Good Effect Research Articles

Research on Mushroom Image Classification Algorithm Based on Deep Sparse Dictionary Learning

The traditional mushroom feature extraction method has low classification efficiency and unsatisfactory effect. Dictionary learning is widely used in image classification. However, the previous work is to learn dictionaries in the original space, which limits the performance of sparse representation classification. In order to solve the problem of spatial redundancy in traditional convolutional neural networks and the weak performance of deep learning in small samples, an improved dictionary learning algorithm, Deep Sparse Dictionary learning (DSDL), is proposed. The input to DSDL is not a matrix gathered from the original grayscale image or a hand-created feature, but rather a relatively deeper feature extraction via a stack autoencoder. Then, a structured dictionary is designed to reconstruct the deep features according to different categories of distinguishing features. In addition, it is necessary to learn the associated structured projection sparse dictionary to ensure that the decoder updates in the direction of the deconvolution operator error is minimal. By utilizing sparse dictionary learning loss functions and autoencoder loss functions, DSDL can simultaneously learn deep latent features and corresponding dictionary pairs. In the testing phase of DSDL, the minimum errors of deep feature and structured projection components for different classes can be directly represented by basic matrix multiplication operations. Experimental results show that the proposed method achieves a good classification effect on mushroom images, which shows the effectiveness of the method.

Neoadjuvant anlotinib combined with penpulimab for resectable locally advanced esophageal squamous cell carcinoma (ESCC): A prospective, single-arm, phase II trial.

352 Background: Neoadjuvant chemoradiation therapy followed by radical surgery has been extensively recommended for locally advanced esophageal squamous cell carcinoma (ESCC). However, toxicities and challenges to the subsequent surgery limit its wide clinical application. Immune checkpoint inhibitors combined with antiangiogenesis have synergistic effects and have shown good therapeutic effects in previous studies, as well as fewer side effects. Here, we tested a novel combination of anlotinib (an antiangiogenic kinase inhibitor) and penpulimab (an anti-PD-1 antibody), without the use of radio- or chemotherapy, for the neoadjuvant therapy of locally advanced ESCC. Methods: In this prospective, single-arm, phase II clinical trial (ChiCTR2200064848), patients(pts) with resectable locally advanced ESCC (clinical stage III-IVa) were recruited. The study was designed using a two-stage minimax approach. The estimated sample size was 40 pts would provide 80% power at a one-sided alpha error of 5%, threshold pathological complete response (pCR) of 16% and expected pCR of 30%. Here, the first-stage data was included. Notably, 85% (22/25) enrolled pts had medical contraindications (including cardiopulmonary diseases or other underlying medical conditions, along with poor nutritional status and so on). Pts received neoadjuvant therapy with anlotinib (8 mg, po, D1-14, Q3W) combined with penpulimab (200 mg, ivgtt, Q3W) for 2 cycles. Six to eight weeks later, pts underwent radical surgery after exclusion of contraindications. The primary endpoint was pathologic complete response (pCR) rate. Secondary endpoints included tumor regression grade (TRG), R0 resection rate, objective response rate (ORR) and safety profile. Results: From Jan 2023 to Sep 2023, 25 pts (19 males and 6 females) were enrolled with a median age of 67 years (range: 50-74). All pts were available for efficacy assessment and 16 were evaluable for the primary end point. The rest of the pts are waiting for surgery. As of 15 Sep 2023, 19 pts underwent esophagectomy and R0 resection rate was 87.5%. The pCR rate was 18.8% (3/16) and 31.3% of pts had a major pathologic response (MPR). The ORR and DCR were 48.0% and 96.0%, respectively. 84.0% of pts experienced grade 1-2 treatment-related adverse events (TRAEs), 3 (12.0%) pts experienced grade 3 TRAEs (2 elevated blood pressure and 1 elevation of liver enzymes). The safety profile revealed that the most common TRAEs (≥10%) included hypertension (28.0%), hand-foot syndrome (20%), fatigue (20%), abdominal pain (12%), toothache (12%). Conclusions: The preliminary results of this study show promising safety and efficacy of anlotinib combined with penpulimab as neoadjuvant for pts with ESCC, especially for pts not suitable for neoadjuvant chemoradiation or chemotherapy. More ESCC pts would be recruited in the future. Clinical trial information: ChiCTR2200064848 .

Comparison Of Methods for Predicting Changes in Stock Trading Volume

Stocks occupy an important place in the financial sector, while machine learning, which uses data to solve problems, has been applied in several fields. In this paper, three methods of multivariate linear model, BP neural network and ELMAN neural network are adopted to predict the transaction volume change value of ZTE and compare the evaluation. The study shows that, in the short-term prediction of BP neural network and multiple linear model, the two methods have good prediction effect, the BP neural network is stronger; ELMAN neural network is worse, it is not recommended to predict the trading volume change value.

Effect of Tetrastigma hemsleyanum leaves and Extract supplementation on liver metabolomics and the gut microbiota in alcohol-induced liver injury

Alcoholic liver disease (ALD) is caused by chronic heavy alcohol consumption and is characterized by lipid metabolism disorders, oxidative stress, and hypersecretion of proinflammatory cytokines. Tetrastigma hemsleyanum leaves (THL), a popular functional food in China, have good anti-inflammatory, antioxidant, and liver protection effects. However, whether THL extracts improve alcoholic liver disease and the underlying mechanism of action have not been determined. Therefore, in this study, we used a C57BL/6J male mouse model to investigate the effect of THL extract on ALD and to explore the underlying mechanism involved preliminarily. The alleviation of alcohol-induced liver dysfunction and changes in lipid metabolism-related pathways were observed after THL extract administration stimulated fatty acid synthesis and fatty acid β-oxidation by modulating the expression levels of related mRNAs and genes, thereby alleviating lipid accumulation and oxidative stress in the liver. The composition and abundance of the gut microbiota in mice also underwent alterations. Collectively, these findings suggest that supplementation with THL extract can mitigate liver damage and reduce hepatic lipid accumulation in mice with alcoholic fatty liver disease. It regulates hepatic metabolism and generates various metabolites associated with diminished lipid buildup, such as Plafibride, LysoPI (20:4(5Z,8Z,11Z,14Z)/0:0), 1,3,7-trimethyluric acid, and phytol. Moreover, it impacts specific gut microbiota in mice with alcoholic fatty liver disease including Bacteroides and other beneficial microorganisms while enhancing the production of short-chain fatty acids to improve the intestinal microenvironment.

Research On Vegetable Replenishment and Pricing Based on LSTM Neural Network Prediction Modeling

Vegetables in fresh food supermarkets face challenges with pricing and replenishment since the majority of vegetable varieties have a short shelf life. Fresh food supermarkets must replenish goods based on sales history and demand. In this paper, the pricing and replenishment decisions of fresh supermarkets are predicted using the random forest and LSTM neural network prediction models. The findings indicate that the prediction model in this paper can have a good predictive effect and that the decisions made by fresh supermarkets regarding pricing and replenishment are related to the historical time series data. This paper's study can assist in resolving price and replenishment issues in fresh food stores, which will have some positive economic effects.

Analysis on early predictors of respiratory depression in patients with glufosinate poisoning

Objective: To investigate the early predictors of respiratory depression in patients with glufosinate poisoning, and provide reference for clinicians to make decisions. Methods: In March 2022, the clinical data of patients with glufosinate poisoning admitted to the intensive care unit of the Affiliated Xiangshan Hospital of Wenzhou Medical University from March 2018 to January 2022 were retrospectively analyzed. The patients were divided into respiratory depression group and non-respiratory depression group according to the occurrence of respiratory depression during hospitalization. The clinical data such as age, gender, past history, intake, initial treatment and laboratory examination were compared between the two groups. Multivariate logistic regression was used to analyze the predictors of respiratory depression in patients with glufosinate poisoning, and its predictive value was analyzed by receiver operating characteristic (ROC) curve. Results: A total of 34 patients with glufosinate poisoning were enrolled, including 13 patients in non-respiratory depression group and 21 patients in respiratory depression group. There were significant differences in intake, blood amylase and bicarbonate radical in arterial blood gas between the two groups (P<0.05). Respiratory depression occurred at 6.5-48.0 h after ingestion, with a median of 15.0 (9.5, 24.0) h. Multivariate logistic regression analysis showed that the intake of glufosinate (OR=1.440, 95%CI: 1.033-2.009, P=0.032) and bicarbonate radical in arterial blood gas (OR=0.199, 95%CI: 0.040-0.994, P=0.049) were predictors of respiratory depression in patients with glufosinate poisoning, and the area under the curve (AUC) of ROC curves were 0.936 and 0.842. The optimal cut-off values were 15.0 g (sensitivity=95.2%, specificity=76.9%) and 17.6 mmol/L (sensitivity=71.4%, specificity=84.6%), respectively. Conclusion: The intake of glufosinate and bicarbonate radical in arterial blood gas have good prediction effects on the occurrence of respiratory depression in patients with glufosinate poisoning.

A biodegradable Zn-5Gd alloy with biomechanical compatibility, cytocompatibility, antibacterial ability, and in vitro and in vivo osteogenesis for orthopedic applications

Zinc (Zn) and some of its alloys are recognized as promising biodegradable implant materials due to their acceptable biocompatibility, facile processability, and moderate degradation rate. Nevertheless, the limited mechanical properties and stability of as-cast Zn alloys hinder their clinical application. In this work, hot-rolled (HR) and hot-extruded (HE) Zn-5 wt.% gadolinium (Zn-5Gd) samples were prepared by casting and respectively combining with hot rolling and hot extrusion for bone-implant applications. Their microstructure evolution, mechanical properties, corrosion behavior, cytotoxicity, antibacterial ability, and in vitro and in vivo osteogenesis were systematically evaluated. The HR and HE Zn-5Gd exhibited significantly improved mechanical properties compared with those of their pure Zn counterparts and the HR Zn-5Gd showed a unique combination of tensile properties with an ultimate tensile strength of ∼311.6 MPa, yield strength of ∼236.5 MPa, and elongation of ∼40.6%, all of which are greater than the mechanical properties required for bone-implant materials. The HR and HE Zn-5Gd showed higher corrosion resistance than their pure Zn counterpart in Hanks’ solution and the HE Zn-5Gd had the lowest corrosion rate of 155 µm/y measured by electrochemical corrosion and degradation rate of 26.9 µm/y measured by immersion testing. The HR and HE Zn-5Gd showed high cytocompatibility toward MC3T3-E1 and MG-63 cells, high antibacterial effects against S. aureus, and better in vitro osteogenic activity than their pure Zn counterparts. Furthermore, the HE Zn-5Gd exhibited better in vivo biocompatibility, osteogenesis, and osteointegration ability than pure Zn and pure Ti. Statement of significanceThis work reports the mechanical properties, corrosion behaviors, cytocompatibility, antibacterial ability, in vitro and in vivo osteogenesis of biodegradable Zn–Gd alloy for bone-implant applications. Our findings demonstrate that the hot-rolled (HR) Zn–5Gd showed a unique combination of tensile properties with an ultimate tensile strength of ∼311.6 MPa, yield strength of ∼236.5 MPa, and elongation of ∼40.6%. The HR and HE Zn-5Gd showed higher corrosion resistance than their pure Zn counterpart in Hanks’ solution. The HR and HE Zn-5Gd showed high cytocompatibility toward MC3T3-E1 and MG-63 cells, good antibacterial effects against S. aureus, and better in vitro osteogenic activity. Furthermore, the HE Zn-5Gd exhibited better in vivo biocompatibility, osteogenesis, and osteointegration ability than pure Zn and pure Ti.

Synthesis of tetrazole hybridized with thiazole, thiophene or thiadiazole derivatives, molecular modelling and antimicrobial activity

BackgroundTetrazole-based derivatives and their electronic structures have displayed interesting antimicrobial activity. MethodsThe tetrazole-based hybrids linked with thiazole, thiophene and thiadiazole ring systems have been synthesized through various chemical reactions. The computational method DFT/B3LYP has been utilized to calculate their electronic properties. The antimicrobial effectiveness was investigated against representative bacterial and fungal strains. Additionally, the synthesized derivatives binding interaction was stimulated by docking program against PDB ID: 4URO as a model of the ATP binding domain of S. aureus DNA Gyrase subunit B. ResultsThe structures of the synthesized tetrazole-based derivatives were confirmed by IR, NMR, and Mass spectroscopic data. The DFT/B3LYP method showed that the thiadiazole derivatives 9a–c had lower ΔEH-L than the thiophenes 7a–c and thiazoles 5a–c. The hybrids 5b, 5c, and 7b exhibited proper antibacterial activity against Gram’s +ve bacterial strains (S. aureus and S. pneumonia), while 9a displayed potent activity towards Gram’s -ve bacterial strains (S. typhimurium and E. coli). Meanwhile, derivatives 5a-b, 7a, 7c, and 9c showed good effectiveness towards fungal strain (C. albicans). ConclusionThe study provides valuable tetrazole core-linked heterocyclic rings and opens the door to further research on their electrical characteristics and applications. Tetrazoles and thiazoles have antibacterial properties in pharmacological frameworks, making these hybrids potential lead molecules for drug development. The conclusion summarizes the data and suggests that the synthesized chemicals' interaction with a particular protein domain suggests focused biological activity.

Antioxidants and polymer coating for soybean [Glycine max (L.) Merr.] seed longevity enhancement

IntroductionTo explore the effect of polymer coating and antioxidants on the soybean seed quality enhancement by reducing the oxidation process, the experiment was carried out in the Department of Seed Science and Technology, PJTSAU, India. Method: Soybean seed of JS 335 was treated with four antioxidants at concentrations of 100 ppm, 300 ppm, and 500 ppm, either individually or in combination with polymer at 8 ml kg-1 seed and stored in airtight containers. Seed quality parameters like seed germination, seedling abnormality, seed mortality, seedling length, seedling dry weight, and Seedling Vigour Index 1 and 2 were measured at various months of storage period. Results: Polymer coating, either individually or in combination with antioxidants, showed a significant influence on soybean seed quality and longevity. Six months after storage, seeds treated with polymer + ascorbic acid at 100 ppm recorded significantly (p < 0.05) higher germination %, speed of germination and field emergence % (92, 40, and 85), followed by polymer + ascorbic acid at 300 ppm with a high seedling vigour index-2 (86) compared to untreated control (86, 35, 79, and 76, respectively). Seed treated with tocopherol at 300 ppm + polymer recorded higher seedling length (cm) and seedling vigour index-1 (24.73 and 2258) than control (17.49 and 1709, respectively). Among chemical antioxidants, ascorbic acid, followed by tocopherol and reduced glutathione, showed a good effect on soybean seed longevity. Conclusion: Soybean seed quality could be enhanced either with a polymer coating or with a polymer + antioxidant coating with ascorbic acid at 100 and 300 ppm.

A Numerical Simulation Approach for Superheated Steam Flow during Multipoint Steam Injection in Horizontal Well

Superheated steam flow during multipoint steam injection technology has a good effect on improving the steam absorption profile of heavy oil thermal recovery wells, enhancing the production degree of horizontal section of thermal recovery wells, and enhancing oil recovery. Based on the structure of multipoint steam injection horizontal string, considering the characteristics of variable mass flow, pressure drop of steam-liquid two-phase flow, and throttling pressure difference of steam injection valve in the process of steam injection, this paper establishes the calculation model of various parameters of multipoint steam injection horizontal wellbore and calculates the distribution of steam injection rate, temperature, pressure gradient, and dryness along the section of multipoint steam injection in horizontal wellbore. The results show that the temperature and pressure decrease gradually from heel to toe, and the steam dryness decreases gradually. Considering the influence of throttle pressure difference of steam injection valve and pressure drop of gas-liquid two-phase flow in the wellbore, the traditional calculation model of steam injection thermodynamic parameters is optimized, and the optimization of wellbore structure and steam injection parameters is an effective method to achieve uniform steam injection in horizontal wells. The steam injection uniformity of horizontal wells can be effectively improved by adjusting the steam injection valve spacing and steam injection parameters. When the steam injection volume is 200 m3/d and the steam injection valve spacing is 20 m, a more stable steam injection effect can be obtained. The findings of this study can help for better understanding of improving the uniformity of steam injection and enhancing the recovery factor.

Danggui Jixueteng decoction for the treatment of myelosuppression after chemotherapy: A combined metabolomics and network pharmacology analysis

ObjectiveThis study aimed to explore the mechanism of the Danggui Jixueteng decoction (DJD) in treating Myelosuppression after chemotherapy (MAC) through network pharmacology and metabolomics. MethodsWe obtained the chemical structures of DJD compounds from TCMSP and PubMed. SwissTargetPrediction, STITCH, CTD, GeneCards, and OMIM were utilized to acquire component targets and MAC-related targets. We identified the key compounds, core targets, main biological processes, and signaling pathways related to DJD by constructing and analyzing related networks. The main active compounds and key proteins of DJD in treating AA were confirmed by molecular docking. A MAC rat model was established through intraperitoneal injection of cyclophosphamide to confirm DJD's effect on the bone marrow hematopoietic system. Untargeted metabolomics analyzed serum metabolite differences between MAC rats and the control group, and before and after DJD treatment, to explore DJD's mechanism in treating MAC. ResultsOf the 93 active compounds identified under screening conditions, 275 compound targets and 3113 MAC-related targets were obtained, including 95 intersecting targets; AKT1, STAT3, CASP3, and JUN were key proteins in MAC treatment. The phosphatidylinositol-3-kinase/RAC-alpha serine/threonine-protein kinase (PI3K/AKT) signaling pathway may play a crucial role in MAC treatment with DJD. Molecular docking results showed good docking effects of key protein AKT1 with luteolin, β-sitosterol, kaempferol, and glycyrrhizal chalcone A. In vivo experiments indicated that, compared to the model group, in the DJD group, levels of WBCs, RBCs, HGB, and PLTs in peripheral blood cells, thymus index increased, spleen index decreased, serum IL-3, GM-CSF levels increased, and IL-6, TNF-α, and VEGF levels decreased (p < 0.01); the pathological morphology of femoral bone marrow improved. Eleven differential metabolites were identified as differential serum metabolites, mainly concentrated in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and arachidonic acid metabolism. ConclusionThis study revealed that DJD's therapeutic effects are due to multiple ingredients, targets, and pathways. DJD may activate the PI3K/AKT signaling pathway, promote hematopoietic-related cytokine production, regulate related metabolic pathways, and effectively alleviate cyclophosphamide-induced myelosuppression after chemotherapy in rats.

Web-based Ukp Public Health Center Services System Using the Waterfall Method

Basic health services for the community are community health centers which have Health Service Units (UKP). UKP provides general health services to the community, which has many polyclinics and is interconnected with doctors, patients and administration. So far, in processing service data, there have been difficulties in general polyclinic units when receiving patient information, which is still done by recording it in a book, so there are often errors in information in patient registration services that should be received by the polyclinic that corresponds to the target polyclinic. The storage of patient data based on poly is not yet organized because the files that are stored and archived do not exist in each unit, so that when presenting data and searching you have to confirm who is archiving it, making it difficult for the data or information service department and services to be hampered. The method used in this research is the Waterfall method. This service system uses the waterfall method. The service system provides benefits in inputting and presenting data, searching for patient data such as registering online, then checking medical records to go to the clinic, online medical record results and viewing prescription information, then doctors can meet patients who carry out examinations. This system can provide good benefits and increase effectiveness and efficiency in health services for the surrounding community.

Rapid diagnosis of systemic lupus erythematosus by Raman spectroscopy combined with spiking neural network

Multiple organs are affected by the autoimmune inflammatory connective tissue disease known as systemic lupus erythematosus (SLE). If not diagnosed and treated in a timely manner, it can lead to nephritis and damage to the blood system in severe cases, resulting in the patient's death. Therefore, correct and timely diagnosis and treatment are essential for patients. In this study, a framework based on neural network algorithm and Raman spectroscopy technique was established to diagnose SLE patients. Firstly, we pre-processed the obtained Raman data by three methods: baseline correction, smoothing processing and normalization methods, before using it as input for the model, and then ANN, ResNet and SNN classification models were established. The respective classification accuracies for SLE patients were 89.61%, 85.71%, and 95.65% for the three models, with corresponding AUC values of 0.8772, 0.8100, and 0.9555. The results of the experimental indicate that SNN possesses a good classification effect, and the number of model parameters is only 525,826, which is 414,221 less than that of ResNet model. Since the network only uses 0 and 1 to transmit information, and only has basic operations such as summation, compared with the second-generation artificial neural network, which simplifies the product operation of floating point numbers into multiple addition operations, the network has low energy consumption and is suitable for embedding portable Raman spectrometer for clinical diagnosis. This research highlights the significant potential for quick and precise SLE patient discrimination offered by Raman spectroscopy in conjunction with spiking neural networks.

Research on the Arrangement of Additional Source for Large Space Protection of an Active Noise Barrier

When active noise control technology is applied to traffic noise control, additional sources are often added to the facade of the barrier, and error sensors are placed in the protected area. The noise reduction effect in the area without error sensors is often ignored. In this paper, the effect of the additional source configuration on the sound field in the space without error sensors is researched. By analyzing the directivity and distribution of the sound field at the top of the barrier under various conditions, it is believed that the optimal location of the additional source is related to the height of the primary source and the barrier. An approximate model is established to evaluate the optimal location of the additional source for achieving a good noise reduction effect in a large space. Experiments are also carried out to verify the model. The conclusions are beneficial for improving the noise reduction effect in the area higher than the barrier and without error microphones.

Assessing myocardial indices and inflammatory factors to determine anxiety and depression severity in patients with chronic heart failure.

Patients with chronic heart failure (CHF) have a progressive disease that is associated with poor quality of life and high mortality. Many patients experience anxiety and depression (A&D) symptoms, which can further accelerate disease progression. We hypothesized that indicators of myocardial function and inflammatory stress may reflect the severity of A&D symptoms in patients with CHF. Changes in these biomarkers could potentially predict whether A&D symptoms will deteriorate further in these individuals. To measure changes in cardiac and inflammatory markers in patients with CHF to determine A&D severity and predict outcomes. We retrospectively analyzed 233 patients with CHF treated at the Jingzhou Hospital, Yangtze University between 2018-2022 and grouped them according to Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores. We compared clinical data in the no-A&D, mild-A&D, moderate-A&D, and severe-A&D groups, the SAS and SDS scores with the New York Heart Association (NYHA) functional classification, and cardiac markers and inflammatory factors between the no/mild-A&D and moderate/severe-A&D groups. Regression analysis was performed on the markers with P < 0.05 to determine their ability to predict A&D severity in patients and the area under the receiver operating characteristic curve (AUROC) was used to evaluate their accuracy. In the inter-group comparison, the following variables had an effect on A&D severity in patients with CHF: NYHA class, left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter, N-terminal pro-brain natriuretic peptide (NT-proBNP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (P < 0.05). Other variables did not differ significantly between the A&D groups (P > 0.05). In addition, we found that higher NYHA classes were associated with higher the SAS and SDS scores (P < 0.05). Regression analysis showed that LVEF, NT-proBNP, and IL-6 were independent risk factors for A&D severity (P < 0.05). Among them, NT-proBNP had the best predictive ability as a single indicator (AUROC = 0.781). Furthermore, the combination of these three indicators exhibited a good predictive effect toward discriminating the extent of A&D severity among patients (AUROC = 0.875). Cardiac and inflammatory biomarkers, such as LVEF, NT-proBNP, and IL-6, are correlated with A&D severity in patients with CHF and have predictive value.

Screening and biodiversity analysis of cultivable inorganic phosphate-solubilizing bacteria in the rhizosphere of Hydrilla verticillata.

The inorganic phosphate-solubilizing bacteria (IPB) in the rhizosphere of Hydrilla verticillata can convert insoluble inorganic phosphorus in the environment into soluble phosphorus that can be directly absorbed and utilized by Hydrilla verticillata. In this research, the roots and rhizosphere sediments of Hydrilla verticillata were collected from high-organic matter urban landscape water. The National Botanical Research Institute's Phosphate growth medium (NBRIP medium) was used to screen for efficient cultivable IPB. The 16S rRNA gene sequence analysis was used to determine the taxonomic affiliation of the strains, and ammonium molybdate spectrophotometry was used to detect the phosphate-solubilizing ability of the strains. The results show that a total of 28 IPB strains with good phosphate-solubilizing effect are obtained from the roots and rhizosphere sediments of Hydrilla verticillata. These IPB strains belong to two phyla, four orders, seven classes, nine families, and nine genera. Among these, Bacillus and Acinetobacter are the dominant genera, and the strains SWIH-7, SWIP-6, SWIP-7, SWIP-13, SWIP-15 and SWIP-16 are potential new species. The IPB strains isolated and screened in this research are rich in diversity, with potential new species and stable phosphate-solubilizing characteristic. These IPB strains are suitable for further development as microbial bacterial agents, which can be applied to promote the recovery of submerged plants in polluted water with high-organic matter, treatment of polluted water and ecological restoration of water.

Identification of wheat seedling varieties based on MssiapNet.

In the actual planting of wheat, there are often shortages of seedlings and broken seedlings on long ridges in the field, thus affecting grain yield and indirectly causing economic losses. Variety identification of wheat seedlings using physical methods timeliness and is unsuitable for universal dissemination. Recognition of wheat seedling varieties using deep learning models has high timeliness and accuracy, but fewer researchers exist. Therefore, in this paper, a lightweight wheat seedling variety recognition model, MssiapNet, is proposed. The model is based on the MobileVit-XS and increases the model's sensitivity to subtle differences between different varieties by introducing the scSE attention mechanism in the MV2 module, so the recognition accuracy is improved. In addition, this paper proposes the IAP module to fuse the identified feature information. Subsequently, training was performed on a self-constructed real dataset, which included 29,020 photographs of wheat seedlings of 29 varieties. The recognition accuracy of this model is 96.85%, which is higher than the other nine mainstream classification models. Although it is only 0.06 higher than the Resnet34 model, the number of parameters is only 1/3 of that. The number of parameters required for MssiapNet is 29.70MB, and the single image Execution time and the single image Delay time are 0.16s and 0.05s. The MssiapNet was visualized, and the heat map showed that the model was superior for wheat seedling variety identification compared with MobileVit-XS. The proposed model has a good recognition effect on wheat seedling varieties and uses a few parameters with fast inference speed, which makes it easy to be subsequently deployed on mobile terminals for practical performance testing.

Analisis Jurnal Internasional Pengaruh Komunikasi Kerja pada Kinerja Karyawan Perusahaan

Analyzing several journals was carried out to find out what influence work communication has on the performance of workers in the company. To find out and gain a clearer understanding of how factors might influence the effectiveness of communication in organizations or companies. The method used is data analysis by paying attention to things such as the influence of work motivation, work compliance, work communication and employee performance. Looking at the existing data, it can be concluded that work motivation and communication in a company can increase employee productivity. Good work communication can be useful for companies in helping develop good work communication strategies and effectiveness.

Integration of metabolomics and network pharmacology technology to explain the effect mechanisms of Danggui Buxue decoction in vascular dementia.

Danggui Buxue decoction (DBD) is a traditional Chinese medicine herbal decoction that has a good therapeutic effect on vascular dementia (VaD). However, its pharmacodynamic substances and underlying mechanisms are ambiguous. The work aimed to decipher the pharmacodynamic substances and molecular mechanisms of DBD against VaD rats based on gas chromatography-mass spectrometry metabonomics, network pharmacology, molecular docking, and experimental verification. The results indicated that DBD significantly improved the learning abilities and cognitive impairment in the VaD rat model. Integration analysis of the metabolomics and network pharmacology approach revealed that DBD might primarily affect arachidonic acid (AA) and inositol phosphate metabolic pathways by regulating the platelet activation signaling pathways. Six core targets (TNF [tumor necrosis factor], IL-6 [interleukin 6], PTGS2 [prostaglandin-endoperoxide synthase 2], MAPK1, MAPK3, and TP53) in the platelet activation signaling pathways also had a good affinity to seven main active components (saponins, organic acids, flavonoids, and phthalides) of DBD through the verification of molecular docking. Enzyme-linked immunosorbent assay results (ELISA) showed that the levels of TNF, IL-6, PTGS2, thromboxane B2, and caspase-3 in the platelet activation signaling pathway can be regulated by DBD. Our results indicated that DBD treated VaD mainly by modulating the platelet activation signaling pathway, and AA and inositol phosphate metabolism.

Virtual reality modeling application based on multi perspective and deep learning in the new media presentation and brand building of Dongguan City memory

AbstractTo help the new media presentation and brand building of Dongguan City memory, a virtual reality modeling model based on multi‐perspective and deep learning is proposed. First, in order to address the issue of imbalanced input and output information in depth map prediction tasks, as well as poor accuracy of predicted depth map boundaries, a depth map prediction model based on multiple perspectives and deep learning is built. Then, a single perspective modeling framework is proposed to address the scarcity of perspectives in practical situations, and a dynamic fusion model is built for single‐view virtual reality scenes based on multi‐view generation networks. The results indicated that the mean square error, average relative error, and average logarithmic error were minimized at 0.52, 0.138, and 0.068, respectively. The multi‐threshold accuracy index demonstrated peak values at 0.772, 0.8821, and 0.947, respectively. The grid simplification algorithm exhibited the shortest running times at 1.57, 2.52, 3.91, and 6.53 s, respectively. Moreover, the single‐view modeling frame displayed the smallest angle distance at 0.1449, while the overlap degree of the point cloud scene reached the highest levels at 77.47, 79.49, 83.5, and 84.47, respectively. To sum up, the model has a good application effect in virtual reality modeling and positively affects virtual reality technology development.