Search

[Objective] To explore the effectiveness of applying the PDCA (Plan-Do-Check-Act) cycle to enhance the compatibility rate of five Rh blood group antigen phenotypes between donors and recipients across multiple hospital campuses. [Methods] Clinical blood transfusion data from May to July 2022 were selected. Specific improvement measures were formulated based on the survey results, and the PDCA cycle management model was implemented from August 2022. The post-intervention phase spanned from August 2022 to October 2023. The Rh phenotype compatibility rate, the detection rate of Rh system antibodies, and the proportion of Rh system antibodies among unexpected antibodies were compared between the pre-intervention phase (May to July 2022) and the post-intervention phase. [Results] After the continuous improvement with the PDCA cycle, the compatibility rate for the five Rh blood group antigen phenotypes between donors and recipients from August to October 2023 reached 81.90%, significantly higher than the 70.54% recorded during the pre-intervention phase (May to July 2022, P

Abstract High-throughput mesoscopic optical imaging technology has tremendously boosted the efficiency of procuring massive mesoscopic datasets from mouse brains. Constrained by the imaging field of view, the image strips obtained by such technologies typically require further processing, such as cross-sectional stitching, artifact removal, and signal area cropping, to meet the requirements of subsequent analyse. However, obtaining a batch of raw array mouse brain data at a resolution of $$0.65 \times 0.65 \times 3 \,\upmu \hbox {m}^{3}$$ 0.65 × 0.65 × 3 μ m 3 can reach 220TB, and the cropping of the outer contour areas in the disjointed processing still relies on manual visual observation, which consumes substantial computational resources and labor costs. In this paper, we design an efficient deep differential guided filtering module (DDGF) by fusing multi-scale iterative differential guided filtering with deep learning, which effectively refines image details while mitigating background noise. Subsequently, by amalgamating DDGF with deep learning network, we propose a lightweight deep differential guided filtering segmentation network (DDGF-SegNet), which demonstrates robust performance on our dataset, achieving Dice of 0.92, Precision of 0.98, Recall of 0.91, and Jaccard index of 0.86. Building on the segmentation, we utilize connectivity analysis for ascertaining three-dimensional spatial orientation of each brain within the array. Furthermore, we streamline the entire processing workflow by developing an automated pipeline optimized for cluster-based message passing interface(MPI) parallel computation, which reduces the processing time for a mouse brain dataset to a mere 1.1 h, enhancing manual efficiency by 25 times and overall data processing efficiency by 2.4 times, paving the way for enhancing the efficiency of big data processing and parsing for high-throughput mesoscopic optical imaging techniques.

ObjectiveTo explore and analyze the influencing factors of myopia among the primary and middle school students, and to provide an evidence for the prevention and control of myopia in students through the combination of scientific use in electronic products and adherence improvement to outdoor activities.MethodsStratified cluster sampling method was used to select 4 schools in Jiading District, in which all the students in grades 3‒5 of primary school and grades 7‒9 of middle school were enrolled into the study for a questionnaire survey and refraction examination. The χ2 test was used to compare the data between the myopic and non-myopic group. Logistic regression analysis was used to analyze the influencing factors of myopia.ResultsThe total myopia detection rate among the primary and middle school students in the industrial zone of Jiading District was 62.8%, with a detection rate of 46.9% for primary school students and 75.6% for middle school students, respectively. Logistic regression analysis showed that middle school (OR=3.639, 95%CI=3.045‒4.349, P1 semester (OR=1.227, 95%CI=1.031‒1.461, P=0.021), the length of time spent on extracurricular tutorial classes for main courses ≥1 hour in a week (OR=1.205,95%CI=1.009‒1.440, P=0.040), parents reduced the length of time that their children spent on exercise (OR=1.205, 95%CI=1.009‒1.440, P=0.040), and parental myopia (OR=2.611, 95%CI=2.157‒3.160, P

Vitiligo is an acquired chronic depigmentary skin disorder which results from the destruction of melanocytes. The clinical characteristic of vitiligo is the development of white macules due to the loss of functioning melanocytes in the skin or hair. Vitiligo in the genital area brings about a serious psychological impact on individuals. However, the choices of treatment for genital vitiligo are scarce. This study sheds light on the leverage of 5-ALA (5-Aminolevulinic Acid) Photodynamic Therapy (PDT) for the management of genital vitiligo. Nine patients with genital vitiligo who underwent 20 % ALA-PDT treatment were reported. The results revealed that the treatment protocol simulated melanocyte regeneration in the patients effectively. ALA-PDT exerts significant anti-inflammatory effects by reducing pro-inflammatory cytokines and inducing regulatory T cells, which may help suppress the autoimmune response involved in vitiligo pathogenesis. Notably, five patients who were defined as responders showed moderate to excellent improvement in repigmentation and the treatment was well tolerated in all cases. Our findings suggest that ALA-PDT is a potential treatment option for genital vitiligo.

In order to improve the classification accuracy of power quality disturbances, a new method combining convolutional neural network (CNN) and attention mechanism bidirectional long short memory network (ABiLSTM) for power quality disturbances classification is proposed. Firstly, spatial features of disturbance signals are extracted through CNN. Secondly, the BiLSTM hidden layer learns the internal dynamic transformation rules of local features extracted by CNN and extracts temporal features; Using attention mechanism to distinguish features extracted by BiLSTM through weighting, and mining deep temporal correlations. Finally, the disturbance classification results are output through the fully connected layer. During the experiment, the power quality disturbance signal is simulated by MATLAB Simulink. The data set obtained from the simulation is used for training and testing the algorithm, and the real data set is used to verify the algorithm. The experimental results indicate that the CNN-ABiLSTM model can automatically recognize and classify features related to power quality disturbances. Compared with other methods, this method overcomes the limitations of traditional signal analysis and feature selection, and the classification accuracy of CNN-ABiLSTM for power quality disturbance signals is 99.79%, which is superior to CNN, CNN-LSTM, and ABiLSTM methods.

With their high reliability, small size and low power consumption, rubidium atomic clocks are widely used in the fields of navigation, communication, etc. In particular, rubidium atomic clocks for navigation satellites have developed excellent stability. However, their inherent frequency drift characteristics (about E-12 to E-13/day) will deteriorate their long-term performance and affect the autonomous punctual timing of satellites. In this paper, we fully analyzed the frequency data of high-performance rubidium atomic clocks, aiming to figure out the physical mechanism behind the frequency drift. A high-precision frequency drift compensation scheme is proposed and experimentally verified. The results show that the drift rate of high-performance rubidium clock can be maintained in the order of E-15/day within 60 days without external taming, and the day stability can reach the order of E-15 (Allan variance), which greatly improves the autonomous punctual timing ability of rubidium atomic clock．

During the artistic journey, creators frequently encounter challenges stemming from pressure, resource constraints, and waning inspiration, all of which can impede their creative flow. Addressing these obstacles requires a multifaceted strategy aimed at nurturing creativity throughout the artistic process. Procedural art generation emerges as a viable solution to invigorate artistic creativity. In this study, the deep Q-network (DQN) was constructed to solve the shortage of artistic creativity through its automatic decision-making ability. The model was trained with different types of artistic styles (abstract and minimalism) in WikiArt dataset. The model generates various artistic elements of different styles, forms, or thinking according to the input parameters or constraints, and selects specific colors, textures, or shapes to help the artist maintain focus in the creation process and expand the creativity in the creation process. In order to achieve this goal, in the process of performing the procedural art generation task with DQN, the experiment collected the generation speed, interpretability, and creativity evaluation feedback of each style of art. The feedback results show that the scores of color field painting and minimalism were 83.2, 93.5, 86.3 and 86.6, 91.5, 82.1 respectively. The research shows that employing dynamic mass spectrometry networks enables the automation of the art creation process. This innovative approach facilitates the exploration of diverse creative ideas tailored to various artistic tasks, thereby fostering advancements in art creation and nurturing creativity.

ABSTRACT: Duck hepatitis A virus type 3 (DHAV-3) is an infectious virus that is highly fatal to ducklings and causes significant economic losses in the duck industry worldwide. Biosecurity and vaccination are required to control the pathogen. In the present study, we attenuated a lowly pathogenic DHAV-3 clinical isolate, named as HB, by serial passaging in duck embryos, and followed by several adaptive proliferations in specific-pathogen-free (SPF) chicken embryos. The virulence of DHAV-3 at different passages was assessed by infecting 3-day-old ducklings. We found that the HB strain lost pathogenicity to ducklings from the 55th passage onwards. The 80th passage strain (HB80), which achieved good growth capacity in duck embryos with a viral titer of 108.17 50% egg lethal dose per milliliter (ELD50/mL), was selected as a live attenuated vaccine candidate. The HB80 strain did not induce clinical symptoms or pathological lesions in 3-day-old ducklings and showed no virulence reversion after 5 rounds of in vivo back-passage. The minimum effective dose of HB80 was determined to be 104.5 ELD50 by hypodermic inoculation of the neck. Importantly, a single dose of HB80 elicited good immune responses and provided complete protection against challenge with the lethal DHAV-3 strain. Compared with the genomic sequence of the parental HB strain, HB80 had 7 amino acid substitutions, two of them are in the hypervariable region of the VP1 and polymerase-encoding 3D regions, which may play a role in virulence attenuation. Our data suggest that the attenuated HB80 strain is a promising vaccine candidate for the prevention of DHAV-3 infections in China. HB80 has been registered as a New Veterinary Drug Registration Certificate by the Chinese Ministry of Agriculture and Rural Affairs (MARA), and is the first live attenuated DHAV-3 vaccine strain to be officially licensed in China.

Most prior studies on the insulation effect of off-wall insulation have neglected the natural convection of the air lining; this assumption is inconsistent with reality. In this study, the effect of natural air convection on the heat transfer mechanism in off-wall insulation lining was studied via experiments and numerical simulation. The thermal insulation effect of the air lining was evident. The temperature field distribution of the surrounding rock is affected by the natural convection in the air lining and is thus clearly related to the azimuth. The temperature disturbance of the surrounding rock in the direction of θ = 0° is the minimum, whereas that in the direction of θ = 180° is the maximum. The most significant factors affecting the thermal insulation effect of the off-wall insulation lining are the thermal conductivity of the rock and thermal resistance of the insulation layer, followed by thickness of the air lining and temperature difference between the original rock temperature and the air flow. The relationships between the average unstable heat transfer coefficient and various parameters were obtained via fitting. The fitting formula can be used in engineering practice to calculate the heat transfer from the surrounding rock to the tunnel airflow under various conditions.

The penetration rate of photovoltaic power generation keeps increasing. To address the issues of poor cluster partitioning and lengthy processing times for photovoltaic power station clusters， the paper proposes a method for rapid cluster partitioning method for photovoltaic （PV） plants based on prototype extraction and clustering. Firstly， photovoltaic data is preprocessed to eliminate differences in magnitude and dimensionality among data sets. Subsequently， influential factors on photovoltaic output power are identified using the Pearson correlation coefficient method. Random sampling， k-means++， and an improved spectral clustering method are then employed for sampling， prototype extraction， and prototype clustering of PV plants， respectively. Building upon an enumeration approach and hierarchical optimization， optimal hyperparameters for the aforementioned processes are determined. Finally， various scenarios are set up for case study comparisons， calculating both intra-cluster and inter-cluster indicators as well as clustering time metrics. Through comprehensive analysis， the effectiveness of the proposed method in addressing the rapid clustering for large-scale PV plants is validated.

In response to the challenge of sample data imbalance in fault diagnosis methods for photovoltaic power plants based on machine learning， the paper proposes a fault diagnosis method leveraging an enhanced BP-Bagging algorithm. Firstly， a mapping relationship between photovoltaic data and fault types is established using a BP neural network to achieve fault diagnosis in photovoltaic systems. Subsequently， the Bagging algorithm is enhanced by utilizing random under-sampling （RUS） to address the issue of class imbalance in samples. Furthermore， to tackle the problem of overfitting in the BP network， the paper introduces a fault diagnosis model for photovoltaic power plants based on the enhanced BP-Bagging. This involves parallel training of multiple BP networks and determining fault diagnosis results through a voting method. Finally， the paper conducts comparative experiments with different algorithms， calculates evaluation metrics related to model accuracy， and validates that the proposed method demonstrates high overall performance. To a certain extent， it effectively mitigates the challenge of sample class imbalance in fault diagnosis of photovoltaic power plants， thereby improving the accuracy of fault diagnosis in such systems.

ObjectiveIn recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields.MethodsA method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F1 were used to evaluate the model accuracy in this study.Results and DiscussionThe research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared (1 300-1 800 nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F1 of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F1 compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F1 of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F1 of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F1=0.70) and SCR severity classification model (OA=71.39%, Macro F1=0.69) were lower than that of NCLB.ConclusionsThe aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for large-scale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture.

C37H52O3, orthorhombic, P212121 (no. 19), a = 8.2477(3) Å, b = 19.2976(7) Å, c = 19.6372(4) Å, V = 3125.47(17) Å3, Z = 4, R gt(F) = 0.0558, wR ref(F 2) = 0.1728, T = 293 K.

Abstract Background Rheum palmatum L. has important medicinal value because it contains biologically active anthraquinones. However, the key genes and TFs involved in anthraquinone biosynthesis and regulation in R. palmatum remain unclear. Methods Based on full length transcriptome data, in this study, we screened the differentially expressed genes in the anthraquinone biosynthesis pathway. The R2R3-MYB family genes of R. palmatum were systematically identified based on full-length transcriptome sequencing followed by bioinformatics analyses. The correlation analysis was carried out by using co-expression analysis, protein interaction analysis, and real-time fluorescence quantitative analysis after MeJA treatment. The RpMYB81 and RpMYB98 genes were amplified by RT-PCR, and their subcellular localization and self-activation characteristics were analyzed. Results Comparative transcriptome analysis results revealed a total of 3525 upregulated and 6043 downregulated DEGs in the CK versus MeJA group; 28 DEGs were involved in the anthraquinone pathway. Eleven CHS genes that belonged to the PKS family were differentially expressed and involved in anthraquinone biosynthesis. Twelve differentially expressed MYBs genes were found to be co-expressed and interact with CHS genes. Furthermore, 52 MYB genes were identified as positive regulators of anthraquinone biosynthesis and were further characterized. Three MYB genes including RpMYB81, RpMYB98, and RpMYB100 responded to MeJA treatment in R. palmatum, and the levels of these genes were verified by qRT-PCR. RpMYB81 was related to anthraquinone biosynthesis. RpMYB98 had an interaction with genes in the anthraquinone biosynthesis pathway. RpMYB81 and RpMYB98 were mainly localized in the nucleus. RpMYB81 had self-activation activity, while RpMYB98 had no self-activation activity. Conclusion RpMYB81, RpMYB98, and RpMYB100 were significantly induced by MeJA treatment. RpMYB81 and RpMYB98 are located in the nucleus, and RpMYB81 has transcriptional activity, suggesting that it might be involved in the transcriptional regulation of anthraquinone biosynthesis in R. palmatum.

Infective endocarditis often leads to various vascular complications, among which cerebrovascular complications are associated with increased mortality in the early and late stages of the disease. Anticoagulant therapy for infective endocarditis is still controversial. How to balance the risk of cerebral embolism and bleeding events during the treatment is a difficult problem to define in clinical practice. This article reviews the epidemiology, pathophysiology, imaging evaluation methods and anticoagulation treatment strategies of disease⁃related cerebral embolism and bleeding events, in order to provide guidance for clinical anticoagulant treatment of infective endocarditis.

Driven by the demand for satellite navigation and deep space exploration, the performance of lamp-pumped rubidium atomic clock has been greatly improved, and its short-term frequency stability has reached the level of a small coefficient of 10-13τ -1/2. To further improve the frequency stability of rubidium clock and explore its performance limit, this article is based on the redesign and experimental verification of the structure of the physical package (PP, Φ40 microwave cavity) in the early stage, with a comprehensively optimized design of the optical system of PP, the performance of the spectral lamp and pumping light has been enhanced. Finally, the SNR of PP has been significantly increased. The test and evaluation results showed that the contribution of the shot noise of the newly designed PP to the frequency stability of rubidium clock is 4.2×10-14τ -1/2. The results of this research lay the foundation for the short-term stability of rubidium clocks to achieve 5×10-14τ -1/2, and the long-term stability to break through 1×10-15.

Three different sweet red wines were produced using Tempranillo grapes with three different sugar concentrations: 25, 50 and 75 g/L, using sulfur dioxide and low temperature to stop the alcoholic fermentation. They were compared to the wine produced without stopping the alcoholic fermentation. Cold pre-fermentation macerations and ultrasound during the post-fermentation stage were applied to try to improve the organoleptic properties specifically for sweet wines. The treatment with ultrasound after stopping the fermentation enhanced the extraction of anthocyanins compared to the wines produced without ultrasound, increasing the red color of the final wines, resulting in increments in the range of 16–30%. In any case, significant differences were found between the regular dry red wine and the sweet wines in terms of polyphenolic content, anthocyanin, tannin concentration and absorbance at 520 nm, with lower contents for the sweet wines. The wines were evaluated by different tasting panels showing different results depending on the tasting panel composition. First, an inverse relationship was observed between the sweetness of the wines and their acidity, bitterness, and astringency descriptors. Additionally, wines with higher sugar levels were described as having the most fruity aromas. Finally, no differences in vegetal aromas were found in the different wines.

The changes produced during the aging of wines in oak barrels are strongly dependent on the oak’s geographical origin and aging time. This paper analyzes the effect of Quercus alba oak from four different geographical locations in four states in the USA, namely Missouri (Mo), Ohio (Oh), Kentucky (Kt), and Pennsylvania (Py), during 24 months of aging. Oak origin had a higher effect on the wine’s aromatic composition than the polyphenolic one. Mo and Oh barrels enhanced coconut, spicy, and sweet notes for 12 months of aging, while Kt barrels achieved higher extraction of wood-related compounds at longer aging (24 months). Py wines showed the lowest contents of most volatile compounds at both aging times, as well as hydroxycinnamic acids, flavanols, anthocyanins, flavonols, stilbenes, and ellagitannins, attributed to their higher porosity. At 12 months of aging, Kt wines showed the highest content of ellagitannins, and Mo wines had the highest content of anthocyanins, but Oh wines had the highest concentrations at 24 months. In the sensory analysis, Kt wines were preferred at both aging times. Kt and Mo wines achieved the highest punctuations for the olfactory phase at 12 months of aging and Kt wines kept it after 24 months. These findings are essential for producers to achieve the sensory characteristics of their wines through strategic barrel aging.

In order to study the difference in the encapsulation capacity of different microcapsule wall materials for volatile flavor substances, microcapsules were prepared with seven common wall materials by ultrasonic spray-freeze drying technology and their morphology, encapsulation and slow release capacity for 32 volatiles were evaluated by cold field emission scanning electron microscopy (CFE-SEM), electronic nose (EN), and gas chromatography-mass spectrometry (GC-MS). These results showed that the encapsulation capacity of different wall materials exhibited selectivity for different volatiles. In general, β-cyclodextrin had a stronger encapsulation capacity for the volatile compounds, with a higher encapsulation and release efficiency, so it was an ideal wall material. After 30 minutes of heating, the release profile of volatiles encapsulated by β-cyclodextrin in the EN radar plot was similar to that of free volatiles, indicating that the microcapsules prepared in this study had good slow-release performance. These results provide a theoretical basis for the utilization of microencapsulated products for flavor enhancement and flavor stability improvement in the food field.

tert-butyl peroxy benzoate (TBPB)/acrylated epoxy soybean oil composite(AESO) composite was prepared by immersion compression for-ming technology, and its curing process and structure were investigated. The results showed that the reaction activation energy of TBPB/AESO composite calculated based on Kissinger method and Starink method was 78.803 kJ/mol. Sesthk-Berggren was selected as the curing model, and the calculated curing kinetics equation based on the Malek method fitted well with the experimental data. The optimized curing process of TBPB/AESO involved increasing slowly the temperature to 407 K, then increasing rapidly the temperature to 424 K for constant temperature curing, and finally increasing slowly the temperature to 489 K for a certain period of time.

Objective To analyze the adverse drug reactions (ADR) of atezolizumab and durvalumab based on the US Food and Drug Administration Public Data Open Project (OpenFDA) to provide reference for rational clinical use of drugs. Methods The ADR data of atezolizumab and durvalumab from January 1, 2004 to January 28, 2022 were retrieved through the application programming interface (API) access to the interactive icon panel of the OpenFDA ADR endpoint. Results A total of 13 630 and 5 374 ADR reports respectively related to atezolizumab and durvalumab were obtained. More cases were reported in males than in females. The most common ADR of atezolizumab were fever, diarrhea and pneumonia, and the common ADR of durvalizumab was radiation pneumonitis. The incidences of ADR and serious ADR were similar in atezolizumab and durvalumab, except the incidences of anemia, febrile neutropenia and radiation pneumonitis. Conclusion ADR of atezolizumab and durvalizumab can be quickly and conveniently excavated and studied based on the OpenFDA database. With the increase of the use of these two drugs, the rare ADR in clinic is increasingly prominent, so clinical medication monitoring should be strengthened to promote their safe use.

Cytoarchitecture, the organization of cells within organs and tissues, serves as a crucial anatomical foundation for the delineation of various regions. It enables the segmentation of the cortex into distinct areas with unique structural and functional characteristics. While traditional 2D atlases have focused on cytoarchitectonic mapping of cortical regions through individual sections, the intricate cortical gyri and sulci demands a 3D perspective for unambiguous interpretation. In this study, we employed fluorescent micro-optical sectioning tomography to acquire architectural datasets of the entire macaque brain at a resolution of 0.65 μm × 0.65 μm × 3 μm. With these volumetric data, the cortical laminar textures were remarkably presented in appropriate view planes. Additionally, we established a stereo coordinate system to represent the cytoarchitectonic information as surface-based tomograms. Utilizing these cytoarchitectonic features, we were able to three-dimensionally parcel the macaque cortex into multiple regions exhibiting contrasting architectural patterns. The whole-brain analysis was also conducted on mice that clearly revealed the presence of barrel cortex and reflected biological reasonability of this method. Leveraging these high-resolution continuous datasets, our method offers a robust tool for exploring the organizational logic and pathological mechanisms of the brain’s 3D anatomical structure.

Objective To compare the clinical effects of coronary artery bypass grafting (CABG) between the left anterior small thoracotomy (LAST) and lower-end sternal splitting (LESS) approaches for coronary artery disease. Methods In total, 110 patients who underwent LAST from October 2015 to December 2020 in Tianjin Chest Hospital were selected as the observation group. Patients who underwent the LESS approach during the same period were analyzed. The propensity score was calculated by a logistic regression model, and nearest-neighbor matching was used for 1:1 matching. Results The length of hospital stay and ventilator support time were significantly shorter in the LAST than LESS group. The target vessels in the obtuse marginal branch and posterior left ventricular artery branch grafts were significantly more numerous in the LAST than LESS group, but those in the right coronary artery graft were significantly less numerous in the LAST group. Conclusions CABG using either the LAST or LESS approach is safe and effective, especially in low-risk patients. The LAST approach can achieve complete revascularization for multivessel lesions and has the advantages of less trauma and an aesthetic outcome. However, it requires a certain learning curve to master the surgical techniques and has specific surgical indications.

C18H15FN4O, triclinic, P 1‾ $\overline{1}$ (no. 2), a = 8.8914(5) Å, b = 10.5705(7) Å, c = 16.4831(9) Å, α = 96.673(5)°, β = 99.397(5)°, γ = 95.062(5)°, V = 1508.93(16) Å3, Z = 4, Rgt (F) = 0.0477, wRref (F 2) = 0.1232, T = 293 K.

C10H10O2, monoclinic, C2/c (no. 15), a = 16.6341(14) Å, b = 7.4319(4) Å, c = 14.8943(10) Å, β = 123.243(8)°, V = 1540.0(2) Å3, Z = 8, Rgt(F) = 0.0371, wRref(F2) = 0.0997, T = 293 K.

C36H52FNO3, orthorhombic, P212121 (no. 19), a = 7.8101(5) Å, b = 13.1592(7) Å, c = 30.5721(15) Å, V = 3142.0(3) Å3, Z = 4, R gt(F) = 0.0488, wR ref(F 2) = 0.1048, T = 293 K.

Based on the background of high quality development of manufacturing enterprises, this article analyzes the significant impact of digital economy on manufacturing industry, and explores the construction of digital maturity evaluation system in line with the current development status of manufacturing industry. The capability maturity evaluation of digital transformation of manufacturing industry in Yangtze River Delta based on service-oriented distributed application framework is proposed. By determining the construction ideas and principles of the evaluation index system of manufacturing digitization maturity, this article studies the evaluation index system of manufacturing digitization from four first-level indexes including digitization strategy, digitization infrastructure, digitization operation and digitization performance, ten second-level indexes including digitization emphasis, and 27 third-level indexes including the level setting of digitization management positions, and it proposed to carry out the digital maturity evaluation of manufacturing enterprises. This article conducts a survey on 183 manufacturing industries in the Yangtze River Delta region, analyzes the current situation of enterprises’ digital transformation, and explores the influence mechanism of enterprises’ digital level on regional integration development. The results show that most of the manufacturing enterprises in the Yangtze River Delta have strong desire to transform but still face many difficulties. Enterprises’ emphasis on digital transformation and application ability of digital technology significantly affect the development of regional integration.

With the deepening of the new curriculum reform, students’ comprehensive quality education has attracted wide attention from all walks of life, and people pay more and more attention to aesthetic education. As an important part of aesthetic education, fine art education plays an important role in cultivating students’ aesthetic ability. In the process of art education, painting technique teaching plays a very important role and value, which is conducive to students’ in-depth understanding of the connotation of painting art and training students’ ability of practice and image thinking. In this paper, ARCS model is used to investigate and analyze the current situation of students’ art learning, and ARCS model is used to design a teaching scheme for reference for art teachers, and teaching experiments are carried out. The concrete implementation methods, steps and case analysis of ARCS motivation model in middle school art teaching design were summarized, and the improvement measures were further proposed according to the experimental results. In the whole research process, a combination of qualitative and quantitative analysis is adopted. The final research shows that it is necessary for teachers to use ARCS motivation model to design teaching programs.

In the context of the new dual-cycle development model, the Long Triangle, as the main development pillar of China's economic development, aims to enhance the quality development level of the digitization and industrialization, and achieve the deep development of the two. Based on the differential equation, this paper presents the problems of the linkage and coordination between digitization and sustainable development of manufacturing in Long Triangle. The countermeasures based on panel data of three provinces and a city in Long Triangle from 2011 to 2019, an index system of quality of digitization and productivity has been established, and spatio-temporal evolution characteristics of joint level and problems in the process of joint coupling are analyzed. The results show that from 2011 to 2019, the level of linkage and coordination between economic digitization and sustainable development of production in Long Triangle region is increasing, but the overall level is not high and the spatial difference is large. There are also significant differences between rank and benign cooperation; The key obstacle factors in each province are both relatively concentrated and different.

In the new era, the national high-tech industrial development Zone is a powerful platform for implementing the innovation-driven development strategy and promoting China’s high-level development. An empirical study of panel data from 285 county-level cities in China during 2001-2022 was conducted using the binary difference method. The results show that high-tech enterprises have a noticeable promoting effect on improving the quantity of industrial structure. The transformation and upgrading of our industrial design by high-tech enterprises have significant cyclical heterogeneity. It has significantly enhanced the industrial system compared with the “growth type” and “mature type” national high-tech development zones. There is no apparent regional difference in the quality effect of high-tech enterprises on industrial structure upgrading. Still, there is an evident regional difference in the quantity and rationality of industrial structure height.

Objective To analyze the irrational use of proton pump inhibitors (PPI) for injection in clinical practice and its reasons, and to improve the rationality of PPI through appropriate intervention measures. Methods The consumption of PPI in June 2023 was retrieved from the hospital information system of Nanjing Medical University Affiliated Brain Hospital/Nanjing Chest Hospital. The irrational use of PPI during the use process was classified, counted, and summarized. Results A total of 789 PPI for injection were used in Nanjing Medical University Affiliated Brain Hospital/Nanjing Chest Hospital in June 2023, including 569 injections of esomeprazole, 84 injections of omeprazole, 42 injections of esomeprazole, 41 injections of lansoprazole, 36 injections of pantoprazole, and 17 injections of rabeprazole. Ninety-one cases of irrational medication orders were found, including over-treatment (36 cases, accounting for 39.56%), off-label use (23 cases, accounting for 25.27%), inappropriate dosing (23 cases, accounting for 25.27%), and inappropriate frequency (9 cases, accounting for 9.89%). Conclusion The phenomenon of irrational use of proton pump inhibitors in hospitals is serious, including empirical use, habitual use, and preventive use. Pharmacists should take various effective countermeasures to actively intervene, timely feedback the information of irrational use to clinical practice, promote the rational use of proton pump inhibitors in clinical practice, and improve the level of rational drug use in clinical practice.

ObjectiveThis study aims to investigate the current state of WeChat parent-child relationships among college students and their association with family factors.MethodsUsing convenient sampling, 6159 students,age from 18 to 25,the participants were selected from colleges of Chengdu to complete a questionnaire survey on response and attitudes towards parents' WeChat friend requests, Family APGAR Index,Parental Management Scale,Family Conflict Scale,were used as measurement tools.ResultsResponses to parental WeChat friend requests showed significant positive correlations with higher levels of family function satisfaction (r = 0.10, PConclusionThis study reveals that Chinese college students generally accept their parents' WeChat friend requests, influenced by family dynamics and traditional values. Positive parental management, family harmony, and good marital relationships enhance students' willingness to connect with their parents on WeChat, indicating the complex interplay between digital interactions and traditional family concepts.This underscores the importance for parents to adopt modern and scientific parenting approaches, fostering a harmonious and nurturing family atmosphere rooted in trust and mutual respect for their children.

The main olfactory bulb is the key element of the olfactory pathway of rodents. To precisely dissect the neural pathway in the main olfactory bulb (MOB), it is necessary to construct the three-dimensional morphologies of the anatomical structures within it with micro-level resolution. However, the construction remains challenging due to the complicated shape of the anatomical structures in the main olfactory bulb and the high resolution of micro-optical images. To address these issues, we propose an interactive volume image segmentation method with micro-level resolution in the horizontal and axial direction. Firstly, we obtain the initial location of the anatomical structures by manual annotation and design a patch-based neural network to learn the complex texture feature of the anatomical structures. Then we randomly sample some patches to predict by the trained network and perform an annotation reconstruction based on intensity calculation to get the final location results of the anatomical structures. Our experiments were conducted using Nissl-stained brain images acquired by the Micro-optical sectioning tomography (MOST) system. Our method achieved a mean dice similarity coefficient (DSC) of 81.8% and obtain the best segmentation performance. At the same time, the experiment shows the three-dimensional morphology reconstruction results of the anatomical structures in the main olfactory bulb are smooth and consistent with their natural shapes, which addresses the possibility of constructing three-dimensional morphologies of the anatomical structures in the whole brain.

C13H18N2O4S, orthorhombic, P212121 (no. 19), a = 6.8619(2) Å, b = 12.0735(4) Å, c = 16.6591(5) Å, V = 1380.16(7) Å3, Z = 4, R gt(F) = 0.0236, wR ref(F 2) = 0.0604, T = 150 K.

C20H17F3O3, monoclinic, P21/c (no. 14), a = 9.0684(4) Å, b = 13.1942(6) Å, c = 13.7883(7) Å, β = 96.250(5)°, V = 1639.97(13) Å3, Z = 4, R gt(F) = 0.0415, wR ref(F 2) = 0.1125, T = 100 K.

C18H12F4O, triclinic, P1̄ (no. 2), a = 7.9400(8) Å, b = 8.1071(9) Å, c = 13.3238(15) Å, α = 89.394(9)° $89.394\left(9\right)^{\circ} $ , β = 76.343(9)° $76.343\left(9\right)^{\circ} $ , γ $$ =63.411(11)° $\text{63.411}\left(11\right)^{\circ} $ , V = 740.79(15) Å3, Z = 2, Rgt ${R}_{\mathit{g}\mathit{t}}$ (F) = 0.0472, wRref $w{R}_{\mathit{r}\mathit{e}\mathit{f}}$ (F 2) = 0.1244, T = 293 K.

To investigate the effect of different structures of MOF on the catalytic performance of ammonium perchlorate (AP) thermal decomposition. By adding 2-methylimidazole (2-MI) as an inhibitor during the synthesis of MOF-5, a two-dimensional (2D) MOF material was prepared and characterized by SEM, EDS, XRD, FT-IR and other means. The prepared two-dimensional material was added to the AP raw material to investigate its effect on the AP thermal decomposition process. After DSC and TG test, it was found that the addition of this kind of 2D MOF material can completely eliminate the low-temperature decomposition peak of the AP-based composite and advance the high temperature decomposition peak to 296.5 °C, and the apparent heat of decomposition increase to 905.6 J/g, which has a better catalytic effect than the bulk MOF-5. In addition, through the combustion test, it was found that 2D MOF, as a burning rate catalyst, can effectively enhance the burning rate of AP based solid propellant.

Objective: Recently, artificial neural networks (ANNs) have been proven effective and promising for the steady-state visual evoked potential (SSVEP) target recognition. Nevertheless, they usually have lots of trainable parameters and thus require a significant amount of calibration data, which becomes a major obstacle due to the costly EEG collection procedures. This paper aims to design a compact network that can avoid the over-fitting of the ANNs in the individual SSVEP recognition. Method: This study integrates the prior knowledge of SSVEP recognition tasks into the attention neural network design. First, benefiting from the high model interpretability of the attention mechanism, the attention layer is applied to convert the operations in conventional spatial filtering algorithms to the ANN structure, which reduces network connections between layers. Then, the SSVEP signal models and the common weights shared across stimuli are adopted to design constraints, which further condenses the trainable parameters. Results: A simulation study on two widely-used datasets demonstrates the proposed compact ANN structure with proposed constraints effectively eliminates redundant parameters. Compared to existing prominent deep neural network (DNN)-based and correlation analysis (CA)-based recognition algorithms, the proposed method reduces the trainable parameters by more than ${90}\%$ and ${80}\%$ respectively, and boosts the individual recognition performance by at least ${57}\%$ and ${7}\%$ respectively. Conclusion: Incorporating the prior knowledge of task into the ANN can make it more effective and efficient. The proposed ANN has a compact structure with less trainable parameters and thus requires less calibration with the prominent individual SSVEP recognition performance.

Visual search is ubiquitous in daily life and has attracted substantial research interest over the past decades. Although accumulating evidence has suggested complex neurocognitive processes underlying visual search, the neural communication across the brain regions remains poorly understood. The present work aimed to fill this gap by investigating functional networks of fixation-related potential (FRP) during the visual search task. Multi-frequency electroencephalogram (EEG) networks were constructed from 70 university students (male/female = 35/35) using FRPs time-locked to target and non-target fixation onsets, which were determined by concurrent eye-tracking data. Then graph theoretical analysis (GTA) and a data-driven classification framework were employed to quantitatively reveal the divergent reorganization between target and non-target FRPs. We found distinct network architectures between target and non-target mainly in the delta and theta bands. More importantly, we achieved a classification accuracy of 92.74% for target and non-target discrimination using both global and nodal network features. In line with the results of GTA, we found that the integration corresponding to target and non-target FRPs significantly differed, while the nodal features contributing most to classification performance primarily resided in the occipital and parietal-temporal areas. Interestingly, we revealed that females exhibited significantly higher local efficiency in delta band when focusing on the search task. In summary, these results provide some of the first quantitative insights into the underlying brain interaction patterns during the visual search process.

Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method μ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the μ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the μ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, & SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiologically plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the μ-STAR model for source imaging in various applications.

C19H14F4O2, triclinic, P1‾$P\overline{1}$ (no. 2), a = 8.3798(6) Å, b = 9.2019(7) Å, c = 10.4504(7) Å, α = 77.941(6)°, β = 85.712(6) , γ = 79.078(6)°, V = 773.25(10) Å3, Z = 2, Rgt(F) = 0.0458, wRref(F2) = 0.1165, T = 100 K.

ObjectiveTo optimize the enzymatic hydrolysis technology of bioactive peptide in Katsuwonus pelamis and observe the anti-fatigue effect of bioactive peptide on mice.MethodsWith the yield of bioactive peptide in Katsuwonus pelamis as the evaluation index, the papain, neutral protease, flavor protease, alkaline protease and acid protease were used to screen out the best enzymes for Katsuwonus pelamis surimi. The effects of solid-liquid ratio, enzyme amount, enzymatic reaction time and temperature on the enzymatic hydrolysis were investigated. The response surface methodology was designed based on the single factor experiment to obtain the optimal enzymatic hydrolysis condition of bioactive peptide in Katsuwonus pelamis. The fatigue mice model was established by incremental loaded swimming training and the experiment mice were randomly divided into quiet blank group, exercise model group, positive control group, low-, medium- and high-dose of bioactive peptide in Katsuwonus pelamis groups (200, 400, 800 mg/kg, respectively) according to the body mass. Except for the quiet blank group, the other groups were trained with incremental loaded swimming with the training time as the variable. The loaded swimming test was conducted to detect the exhaustion time of mice. The contents of serum urea nitrogen (BUN) and serum lactic acid (LD), serum lactic dehydrogenase (LDH) activity, liver malondialdehyde (MDA) content and liver superoxide dismutase (SOD) activity were measured with corresponding kits.Results1) The yield of bioactive peptide in Katsuwonus pelamis hydrolyzed by flavor protease was statistically higher than that of neutral protease, papain, alkaline protease and acid protease (PPKatsuwonus pelamis was 33.166%. 2) The contents of BUN, LD, MDA, and the activities of LDH, SOD in the exercise model group were significantly higher than those in the quiet blank group (PKatsuwonus pelamis groups were significantly prolonged (PPPKatsuwonus pelamis with flavor protease, which verified that bioactive peptide in Katsuwonus pelamis could relieve fatigue in exhausted swimming mice.

The bremsstrahlung is an important source term in the shielding design of the electron accelerator. In order to study the characteristics of the photon source term in the 90° direction and the influence of target radius and thickness on the bremsstrahlung in the 90° direction, the Monte Carlo program of MCNPX27 was used to calculate the radiation source term from the 15 MeV3 GeV electron beam incident on the iron target with different thicknesses and radii. The variation characteristics of radiation source items such as photon radiation dose rate and photon energy spectrum with target thickness and radius were obtained and comparatively analyzed. By comparing with the photon source term in the 0° direction and the energy deposition of cascade electron in the target, the main features of the photon source term in the 90° direction were obtained. Finally, the effects of beam power and beam intensity on radiation dose rates in the 90° and 0° directions were analyzed. The result shows that, due to the combined influence of electron energy deposition in the target and self-absorption of photons from the target, the radius and thickness of the target are expected as important factors which affect the photon source term in the 90° direction. The energy of the cascade electrons deposited in the target and the radiation dose rate in the 90° direction increases with the thickness. As the energy of the cascade electrons is completely deposited in the target, the radiation dose rate in the 90° direction will not change with the target thickness. The radiation dose rate in the 90° direction also increases with the target radius, but when the target radius is larger, the radiation dose rate in the 90° direction decreases due to the selfabsorption. Apart from that, the effect of incident electron energy on the photon source terms in the 90° direction and 0° direction is different. More than 99% of the photons in the 90° direction are below 10 MeV, and most of them are within 3 MeV. In the 90° direction, the photon energy spectrum shape has little relationship with the incident electron energy. However, the photon energy in the 0° direction can reach the incident electron energy, which is generally much larger than the photon energy in the 90° direction, and the shape of the energy spectrum is greatly affected by the incident electron. As a suggestion, the influence of the target size should be considered in the shielding design of the electron accelerator, and the design in the lateral shielding should be optimized according to the difference between the photon source term in the 90° direction and 0° direction.

Major depressive disorder is a serious disease that impacts human health worldwide. Traditional antidepressant medications are limited by the delayed onset of therapeutic effects and the unacceptable side effects. Recently, the rapid antidepressant effect of ketamine, which decreases depressive symptoms within hours, has been a very attractive finding in the field, but the underlying mechanisms mediating this rapid response remain unclear. Besides, the clinical application of ketamine still carries risks, such as addiction and deleterious side effects. In May 2022, Rawat et al. reported that the acute activation of adult-born immature granule neurons (ABINs) in the dentate gyrus played a key role in the rapid antidepressant effects of ketamine. This commentary reviews the important findings of this research and looks forward to the future work.

Gastric cancer is a common malignant tumor of the digestive tract, with a low early diagnosis rate. N-methyl-N-nitro-N-nitroguanidine (MNNG) is one of the main risk factors for gastric cancer. Phytochemicals are healthy active substances derived from vegetables, fruits, nuts, tea, herbal medicines and other plants. Taking phytochemicals is a very promising strategy for the prevention and treatment of gastric cancer. Many studies have proved that phytochemicals have protective effects on MNNG induced gastric cancer via inhibiting cell proliferation, enhancing immunity, suppressing cell invasion and migration, inducing apoptosis and autophagy, blocking angiogenesis, inhibiting Helicobacter pylori infection as well as regulating metabolism and microbiota. The intervention and therapeutic effects of phytochemicals in MNNG induced gastric cancer have attracted more and more attention. In order to better study and explore the role, advantages and challenges of phytochemicals in MNNG induced gastric cancer, we summarized the intervention and therapeutic effects of phytochemicals in MNNG induced gastric cancer. This review may help to further promote the research and clinical application of phytochemicals in MNNG induced gastric cancer, and provide some new insights.

Abstract Over 50% of women experience mood disturbance in the postpartum period, with significant implications for maternal and infant health but identifying those at risk is not easily possible. The essential amino acid, tryptophan (TRP) through its neuroactive metabolites, has been implicated in the pathology of mood disorders. Thus, TRP levels tested in the peripartum period have been proposed as a potential biomarker for subsequent development of postpartum mood disturbances, in particular postpartum depression (PPD). A systematic review and meta-analysis following PROSPERO guidelines [CRD42021252462] was conducted on peer-reviewed, English language studies that measured blood levels of TRP during the postpartum period in women who were also evaluated for postpartum “blues” or PPD. Thirteen studies met the inclusion criteria, of which five studies contained sufficient data to conduct a meta-analysis. Low total TRP levels in postpartum days 1 to 5 were significantly associated with PPD (SMD: -5.39, 95%CI [-7.72, -3.05]). No significant association was found between free TRP levels in the postpartum period and PPD (SMD: -3.43, 95%CI [-7.76, 0.89]). Our findings confirm the necessity for more replicable designed studies regarding TRP and its relationship to postpartum depression. If there were greater clarity regarding TRP metabolism during pregnancy, then the next step would be to consider measuring total plasma TRP levels on postpartum days 1 to 5 to identify women at greater risk of developing PPD.

The development of food infrared heating theories were discussed in this review, and the advancement of infrared heating in food processing were summarized. Moreover, the principle of food processing and process optimum method in various food infrared thermal treatment technologies, including sterilizing, thawing, blanching, baking, peeling and drying processes were also discussed.

With increasing concerns for energy conservation and environmental protection, research on glazed hollow bead thermal insulation mortar is of utmost importance. This type of mortar offers superior thermal insulation, leading to reduced energy consumption and emissions, in line with the current green building trends. This article aims to investigate the impact of varying component proportions on the parameters of thermal insulation mortar through an orthogonal experiment with four factors and three levels: glazed hollow bead, sepiolite, air-entraining agent, and cellulose ether. Additionally, a single-factor experiment is conducted to analyze the influence degree of water-solid ratio and these four factors. The experimental results are then verified through SEM (Scanning Electron Microscope) observation. The research findings indicate that glazed hollow beads have the most significant impact on thermal conductivity and compressive strength, while the air-entraining agent exerts the greatest influence on flexural strength. Specifically, when the content of glazed hollow bead is 2%, sepiolite 1%, air-entraining agent 0.6%, and cellulose ether 0.6%, the thermal conductivity can reach a minimum value of 0.0533W/(m·K). On the other hand, when the content of glazed hollow bead is 1%, sepiolite 2%, air-entraining agent 0.4%, and cellulose ether 0.6%, the compressive strength can achieve a maximum value of 2.4 MPa. These findings provide a solid foundation for further exploration into improving the performance of thermal insulation mortar.