification, and visualization of their behavior within biological systems, providing valuable insights into biodistribution, cellular uptake, and the optimization of drug delivery strategies. Our prior research established reversely engineered LNPs as an exceptional mRNA delivery platform for treating ulcerative colitis. This study presents a detailed protocol for labeling interleukin-22 (IL-22) mRNA-loaded LNPs, their oral administration to mice, and visualization of DiR-labeled LNPs biodistribution in the gastrointestinal tract using IVIS spectrum. This fluorescence-based approach will assist researchers in gaining a dynamic understanding of nanoparticle fate in other models of interest.

Abstract Multiple tumors are synergistically promoted by c-Met and TRK, and blocking their cross-signalling pathway may give better effects. In this study, we developed a tyrosine kinase inhibitor 1D228, which exhibited excellent anti-tumor activity by targeting c-Met and TRK. Models in vitro, 1D228 showed a significant better inhibition on cancer cell proliferation and migration than the positive drug Tepotinib. Models in vivo, 1D228 showed robust anti-tumor effect on gastric and liver tumor growth with 94.8% and 93.4% of the TGI, respectively, comparing 67.61% and 63.9% of Tepotinib. Importantly, compared with the combination of Larotrectinib and Tepotinib, 1D228 monotherapy in MKN45 xenograft tumor models showed stronger antitumor activity and lower toxicity. Mechanistic studies showed that 1D228 can largely inhibit the phosphorylation of TRKB and c-Met. Interestingly, both kinases, TRKs and c-Met, have been found to be co-expressed at high levels in patients with gastric cancer through IHC. Furthermore, bioinformatics analysis has revealed that both genes are abnormally co-expressed in multiple types of cancer. Cell cycle analysis found that 1D228 induced G0/G1 arrest by inhibiting cyclin D1. Additionally, vascular endothelial cells also showed a pronounced response to 1D228 due to its expression of TRKB and c-Met. 1D228 suppressed the migration and tube formation of endothelial cells, which are the key functions of tumor angiogenesis. Taken together, compound 1D228 may be a promising candidate for the next generation of c-Met and TRK inhibitors for cancer treatment, and offers a novel potential treatment strategy for cancer patients with abnormal expressions of c-Met or NTRK, or simultaneous of them.

Ubiquitination modifications permit the degradation of labelled target proteins with the assistance of proteasomes and lysosomes, which is the main protein degradation pathway in eukaryotic cells. Polyubiquitination modifications of proteins can also affect their functions. De-ubiquitinating enzymes reverse the process of ubiquitination via cleavage of the ubiquitin molecule, which is known as a de-ubiquitination. It was demonstrated that ubiquitination and de-ubiquitination play key regulatory roles in fatty acid transport, de novo synthesis, and desaturation in dairy mammary epithelial cells. In addition, natural plant extracts, such as stigmasterol, promote milk fat synthesis in epithelial cells via the ubiquitination pathway. This paper reviews the current research on ubiquitination and de-ubiquitination in dairy milk fat production, with a view to providing a reference for subsequent research on milk fat and exploring new directions for the improvement of milk quality.

The zinc oxide rotary kiln, as an essential piece of equipment in the zinc smelting industrial process, is presenting new challenges in process control. China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry, including green production and the use of fewer resources; thus, traditional stability control is no longer suitable for multi-objective control tasks. Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics (CFD) simulation models to study its dynamics, these models cannot be directly applied to process control due to their high computational complexity. To address these issues, this paper proposes a multi-objective adaptive optimization model predictive control (MAO-MPC) method based on sparse identification. More specifically, with a large amount of data collected from a CFD model, a sparse regression problem is first formulated and solved to obtain a reduction model. Then, a two-layered control framework including real-time optimization (RTO) and model predictive control (MPC) is designed. In the RTO layer, an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up. By solving the optimization problem in real time, a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state. Our experiments show the strength and reliability of the proposed method, which reduces the usage of coal while maintaining high profits.

Abstract Chemotherapy is widely used to treat colorectal cancer (CRC). Despite its substantial benefits, the development of drug resistance and adverse effects remain challenging. This study aimed to elucidate a novel role of glucagon in anti‐cancer therapy. In a series of in vitro experiments, glucagon inhibited cell migration and tube formation in both endothelial and tumor cells. In vivo studies demonstrated decreased tumor blood vessels and fewer pseudo‐vessels in mice treated with glucagon. The combination of glucagon and chemotherapy exhibited enhanced tumor inhibition. Mechanistic studies demonstrated that glucagon increased the permeability of blood vessels, leading to a pronounced disruption of vessel morphology. Signaling pathway analysis identified a VEGF/VEGFR‐dependent mechanism whereby glucagon attenuated angiogenesis through its receptor. Clinical data analysis revealed a positive correlation between elevated glucagon expression and chemotherapy response. This is the first study to reveal a role for glucagon in inhibiting angiogenesis and vascular mimicry. Additionally, the delivery of glucagon‐encapsulated PEGylated liposomes to tumor‐bearing mice amplified the inhibition of angiogenesis and vascular mimicry, consequently reinforcing chemotherapy efficacy. Collectively, the findings demonstrate the role of glucagon in inhibiting tumor vessel network and suggest the potential utility of glucagon as a promising predictive marker for patients with CRC receiving chemotherapy.

IntroductionIn order to solve the problem of precise identification and counting of tea pests, this study has proposed a novel tea pest identification method based on improved YOLOv7 network.MethodsThis method used MPDIoU to optimize the original loss function, which improved the convergence speed of the model and simplifies the calculation process. Replace part of the network structure of the original model using Spatial and Channel reconstruction Convolution to reduce redundant features, lower the complexity of the model, and reduce computational costs. The Vision Transformer with Bi-Level Routing Attention has been incorporated to enhance the flexibility of model calculation allocation and content perception.ResultsThe experimental results revealed that the enhanced YOLOv7 model significantly boosted Precision, Recall, F1, and mAP by 5.68%, 5.14%, 5.41%, and 2.58% respectively, compared to the original YOLOv7. Furthermore, when compared to deep learning networks such as SSD, Faster Region-based Convolutional Neural Network (RCNN), and the original YOLOv7, this method proves to be superior while being externally validated. It exhibited a noticeable improvement in the FPS rates, with increments of 5.75 HZ, 34.42 HZ, and 25.44 HZ respectively. Moreover, the mAP for actual detection experiences significant enhancements, with respective increases of 2.49%, 12.26%, and 7.26%. Additionally, the parameter size is reduced by 1.39 G relative to the original model.DiscussionThe improved model can not only identify and count tea pests efficiently and accurately, but also has the characteristics of high recognition rate, low parameters and high detection speed. It is of great significance to achieve realize the intelligent and precise prevention and control of tea pests.

Inflammatory bowel disease (IBD), marked by chronic gastrointestinal tract inflammation, poses a significant global medical challenge. Current treatments for IBD, including corticosteroids, immunomodulators, and biologics, often require frequent systemic administration through parenteral delivery, leading to nonspecific drug distribution, suboptimal therapeutic outcomes, and adverse effects. There is a pressing need for a targeted drug delivery system to enhance drug efficacy and minimize its systemic impact. Nanotechnology emerges as a transformative solution, enabling precise oral drug delivery to inflamed intestinal tissues, reducing off-target effects, and enhancing therapeutic efficiency. The advantages include heightened bioavailability, sustained drug release, and improved cellular uptake. Additionally, the nano-based approach allows for the integration of theranostic elements, enabling simultaneous diagnosis and treatment. Recent preclinical advances in oral IBD treatments, particularly with nanoformulations such as functionalized polymeric and lipid nanoparticles, demonstrate remarkable cell-targeting ability and biosafety, promising to overcome the limitations of conventional therapies. These developments signify a paradigm shift toward personalized and effective oral IBD management. This review explores the potential of oral nanomedicine to enhance IBD treatment significantly, focusing specifically on cell-targeting oral drug delivery system for potential use in IBD management. We also examine emerging technologies such as theranostic nanoparticles and artificial intelligence, identifying avenues for the practical translation of nanomedicines into clinical applications.

In this paper, the texture characteristics, stress relaxation and gluten strength of rice flour mixed flour dough were analyzed by texture analyzer and electronic gluten analyzer Glugraph-E. From the point of view of mechanics, the effects of different kinds of rice flour on the gluten of high gluten wheat flour were studied. The results showed that the effects of different varieties of rice flour on the texture characteristics of mixed flour dough were different. The hardness of LD5 (Longdao 5) and LD19 (Longdao 19) of rice high gluten wheat mixed dough increased with the increasing of rice flour addition. In terms of elasticity, when the amount of rice flour added was 40%, LD23 (Longdao 23) showed an obvious upward trend, LD5 decreased significantly, and other varieties had little change. When the 40% rice flour was added, the cohesiveness and adhesiveness of rice flour decreased significantly except LD5 and LJ46 (Longjing 46). When the amount of rice flour was 0%~40%, the rice resilience of all varieties decreased. In terms of stress relaxation, when the amount of rice flour was increased to 40%, the stress relaxation time of LD5, LD20 (Longdao 20) and LD25 (Longdao 25) was longer, which meant that there was large polymer formation in the dough and the dough tended to be rigid. The tensile value and rebound value measured by the gluten meter showed a downward trend for all rice varieties, and the dough elasticity and strength increased. This research would lay a good foundation for further development of rice flour mixed products.

The salt taste-enhancing and antioxidant effect of the Maillard reaction on peanut protein hydrolysates (PPH) was explored. The multi-spectroscopic and sensory analysis results showed that the Maillard reaction products (MRPs) of hexose (glucose and galactose) had slower reaction rates than those of pentose (xylose and arabinose), but stronger umami and increasing saltiness effects. The Maillard reaction can improve the flavor of PPH, and the galactose-Maillard reaction product (Ga-MRP) has the best umami and salinity-enhancing effects. The measured molecular weight of Ga-MRP were all below 3000 Da, among which the molecular weights between 500–3000 Da accounted for 46.7%. The products produced during the Maillard reaction process resulted in a decrease in brightness and an increase in red value of Ga-MRP. The amino acid analysis results revealed that compared with PPH, the content of salty and umami amino acids in Ga-MRPs decreased, but their proportion in total free amino acids increased, and the content of bitter amino acids decreased. In addition, the Maillard reaction enhances the reducing ability, DPPH radical scavenging ability, and Fe2+ chelating ability of PPH. Therefore, the Maillard reaction product of peanut protein can be expected to be used as a substitute for salt seasoning, with excellent antioxidant properties.

The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities was devised, ensuring precise halts at designated elevations for seamless harvesting operations. The effectiveness of the platform parameter configuration and the reasonableness of the posture changes were verified. Firstly, the planting mode and growth characteristics of Yunnan large-leaf tea trees were analyzed to determine the preset path, posture changes, and mechanism stroke of the Stewart parallel lifting platform, thereby determining the basic design specifications of the platform. Secondly, a 3D model was established using SolidWorks, a robust adaptive PD control model was built using MATLAB for simulation, and dynamic calculations were carried out through data interaction in Simulink and ADAMS. Finally, the rationality of the lifting platform design requirements was determined based on simulation data, a 6-DOF Stewart parallel lifting platform was manufactured, and a motion control system was built for experimental verification according to the design specifications and simulation data. The results showed that the maximum deviation angle around the X, Y, and Z axes was 10°, the maximum lifting distance was 15 cm, the maximum load capacity was 60 kg, the platform response error was within ±0.1 mm, and the stable motion characteristics reached below the millimeter level, which can meet the requirements of automated operation of the auxiliary picking robotic arm.

Abstract To deal with intra‐abdominal sepsis, one of the major global causes of death in hospitalized patients, efficient abscess drainage is crucial. Despite decades of advances, traditional catheters have demonstrated poor drainage and absorption properties due to their simple tubular structures and their dense nonporous surface. Herein, inspired by porous sponges and fractal roots, a multifaceted hydrogel catheter with effective drainage, absorptive, and robust properties, is presented. Its unique fractal structures provide extensive internal branching and a high specific surface area for effective drainage, while the hierarchical porous structures provide a wide range of absorption capabilities. Additionally, its distinctive multi‐interpenetration network maintains robust and appropriate mechanical properties, even after absorption multiple times of liquid and mechanical disturbance, allowing for intact removal from the abdominal cavity without harm to the animal in vivo. Besides, the loaded antimicrobial peptides are capable of being released in situ to inhibit the potential for infections. In vivo experiments have demonstrated that this hydrogel catheter efficiently removes lethal abscesses and improves survival. It is believed that this innovative and practical catheter will create a future precedent for hydrogel drainage devices for more effective management of intra‐abdominal sepsis.

Abstract Introduction Nearly one in seven women worldwide suffers from chronic pelvic pain syndrome (CPPS) each year. Often, CPPS necessitates a combination of treatments. Studies have shown the good therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) upon CPPS. We wish to undertake a randomized controlled trial (RCT) to observe the effect of high-frequency rTMS at different intensities upon CPPS. Methods and analyses In this prospective, double-blinded RCT, 63 female CPPS participants will be recruited and randomized (1:1:1) to high-intensity rTMS, low-intensity rTMS, or sham rTMS. The control group will receive a 10-day course of conventional pelvic floor (PF) rehabilitation (neuromuscular stimulation, magnetic therapy, or light therapy of the PF). On the basis of conventional treatment, participants in the high-intensity rTMS group will receive pulses of 10 Hz with a resting motor threshold (RMT) of 110% for a total of 15,000 pulses. Participants in the low-intensity rTMS group will receive pulses of 10 Hz with an RMT of 80% with 15,000 pulses. The sham rTMS group will be subjected to sham stimulation with the same sound as produced by the real magnetic stimulation coil. The primary outcome will be determined using a visual analog scale, the Genitourinary Pain Index, Zung Self-Rating Anxiety Scale, and Zung Self-Rating Depression Scale. The secondary outcome will be determined by electromyography of the surface of PF muscles at baseline and after treatment completion. Ethics and dissemination This study is approved by the Ethics Committee of Bao’an People’s Hospital, Shenzhen, Guangdong Province (approval number: BYL20211203). The results will be submitted for publication in peer-reviewed journals and disseminated at scientific conferences (Protocol version 1.0-20220709). Trial registration Chictr.org.cn, ID: ChiCTR2200055615. Registered on 14 January 2022, http://www.chictr.org.cn/showproj.aspx?proj=146720 . Protocol version 1.0-20220709.

Neutral leaching can be regarded as the key process of dissolving zinc-calcine in zinc hydro-metallurgy to obtain the zinc-electrolyte, and the variation of external environments and disturbances affect the operation states of the neutral leaching process.To this end, a mechanism and data knowledge-driven process monitoring method for neutral leaching in zinc hydro-metallurgical was proposed.This method firstly started from the physical-chemical reaction mechanism and process mechanism of the neutral leaching process, which can be used to excavate the correlation between the mechanical parameters and the monitoring variables to realize the knowledge-driven selection of key monitoring variables.Secondly, the trend change characteristics of the first-order and second-order key variables were combined to realize the data-driven process monitoring.Finally, this proposed method was applied to the monitoring of the practical neutral leaching process.The results show that this method can effectively realize the monitoring of the zinc hydro-metallurgy neutral leaching process, which can be used to improve the process stability of the neutral leaching process.

Abstract Checkpoint suppressor 1 (CHES1), a transcriptional regulator, had been dysregulated in many types of malignancies including breast cancer, and its expression level is strongly associated with progression and prognosis of patients. However, the underlying regulatory mechanisms of CHES1 expression in the breast cancer and the effects of post-translational modifications (PTMs) on its functional performance remain to be fully investigated. Herein, we found that CHES1 had a high abundance in triple-negative breast cancer (TNBC) and its expression was tightly associated with malignant phenotype and poor outcomes of patients. Furthermore, we confirmed that CHES1 was an acetylated protein and its dynamic modification was mediated by p300 and HDAC1, and CHES1 acetylation enhanced its stability via decreasing its ubiquitination and degradation, which resulted in the high abundance of CHES1 in TNBC. RNA-seq and functional study revealed that CHES1 facilitated the activation of oncogenic genes and pathways leading to proliferation and metastasis of TNBC. Taken together, this research established a novel regulatory role of acetylation on the stability and activity of CHES1. The results demonstrate the significance of CHES1 acetylation and underlying mechanisms in the progression of TNBC, offering new potential candidate for molecular-targeted therapy in breast cancer.

The raw material supply chain is the premise of safe and stable production with an important position.As the front link of the supply chain, order allocation is the focus of enterprises.Due to the complex composition of raw materials, large qualitative differences in suppliers, and information coupling in the metallurgical industry, the current expert decision-making model is labor-intensive and difficult to deal with such complex order allocation problems, resulting in low decision-making efficiency, high procurement costs, and difficult to guarantee the quality of raw materials.Aiming at this problem, a knowledge-driven order allocation method for the raw material supply chain in metallurgical enterprises was proposed.First, on the basis of a multi-level supplier evaluation system, the entropy weight method and the fuzzy analytic hierarchy process were adopted to make full use of data knowledge and experience knowledge, and compatibility degree and different degree were introduced to reasonably allocate the importance of each evaluation index.Then, a multi-attribute decision-making evaluation model was built based on the technique for order preference by similarity to an ideal solution (TOPSIS) to automatically obtain the comprehensive performance and ranking of suppliers, so as to realize the efficient evaluation and management of suppliers.Finally, a multi-objective order quantity allocation model was established by comprehensively considering the supplier characteristics knowledge, ore blending mechanism knowledge, business status knowledge, etc., and the optimal order quantity of the each supplier under complex resource constraints was automatically obtained.Taking a domestic zinc smelting enterprise as an example, the validity and applicability of the proposed method are verified by the relevant data of the raw material supply chain.The results show that the proposed method can automatically complete knowledge-based work such as supplier evaluation and order quantity allocation, which will greatly liberate manual labor, improve decision-making efficiency, effectively reduce procurement cost and improve the quality of raw materials.

Post-translational modification (PTM) has a significant impact on cellular signaling and function regulation. In pancreatic β cells, PTMs are involved in insulin secretion, cell development, and viability. The dysregulation of PTM in β cells is clinically associated with the development of diabetes mellitus. Here, we summarized current findings on major PTMs occurring in β cells and their roles in insulin secretion. Our work provides comprehensive insight into understanding the mechanisms of insulin secretion and potential therapeutic targets for diabetes from the perspective of protein PTMs.

Due to the long-term and irrational use of antibiotics for the prevention and control of bacterial diseases in aquaculture, antibiotic resistance genes have become a new source of pollution in aquatic products. Factors such as the spread of drug-resistant strains and the horizontal transfer of drug-resistant genes have led to multi-drug resistance in fish-infecting bacteria, which seriously affects the quality and safety of aquatic products. In this study, 50 samples of horse mackerel and puffer fish sold in Dalian aquatic products market and seafood supermarket were collected, and the phenotypic characteristics of the bacteria carried by the fish for drugs such as sulfonamides, amide alcohols, quinolones, aminoglycosides and tetracyclines were tested and analyzed, and the resistance genes carried by fish samples were detected by SYBG qPCR. Our statistical analyses demonstrated that the drug resistance phenotypes and genotypes of bacteria carried by mariculture horse mackerel and puffer fish in the Dalian area of China were complex, and the multi-drug resistance rate reached 80%. Among the examined antibiotics, the resistance rates to cotrimoxazole, tetracycline, chloramphenicol, ciprofloxacin, norfloxacin, levofloxacin, kanamycin, and florfenicol exceeded 50%, whereas the resistance rates to gentamicin and tobramycin were 26 and 16%, respectively. The detection rate of the drug resistance genes tetA, sul1, sul2, qnrA, qnrS, and floR exceeded 70% and all samples carried more than three drug resistance genes. The correlation analysis of drug resistance genes and drug resistance phenotypes showed that the detection of the drug resistance genes sul1, sul2, floR, and qnrD was correlated with the detection of drug resistance phenotypes (p 0.05). In general, our findings indicated that the multi-drug resistance of bacteria carried by marine horse mackerel and puffer fish in the Dalian area was serious. From the perspective of drug resistance rate and drug resistance gene detection rate, the aminoglycosides gentamicin and tobramycin are still considered effective in controlling bacterial infection in marine fish in the study area. Collectively, our findings provide a scientific basis for the management of drug use in mariculture, which can prevent the transmission of drug resistance through the food chain and minimize the associated human health risks.

The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment’s results, a database was established for the identification of coal–rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal–rock interface.

This paper proposes a dynamic-projection-integrated particle-filtering-based identification strategy for the friction characteristic curve of a train wheelset under the slipping fault condition. This strategy aims to achieve the identification of the fault friction characteristic curve (FFCC) in the early slipping fault stage. First, a multi-dimensional integrated particle-filtering (MDIPF)-based parameters correction method is proposed. The MDIPF constructs an error particle state transition model encompassing multi-dimensional parameters, which integrates inter-particle correlation to facilitate error fusion during the state transition process. Then, a dynamic projection domain (DPD)-based particle refinement method is proposed. The DPD constructed the contraction factors to dynamically fine-tune the particle projection domain. Finally, a multi-level evaluation-based identification method for the FFCC is proposed. And the dynamic-projection-integrated particle-filtering-based identification strategy is validated, which can actualize the rapid and accurate identification of the FFCC.

Interleukin-22 (IL-22) has been demonstrated as a critical regulator of epithelial homeostasis and repair; it showed an anti-inflammatory effect against ulcerative colitis. Local microinjection of IL-22 cDNA vector has been shown to be effective in treating ulcerative colitis in mouse models. However, microinjection comes with multiple technical challenges for routine colon-targeted drug delivery. In contrast, oral administration can get around these challenges and provide comparable efficacy. We showed in previous studies that oral administration of new lipid nanoparticles (nLNP)-encapsulated IL-22 mRNA targets the colon region and efficiently ameliorates colitis. This protocol describes the details of preparing and characterizing the nLNP-encapsulated IL-22 mRNA using three major lipids that mimic the natural ginger-derived nanoparticles. It provides an nLNP platform that can be used to orally deliver other types of nucleic acids to the colon.

IntroductionTo unearth superior countermeasures that improve psychological health and upgrade the quality of employment for medical students in China in post-epidemic era, this study was designed to determine the possible factors affecting psychological status and future career choice of this population.MethodsA cross-sectional observational study was carried out. Depression Anxiety Stress Scale-21 (DASS-21) and Insomnia Severity Index (ISI) were applied to measure psychological state. Chi-square and logistic regression analyses were adopted to filtrate related factors for psychological health and employment intention.ResultsA total of 936 medical students, including 522 from eastern universities and 414 from western universities, were enrolled in the study. Anxiety among students in China's western universities was higher than that in China's eastern universities (30.4% vs. 22.0%), but no differences in the occurrences of stress (11.4% vs. 13.4%), depression (28.7% vs. 24.5%) and insomnia (30.7% vs. 25.7%). Grades, academic ranking, household income, attitudes about COVID-19 were associated with the occurrence of psychological problems. In addition, major, education level, academic ranking, family income, and clinical experience may affect the choice of future employment location and employment income. Notably, household income affected by COVID-19 and the perception of epidemic prevention and control resulted in changes in future employment region and income. COVID-19 can lead medical students with psychological problems to have a negative attitude toward future employment. Encouragingly, multiple activities, namely, proactive consideration of employment, taking part in career planning training lectures and timely adjustment of career planning, were beneficial to the professional identity of medical students.ConclusionThis study suggests that medical student psychology is influenced by COVID-19 and academic and financial pressures; actively coping with COVID-19 and making career planning in advance will contribute to optimizing future employment. Our findings provide a potent guideline for relevant departments to accurately adjust job deployment and for medical students to actively choose a career in the future.

Burkholderia contaminans, an emerging pathogen related to cystic fibrosis, is known to cause potentially fatal infections in humans and ruminants, especially in immunocompromised individuals. However, the immune responses in cows following its infection have not been fully elucidated. In this study, T- and B-lymphocytes-mediated immune responses were evaluated in 15 B. contaminans-induced mastitis cows and 15 healthy cows with multi-parameter flow cytometry. The results showed that infection with B. contaminans was associated with a significant decrease in the number and percentage of B lymphocytes but with a significant increase in the proportion of IgG+CD27+ B lymphocytes. This indicated that humoral immune response may not be adequate to fight intracellular infection, which could contribute to the persistent bacterial infection. In addition, B. contaminans infection induced significant increase of γδ T cells and double positive (DP) CD4+CD8+ T cells but not CD4+ or CD8+ (single positive) T cells in blood. Phenotypic analysis showed that the percentages of activated WC1+ γδ T cells in peripheral blood were increased in the B. contaminans infected cows. Interestingly, intracellular cytokine staining showed that cattle naturally infected with B. contaminans exhibited multifunctional TNF-α+IFN-γ+IL-2+B. contaminans-specific DP T cells. Our results, for the first time, revealed a potential role of IgG+CD27+ B cells, CD4+CD8+ T cells and WC1+ γδ T cells in the defense of B. contaminans-induced mastitis in cows.

Objective To explore the application effect of target management combined with intelligent display system on apheresis platelet donor recruitment. Methods Control group: 317 apheresis platelet donors were recruited according to the conventional appointment mode of intelligent display system from October 2020 to March 2021, with a total of 1 073 donations. Experimental group: 404 apheresis platelet donors were recruited using quantitative target management plus the intelligent display system from April 2021 to September 2021, with a total of 1 308 donations. The number of first-time donors, repeated donors, recalled donors after first-time donation, and their corresponding donations, as well as the double-dose collection rate, and the transfer-in/-out of platelet product were analyzed and compared. Results The number of first-time donors increased from 89 (28.08%) to 179 (44.31%) while repeated donors decreased from 228 (71.92%) to 225 (55.69%), all P0.05.The cumulative number of first-time and repeated platelet donors increased from 149 (13.89%) to 331 (25.31%), and 924 (86.11%) to 977 (74.69%), respectively(all P0.05). The double-dose collection rate decreased from 24.70% (265/1 073) to 13.46% (176/1 308)(P

This article aimed to improve the utilization of by-products of tofu and produce high-quality tofu, on the basis of the production process of acid slurry tofu, bean dregs and transglutaminase (TG enzyme) were added to it. The soybean-water ratio, soybean dregs addition, acid slurry addition and transglutaminase addition were chosen as four factors, sensory score was used as evaluation index to optimize the preparation process of high-fiber acid slurry tofu, and the product was compared with the traditional acid slurry tofu, the dietary fiber content, sensory evaluation, texture characteristics, protein structure and microstructure of the two were analyzed. Results showed that, the optimal process results were as follows: Soybean-water ratio 1:4.6, 15% soybean dregs addition, 21% acid slurry addition, 0.3% transglutaminase addition. The order of influence on the sensory score of high-fiber acid slurry tofu was: Soybean dregs addition amount> transglutaminase addition amount> acid slurry addition amount> soybean-water ratio. Under this condition, the dietary fiber content of high-fiber acid slurry tofu increased from 0.26% to 2.15%. The texture characteristics were similar to traditional one, texture was not hardened due to the increasing of dietary fiber, flavor was strong, internal tissue was uniform and flexible. From FTIR spectrum, it could be seen that transglutaminase enhanced the cross-linking between protein and protein, and the protein gel structure was more compact. The microstructure was more dense and orderly.

Abstract Background Ulcerative colitis (UC), a subtype of inflammatory bowel disease (IBD), has evolved into a global burden given its high incidence. There is a clinical need to create better diagnostic and therapeutic approaches to UC. Results We fabricated P-selectin binding peptide-decorated poly lactic-co-glycolic acid (PBP-PLGA-NP) doped with two lipophilic dyes, DiL and DiD. Meanwhile, two low-toxic anti-inflammatory natural products (betulinic acid [BA] and resveratrol [Res]) were co-loaded in the PBP-PLGA-NP system. The BA/Res-loaded NPs had an average size of around 164.18 nm with a negative zeta potential (− 25.46 mV). Entrapment efficiencies of BA and Res were 74.54% and 52.33%, respectively, and presented a sustained drug release profile. Further, the resulting PBP-PLGA-NP could be internalized by RAW 264.7 cells and Colon-26 cells efficiently in vitro and preferentially localized to the inflamed colon. When intravenously injected with luminol, MPO-dependent bioluminescence imaging to visualize tissue inflammation was activated by the bioluminescence and fluorescence resonance energy transfer (BRET-FRET) effect. Importantly, injected NPs could remarkably alleviate UC symptoms yet maintain intestinal microbiota homeostasis without inducing organ injuries in the mice models of colitis. Conclusions This theranostic nano-platform not only serves as a therapeutic system for UC but also as a non-invasive and highly-sensitive approach for accurately visualizing inflammation. Graphical Abstract

To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease, is known to increase the risk of colitis-associated cancer (CAC). CAC has been found to be unresponsive to standard chemotherapy regimens, and the current treatments do not utilize effective small-molecule drugs and colon-targeted delivery systems. Previous studies indicated that the M13–nano-liposome (NL) formulation can effectively target the colon and reshape the gut microbiota in ex vivo cultures, generating altered microbial metabolites that can efficiently prevent chronic UC. In this study, we tested the cancer cell uptake ability of the NL formulation and investigated the potential of the M13–NL formulation to prevent CAC in the azoxymethane (AOM)-exposed IL10−/− mouse model. Our findings demonstrate that oral administration of M13–NL prevents tumor development in AOM-exposed IL10−/− mice, suggesting that M13–NL is a promising oral drug formulation for preventing CAC.

Abstract This paper proposes an equivalent temperature estimation‐based finite control set model predictive control (ETE‐FCS‐MPC) method to balance the thermal stress among transistors in three level neutral point clamp (3L‐NPC) traction inverters, which can be used to prolong the whole service life and improve the reliability of the traction inverter. Compared with other methods, the proposed ETE‐FCS‐MPC method requires lesser calculation without additional sensor. Firstly, an energy loss‐based equivalent temperature estimation method is proposed to avoid the complexity of junction temperature prediction. Secondly, an energy variance‐based thermal stress balance control objective is introduced. Then, a normalized evaluation function is established to select the optimal switching state. Finally, the effectiveness of this method is verified on hardware‐in‐the‐loop tests.

Abstract Introduction The analgesic efficacy of magnesium sulphate added to bupivacaine for arthroscopy remains controversial. We conduct a systematic review and meta-analysis to explore the efficacy of magnesium sulphate in combination with bupivacaine for arthroscopy. Methods We searched PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through July 2020 for randomized controlled trials (RCTs) assessing the effect of magnesium sulphate plus bupivacaine versus bupivacaine for arthroscopy. This meta-analysis is performed using the random-effect model. Results Six RCTs were included in the meta-analysis. Overall, compared with bupivacaine for arthroscopy, combination analgesia using magnesium plus bupivacaine was associated with significantly prolonged duration of analgesia (SMD=0.93; 95% CI=0.27 to 1.60; P=0.006) and first time to analgesic requirement (SMD=196.57; 95% CI=13.90 to 379.24; P=0.03), reduced pain scores (SMD=-1.71; 95% CI=-2.96 to -0.46; P=0.007) and analgesic consumption (SMD=-1.04; 95% CI=-1.49 to -0.60; P

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.

Background: Among critically ill patients in the intensive care unit (ICU), sepsis is an urgent global public health problem due to its high incidence, high mortality rate and complex pathogenesis. Objective: This study was to evaluate the predictive value of neutrophil-to-lymphocyte ratio (NLR), and neutrophil-to-lymphocyte and platelet ratio (NLPR) in-hospital mortality in septic patients on days 1, 3 and 5 in ICU. Methods: The data of septic patients admitted to the ICU of the Sixth Affiliated Hospital of Sun Yat-sen University from March, 2018 to July, 2019 were collected. NLR and NLPR were calculated and multivariate logistic regression analysis was performed to identify the relationship between them and in-hospital mortality, respectively. Receiver operating characteristic curve (ROC) was used to determine the efficacy and optimal cutoff value of diagnostic tests. Results: A total of 173 septic patients were included in this analysis, including 108 cases in the survival group and 65 in the death group, with a total mortality rate of 37.6%. A multivariate logistic regression analysis showed that NLR on day 5 was independently correlated with in-hospital mortality rate (OR 1.041, 95% CI: 1.008–1.074), and Day 5 NLPR was also independently associated with in-hospital mortality rate (OR 1.020, 95% CI: 1.001–1.040). The areas under the receiver operating characteristic curve (AUC) of the NLR on days 1 and 3 was 0.513 and 0.542 respectively, and the optimal cutoff value were 23.16 and 15.48, and the AUC of the NLR on day 5 was 0.589, and the best cutoff value was 15.85. The AUC of NLPR on day 1 and 3 was 0.517 and 0.547, respectively, and the optimal cutoff value was 10.25 and 18.47. The AUC of NLPR on day 5 was the largest, 0.654, and the optimal cutoff value was 8.22. After combined NLPR on day 5 with age and sequential organ failure assessment (SOFA) scores, the AUC increase to 0.718. Among the joint predictors, the optimal cutoff value for NLPR on day 5 was 9.31. Conclusion: We found that Day 5 NLPR and NLR were independently correlated with in-hospital mortality. Day 5 NLPR Combined with age and SOFA scores may be help predict mortality in ICU septic hospitalized patients.

BackgroundOsteosarcoma is a common bone sarcoma that occurs in childhood and adolescence. Although research on non-coding RNAs (ncRNAs) of osteosarcoma has been developed rapidly in recent years, a specific bibliometric analysis on this topic has not yet been performed. The bibliometric analysis aims to summarize knowledge atlas, research hotspots, and emerging trends and to provide researchers with new perspectives in further studies.MethodsAll publications regarding ncRNAs of osteosarcoma published from 2000 to 2021 were retrieved from the Web of Science Core Collection. Quantitative indicators including the number of publications and citations, H-index, and journal citation reports were analyzed by using Excel 2019 and R software. VOSviewer and CiteSpace were used to analyze the cooperation among countries/institutions/journals/authors and the co-occurrence of keywords, keywords bursts, and references.ResultsA total of 3206 publications were extracted. A significant growth trend in the annual number of publications over the past 22 years is revealed (R2 = 0.999). The most prolific country and institution were China (2260) and Shanghai Jiao Tong University (134), respectively. Professors Wang W and Liu W contributed the most to this field. The keywords were stratified into six clusters: Cluster 1 (apoptosis and growth), Cluster 2 (cancer and progression), Cluster 3 (microRNAs and downregulation), Cluster 4 (genes and differentiation), Cluster 5 (expression and biological functions), and Cluster 6 (metastasis). The long non-coding RNAs and circular RNAs have been considered as an important research hotspot in the near future.ConclusionThis study offers a scientific perspective on ncRNAs of osteosarcoma and provides researchers with valuable information to understand the knowledge structure and to identify emerging trends in this field.

The development of an exceptionally sensitive diagnostic technique for early identification of aquaculture diseases, specifically Aeromonas hydrophila, is essential for efficient management of disease outbreaks at aquaculture locations. In this research, a swift and sensitive diagnostic assay employing Loop-mediated isothermal amplification (LAMP) of Aeromonas hydrophila was devised and compared to the conventional qPCR method documented by Rong Wang. Validation of the diagnostic assay was carried out using actual samples obtained from aquaculture fish. The findings revealed that based on the rapid detection of crude bacterial genomic DNA, the fluorescent LAMP assay possessed a lower limit of detection (LOD) of 0.559 ng/μL (0.315–1.693, 95% CI), while the LOD for qPCR stood at 4.301 ng/μL (2.084–8.876, 95% CI). Both techniques demonstrated outstanding specificity, exhibiting no cross-reactivity with bacteria from the same or closely related genera. A total of 74 fish samples suspected to be infected with the fish disease were gathered, with 26 and 23 samples testing positive for Aeromonas hydrophila via LAMP and qPCR, respectively. The concordance analysis for LAMP and qPCR methods generated a Kappa value of 0.909 (0.778–1.000, 95% CI), signifying a high degree of diagnostic consensus. This study highlights that the LAMP assay eliminates the thermal cycle temperature change process of qPCR, uses lysate to crudely extract bacterial genomic DNA, and can complete the detection within 40 min, rendering it a practical and efficient alternative for monitoring disease outbreaks at aquaculture sites.

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

PCR is indispensable in basic science and biotechnology for in-orbit life science research. However, manpower and resources are limited in space. To address the constraints of in-orbit PCR, we proposed an oscillatory-flow PCR technique based on biaxial centrifugation. Oscillatory-flow PCR remarkably reduces the power requirements of the PCR process and has a relatively high ramp rate. A microfluidic chip that could perform dispensing, volume correction, and oscillatory-flow PCR of four samples simultaneously using biaxial centrifugation was designed. An automatic biaxial centrifugation device was designed and assembled to validate the biaxial centrifugation oscillatory-flow PCR. Simulation analysis and experimental tests indicated that the device could perform fully automated PCR amplification of four samples in one hour, with a ramp rate of 4.4 ∘C/s and average power consumption of less than 30 W. The PCR results were consistent with those obtained using conventional PCR equipment. Air bubbles generated during amplification were removed by oscillation. The chip and device realized a low-power, miniaturized, and fast PCR method under microgravity conditions, indicating good space application prospects and potential for higher throughput and extension to qPCR.

Xiong’an New Area is defined as the future city of China, and the regulation of water resources is an important part of the scientific development of the city. Baiyang Lake, the main supplying water for the city, is selected as the study area, and the water quality extraction of four typical river sections is taken as the research objective. The GaiaSky-mini2-VN hyperspectral imaging system was executed on the UAV to obtain the river hyperspectral data for four winter periods. Synchronously, water samples of COD, PI, AN, TP, and TN were collected on the ground, and the in situ data under the same coordinate were obtained. A total of 2 algorithms of band difference and band ratio are established, and the relatively optimal model is obtained based on 18 spectral transformations. The conclusion of the strength of water quality parameters’ content along the four regions is obtained. This study revealed four types of river self-purification, namely, uniform type, enhanced type, jitter type, and weakened type, which provided the scientific basis for water source traceability evaluation, water pollution source area analysis, and water environment comprehensive treatment.

Nanotechnology has the potential to revolutionize the field of drug treatment by enabling the targeted delivery and controlled release of drugs at a cellular level [...]

Y chromosomal genetic markers in the non-recombining region are commonly used for human evolution research, familial searching, and forensic male differentiation since they strictly follow paternal inheritance. Y chromosomal short tandem repeats (Y-STRs) possess extraordinarily advantages in forensic applications because of their high polymorphisms and special genetic pattern. Here, we assessed the genetic diversities of 41 Y-STRs and three Y chromosomal insertion/deletion (Y-InDels) loci in the Chinese Inner Mongolia Han population; besides, genetic differentiation analyses among the studied Han population and other previously reported populations were conducted based on 27 same Y-STRs. Totally, 425 alleles were observed in 324 Inner Mongolia Han individuals for these Y-markers. Gene diversities of these Y-markers distributed from 0.0306 to 0.9634. The haplotype diversity and discriminatory capacity of these Y-markers in the Inner Mongolia Han population were 0.9999 and 0.98457, respectively. Haplotype resolution comparisons of different Y-marker groups in the studied Han population revealed that higher haplotype resolution could be achieved for these 44 Y-markers. Population genetic analyses of the Inner Mongolia Han population and other reference populations demonstrated that the studied Han population had relatively closer genetic affinities with Northern Han Chinese populations than Southern Han and other minority groups. To sum up, these 44 Y-markers can be utilized as a valuable tool for male differentiation in the Inner Mongolia Han population.

Quantum dot infrared photodetectors (QDIP) have a weak ability to capture light, which limits the further improvement of absorptivity to a certain extent. Since the localized surface plasmon resonance (LSPR) can effectively couple the optical radiation energy and result in a significant field enhancement effect in the near-field range, it is introduced to improve the absorptivity of the QDIP. Concretely, the strip metal grating structure is coupled to the active region of the traditional QDIP, and a metal reflective layer is added at the bottom of the QDIP to improve the absorptivity by using the metal–semiconductor–metal structure. The simulation results show that the addition of the optimized strip metal grating structure can make the photon absorptivity of QDIP reach more than 90% in both frequency bands of 37.5 and 48.5 THz, and the coupling characteristics of LSPR are studied by the analysis of the electric field distribution of QDIP, which can provide the theoretical guidance for the combination of metal grating and traditional QDIP.

Deep learning is an artificial intelligence technique developed in recent years.Compared with traditional shallow models,deep learning has a strong ability of feature representation and function fitting.It can extract hierarchical features from massive data,which has great potential for data-driven modeling in process industries.Firstly,the history of deep learning was introduced.Then,four widely used deep networks and their applications were introduced in data-driven modeling of process industries.At last,the conclusions about deep learning and its applications in process in dustries were given.

Inspired by the empirical mode decomposition (EMD)-enhanced gas detection work, this paper develops a further improved signal reconstruction method (namely EWT-ASG) for the demodulated harmonics of tunable diode laser absorption spectroscopy (TDLAS), which is mainly based on empirical wavelet transform (EWT) and Savitzky-Golay (S-G) filtering. First, the imported EWT performs better on the decomposition precision as it successfully bypasses the mode aliasing problem of EMD resulting by the lack of mathematical basis. Second, the improved S-G filter effectively suppresses the noisy components of the wavelet coefficients by updating its one key parameter (i.e., window size w) dynamically according to the correlation coefficients between the raw signals and the decomposed wavelet coefficients. The EWT-ASG scheme was first applied on the oxygen concentration detection for pharmaceutical glass vials. The preliminary experimental results indicate that the EWT-ASG method performs better than recent state-of-the-arts, with an average correct discrimination rate of 98.14% when the normalized SNR is 1. Even when the normalized SNR is degenerated from 1 to 0.85, our detection system still survived well, with a highest average correct discrimination rate of 90.45%. The detection system precision is also improved to a large extent, with a minimal Allan deviation of 0.856@(117s).

Commodity prices are important factors for investment management and policy-making, and price forecasting can help in making better business decisions. Due to the complex and volatile nature of the market, commodity prices tend to change frequently and fluctuate violently, often influenced by many potential factors with strong nonstationary and nonlinear characteristics. Thus, it is difficult to obtain satisfactory prediction effects by only using the historical data of prices individually. To address this problem, a novel dynamic price forecasting method based on multi-factor selection and fusion with CNN-LSTM is proposed. First, the factors related to commodity price are collected, and Granger causality inference is used to identify causal factors that affect the commodity price. Then, XGBoost is used to evaluate the importance of the remaining factors and screen out critical factors to reduce the interference of redundant information. Due to the high amount and complicated changes of the selected factors, a convolutional neural network is employed to fuse the selected factors and extract the hidden features. Finally, a long short-term memory network is adopted to establish a multi-input predictor to obtain the dynamic price. Compared with several advanced approaches, the evaluation results indicate that the proposed method has an excellent performance in dynamic price forecasting.

In order to solve the problem of an accurate recognition of tea picking through tea picking robots, an edge device detection method is proposed in this paper based on ShuffleNetv2-YOLOv5-Lite-E for tea with one bud and two leaves. This replaces the original feature extraction network by removing the Focus layer and using the ShuffleNetv2 algorithm, followed by a channel pruning of YOLOv5 at the neck layer head, thus achieving the purpose of reducing the model size. The results show that the size of the improved generated weight file is 27% of that of the original YOLOv5 model, and the mAP value of ShuffleNetv2-YOLOv5-Lite-E is 97.43% and 94.52% on the pc and edge device respectively, which are 1.32% and 1.75% lower compared to that of the original YOLOv5 model. The detection speeds of ShuffleNetv2-YOLOv5-Lite-E, YOLOv5, YOLOv4, and YOLOv3 were 8.6 fps, 2.7 fps, 3.2 fps, and 3.4 fps respectively after importing the models into an edge device, and the improved YOLOv5 detection speed was 3.2 times faster than that of the original YOLOv5 model. Through the detection method, the size of the original YOLOv5 model is effectively reduced while essentially ensuring recognition accuracy. The detection speed is also significantly improved, which is conducive to the realization of intelligent and accurate picking for future tea gardens, laying a solid foundation for the realization of tea picking robots.