The response of ecosystem vulnerability to climate change and human activities in the Poyang lake city group, China.

Ecosystem vulnerability is an ecological response of the environment to external damage. Studying the influencing factors and spatiotemporal changes of ecosystem vulnerability is helpful to maintain ecological balance. At present, studies on ecosystem vulnerability are relatively homogeneous and rarely integrate climate change and human activities. Based on a habitat-function framework, this study analyzed the response of ecosystem vulnerability on climate change and human activities in the Poyang Lake City Group (PLCG) in 2010, 2015 and 2020. The spatial agglomeration of ecosystem vulnerability has been analyzed by using GeoDa model. The interaction of factors on ecosystem vulnerability have been analyzed by using geographical detector. It can be seen that the ecosystem vulnerability of the PLCG have increased from 2010 to 2020. The impacts of climate change to the ecosystem vulnerability have showed a positive correlation. Meanwhile, the key factors leading to the change of ecological vulnerability are still human activities. This methodology demonstrates a high level of robustness when applied to other research domains. This research is conducive to maintaining the integrity of the ecosystem, realizing the development of man and nature, and promoting the sound and rapid development of economic society.

An efficient approach addressing the chemical complexity of Jiawei Fangji Huangqi decoction by integrating ultra-high-performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry and intelligent data processing workflows.

A challenge in the quality control of traditional Chinese medicine is the systematic multicomponent characterization of the compound formulae. Jiawei Fangji Huangqi, a modified form of Fangji Huangqi, is a prescription comprising seven herbal medicines. To address the chemical complexity of the Jiawei Fangji Huangqi decoction, we integrated ion mobility-quadrupole time-of-flight high-definition MSE coupled to ultra-high-performance liquid chromatography and intelligent data processing workflows available in the UNIFI software package. Good chromatographic separation was achieved on CORTECS UPLC T3 column within 52 min, and high-accuracy MS2 data were acquired using high-definition MSE in the negative and positive modes. A chemical library of 1250 compounds was created and incorporated into the UNIFI software to enable automatic peak annotation of the high-definition MSE data. We identified or tentatively characterize 430 compounds in the Jiawei Fangji Huangqi decoction. The potential superiority of high-definition MSE over conventional MS data acquisition approaches was revealed in its spectral quality (MS2 ), differentiation of isomers, separation of coeluting compounds, and target mass coverage. The multiple components of the Jiawei Fangji Huangqi decoction were elucidated, offering insight into its improved pharmacological action compared with that of the Fangji Huangqi formula.

Selecting optimal calibration samples using proximal sensing EM induction and γ-ray spectrometry data: An application to managing lime and magnesium in sugarcane growing soil.

Calcium (Ca) and magnesium (Mg) are essential for growth of sugarcane leaves and roots, as well as respiration and nitrogen metabolism, respectively. To assist farmers decide suitable application rates of lime and Mg fertiliser, respectively, the Australian sugarcane industry established the Six-Easy-Steps nutrient management guidelines based on topsoil (0-0.3 m) Ca (cmol(+) kg-1) and Mg (cmol(+) kg-1). Given the heterogeneous nature of soil, digital soil mapping (DSM) methods can be employed to allow for the precise application rate to be determined. In this study, we examine statistical models (i.e., ordinary kriging [OK], linear mixed model [LMM], quantile regression forests [QRF], support vector machine [SVM], and Cubist regression kriging [CubistRK]) to predict topsoil and subsoil (0.6-0.9) Ca and Mg, employing digital data in combination (i.e., proximal sensing electromagnetic induction (EMI) [e.g., 1mPcon, 1mHcon, etc.], gamma-ray [γ-ray] spectrometry [i.e., TC, K, U and Th] and digital elevation model [DEM] derivatives). We also investigate various sampling designs (i.e., spatial coverage [SCS], feature space coverage [FSCS], conditioned Latin hypercube [cLHS] and simple random sampling [SRS]) and calibration sample size (i.e., n = 180, 150, 120, 90, 60 and 30). The predictions are assessed using Lin's concordance correlation coefficient (LCCC) and ratio of performance to interquartile distance (RPIQ) with an independent validation dataset (i.e., n = 40). The best results were for prediction of subsoil Mg, utilising CubistRK (LCCC = 0.82) with the largest calibration sample size (n = 180), followed by LMM (0.79), SVM (0.76), QRF (0.70) and OK (0.65). This was generally the case for topsoil and subsoil Ca. We also conclude that no single sampling design was universally better, and 180 samples were necessary for predicting topsoil Ca and Mg with moderate agreement (0.65 < LCCC < 0.80). However, with FSCS, a minimum of 120 samples were enough to calibrate a CubistRK model and achieve substantial (LCCC > 0.80) agreement for predicting subsoil Ca and Mg. With respect to soil use and management according to the Six-Easy-Steps, the sandy soil in the north and south require large application rate of lime (3.5 t/ha) and Mg (125 kg/ha), respectively. Conversely, varying amounts of fertiliser rates of lime (2.0, 1.5 and 1 t/ha) and Mg (50 kg/ha) were recommended where Vertosols were previously mapped.

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy.

Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments' results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software.

Most cultivated upland areas of northeast Thailand are characterized by sandy and infertile soils, which are difficult to improve agriculturally. Information about the clay (%) and cation exchange capacity (CEC-cmol(+)/kg) are required. Because it is expensive to analyse these soil properties, electromagnetic (EM) induction instruments are increasingly being used. This is because the measured apparent soil electrical conductivity (ECa-mS/m), can often be correlated directly with measured topsoil (0-0.3 m), subsurface (0.3-0.6 m) and subsoil (0.6-0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired ECa in horizontal (ECah) and vertical (ECav) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ-mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting ECah and ECav data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between ECa and soil properties was between ECah and subsoil clay (R2 = 0.43) and subsoil CEC (R2 = 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R2 = 0.65) and CEC (R2 = 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin's = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin's = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and ECa cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth.

Comparative proteomic analysis of Nostoc flagelliforme reveals the difference in adaptive mechanism in response to different ultraviolet-B radiation treatments.

Nostoc flagelliforme is a pioneer organism in the desert and highly resistant to ultraviolet B (UV-B) radiation, while the involved adaptive mechanism has not been fully explored yet. To elucidate the responsive mechanism, two doses of UV-B radiation (low: 1 W/m2 and high: 5 W/m2) were irradiated for 6 h and 48 h, respectively, and their effects on global metabolism in N. flagelliforme were comprehensively investigated. In this study, we used iTRAQ-based proteomic approach to explore the proteomes of N. flagelliforme, and 151, 172, 124 and 148 differentially expressed proteins were identified under low and high UV-B doses for 6 h and 48 h, respectively. Functional classification analysis showed these proteins were mainly involved in photosynthesis, amino acid metabolism, antioxidant activity and carbohydrate metabolism. Further analysis revealed that UV-B imposed restrictions on primary metabolism including photosynthesis, Calvin cycle, and amino acid metabolism, and cells started defense mechanism through repair of DNA and protein damage, increasing antioxidant activity, and accumulating extracellular polysaccharides to minimize the damage. Moreover, high UV-B dose imposed more severe restrictions and activated stronger defense mechanism compared with low dose. The results would improve the understanding of molecular mechanisms of UV-B-stress adaption in N. flagelliforme.

Cloning and expression pattern of akirin2 gene in broiler.

Akirin2 is an important nuclear factor which plays functions in innate immune response, myogenesis, muscle development, and carcinogenesis. In this study, akirin2 genes were cloned from 4-day-old Sanhuang and AA(+) broiler, and its expression patterns were analyzed by RT-PCR. The results showed that there were four SNPs in the 5'-terminal region of akirin2 coding sequences. Expression profile analysis showed that the akirin2 transcripts were constitutively expressed in 15 tissues tested, and similar expression patterns were found between the two breeds of broilers. In addition, one of the interesting findings was that the akirin2 gene is highly expressed in blood and lowly expressed in heart, respectively. These data can serve as a foundation for further studying functions of akirin2 gene.

Optimizing the ecological network of resource-based cities to enhance the resilience of regional ecological networks.

Mineral extraction in resource-based cities has caused serious damage to the original ecology, resulting in poor regional vegetation growth, reduced carbon sequestration capacity, and reduced ecosystem resilience. Especially in resource-based cities with fragile ecology, the overall anti-interference ability of the environment is relatively worse. Seeking ecological network optimization solutions that can improve vegetation growth conditions on a large scale is an effective way to enhance the resilience of regional ecosystems. This paper introduces carbon sequestration indicators and designs a differential ecological networks (ENs) optimization model (FTCC model) to achieve the goal of improving ecosystem resilience. The model identifies the patches that need to be optimized and their optimization directions based on the differences in ecological function-topology-connectivity-carbon sequestration of the patches. Finally, the resilience of the ecological network before and after optimization was compared, proving that the model is effective. The results show that the sources in the Yulin ENs form three main clusters, with connectivity between clusters relying on only a few patches. The patches in the northeastern and southwest clusters are large but their ecological functions need to be improved. After optimization, 16 new stepping stones were added, 38 new corridors were added, and the ecological function of 39 patches was enhanced. The optimized ecological network resilience was improved in terms of structure, function, and carbon sinks, and carbon sinks increased by 6364.5 tons. This study provides a reference for measures to optimize landscape space and manage ecosystem resilience enhancement in resource-based cities.

Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast.

Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods' time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model's computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Four New Species of Tomentella (Thelephorales, Basidiomycota) from Subtropical Forests in Southwestern China.

Species of the basidiomycetous genus Tomentella are widely distributed throughout temperate forests. Numerous studies on the taxonomy and phylogeny of Tomentella have been conducted from the temperate zone in the Northern hemisphere, but few have been from subtropical forests. In this study, four new species, T. casiae, T. guiyangensis, T. olivaceomarginata and T. rotundata from the subtropical mixed forests of Southwestern China, are described and illustrated based on morphological characteristics and phylogenetic analyses of the internal transcribed spacer regions (ITS) and the large subunit of the nuclear ribosomal RNA gene (LSU). Molecular analyses using Maximum Likelihood and Bayesian analysis confirmed the phylogenetic positions of these four new species. Anatomical comparisons among the closely related species in phylogenetic and morphological features are discussed. Four new species could be distinguished by the characteristics of basidiocarps, the color of the hymenophoral surface, the size of the basidia, the shape of the basidiospores and some other features.

Ferulic acid as a therapeutic agent in depression: Evidence from preclinical studies.

Depression is a common but severe mood disorder with a very high prevalence across the general population. Depression is of global concern and poses a threat to human physical and mental health. Ferulic acid (FA) is a natural active ingredient that has antioxidative, anti-inflammatory, and free radical scavenging properties. Furthermore, studies have shown that FA can exert antidepressant effects through a variety of mechanisms. The aim of the review was to comprehensively elucidate the mechanisms in FA that alleviate depression using animal models. The in vivo (animal) studies on the mechanism of FA treatment of depression were searched in PubMed, Chinese National Knowledge Infrastructure, Baidu academic, and Wan fang databases. Thereafter, the literature conclusions were summarized accordingly. Ferulic acid was found to significantly improve the depressive-like behaviors of animal models, suggesting that FA is a potential natural product in the treatment of depression. The mechanisms are achieved by enhancing monoamine oxidase A (MOA) activity, inhibiting microglia activation and inflammatory factor release, anti-oxidative stress, promoting hippocampal nerve regeneration, increasing brain-derived neurotrophic factor secretion, regulating gut microbiome, and activating protein kinase B/collapsin response mediator protein 2 (AKT/CRMP2) signaling pathway. Ferulic acid produces significant antidepressant effects in animal depression models through various mechanisms, suggesting its potential value as a treatment of depression. However, clinical research trials involving FA are required further to provide a solid foundation for its clinical application.

Alginate-based edible coating with oregano essential oil/ß-cyclodextrin inclusion complex for chicken breast preservation.

A sodium alginate (SA) edible coating containing oregano essential oil (OEO)/ß-cyclodextrin (ß-CD) inclusion complexes (SA/OEO-MP coating) was developed to extend the shelf life of fresh chicken breast during refrigeration storage. First, OEO was inserted into the hydrophobic interior of ß-CD to form an inclusion complex (OEO-MP) that maintained its excellent antioxidant and antibacterial activities. The formed OEO-MP was characterized using fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). In addition, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) results demonstrated that ß-CD could improve the thermal stability of OEO. The encapsulation efficiency reached 71.6 %, and OEO was released continuously from the OEO-MP. The lipid oxidation, total viable count (TVC) and sensory properties of chicken breasts were regularly monitored when OEO-MP was incorporated into the SA coating for chicken breast preservation. Compared with the uncoated group, the SA/OEO-MP-coated groups showed significantly reduced increases in pH, thiobarbituric acid reactive substances (TBARS), total volatile base nitrogen (TVB-N), and TVC, especially in the SA/OEO-MP1 group. In summary, the SA/OEO-MP coating could preserve the chicken breast by reducing lipid oxidation and inhibiting the proliferation of microorganisms. It would be developed as a prospective edible packaging for chicken preservation.

Research on the coupling coordination and driving role of urbanization and ecological resilience in the middle and lower reaches of the Yangtze River.

Background: The growth of urbanization in the 20th and 21st centuries has resulted in unprecedented ecological security issues. The imbalance between urban development and internal ecological security is a growing concern. Methods: Based on the urban development process and the characteristics of ecosystem resilience, the corresponding urbanization evaluation system ("scale-structure-benefit") and ecosystem resilience assessment model ("resistance-adaptability-restoring") were constructed to explore the changes in each dimension as well as to analyze the spatial and temporal changes and driving effects of the coupled coordination level of urbanization and ecological resilience using the coupled coordination degree (CCD) model and geographically and temporally weighted regression (GTWR). Results: (1) From 2005 to 2020, urbanization levels increased (from 0.204 to 0.264, respectively), whereas the level of ecological resilience gradually decreased (from 0.435 to 0.421, respectively). The spatial distribution of urbanization is rather steady, with a "high-northeast low-southwest" pattern of regional distribution; however, the spatial distribution pattern of ecological resilience is essentially the reverse. (2) During the study period, there was an improvement in the level of coordination between urbanization and ecological resilience, with an increase from 0.524 to 0.540. However, the main coordination type remained the same, with over 46% being at the basic coordination stage. The relative development type was dominated by the lagging urbanization stage, with the lagging ecological resilience and synchronous development stages accounting for a smaller proportion, and the space was distributed in a block-like cluster. (3) The running results of the GTWR show that the core dimensions of the whole region are scale, benefit, and structure, and the impact of each dimension shows obvious spatial heterogeneity. Cities with different levels of relative development also have different central dimensions. This research will provide support for the coordination of urban development in areas where economic construction and ecological resilience are not coordinated, and will contribute to the sustainable development of urban areas.

Spatiotemporal heterogeneity between agricultural carbon emission efficiency and food security in Henan, China.

It is significant to investigate the coupling and coordination between agricultural carbon emission efficiency (ACEE) and food security and to achieve peak carbon dioxide emissions and carbon neutrality in agriculture as early as possible while ensuring national food security. The Super-SBM (slack-based model) and the comprehensive index method were used to measure the ACEE and food security level in Henan province from 2010 to 2020. The coupling coordination degree (CCD) and the relative state of ACEE and food security were analyzed using the coupling coordination degree model (CCDM) and the relative development degree model (RDDM). In addition, the interaction between ACEE and food security and the spatial-temporal heterogeneity were analyzed by combining with the geographically and temporally weighted regression (GTWR) model. The results showed that: Firstly, the overall level of ACEE was high, and the spatial heterogeneity of ACEE was significant. The spatial pattern of food security is relatively stable, with high levels in the south and low levels in the north. Secondly, The CCD between ACEE and food security in Henan province generally shows a decreasing trend. In the spatial dimension, the CCD between ACEE and food security in Henan province exhibits a spatial divergence characteristic of low in the center and high in the north and south, with significant regional variations. Finally, there is spatial and temporal heterogeneity between ACEE and food security. The regression coefficients differ significantly among different cities, the regression coefficients do not show a consistent positive or negative correlation, and the regression coefficients are distributed both positively and negatively. This study serves as a guide for achieving the goal of double carbon in agriculture and ensuring food security.

Preparation of a Tetra-Imidazolium Salt and Sensing for p-Dinitrobenzene.

A macrocyclic tetra-imidazolium salt (2) based on quinoxaline was prepared and characterized. The recognition of 2 to nitro compounds was investigated by fluorescence spectroscopy, 1H NMR titrations, MS, IR spectroscopy, and UV/vis spectroscopy. The results displayed that 2 was able to effectively differentiate p-dinitrobenzene from other nitro compounds via the fluorescence method.

Analysis of Noise-Detection Characteristics of Electric Field Coupling in Quartz Flexible Accelerometer.

The internal electric field coupling noise of a quartz flexible accelerometer (QFA) restricts the improvement of the measurement accuracy of the accelerometer. In this paper, the internal electric field coupling mechanism of a QFA is studied, an electric field coupling detection noise model of the accelerometer is established, the distributed capacitance among the components of the QFA is simulated, the structure of the detection noise transfer system of different carrier modulation differential capacitance detection circuits is analyzed, and the influence of each transfer chain on the detection noise is discussed. The simulation results of electric field coupling detection noise show that the average value of detection noise can reach 41.7 µg, which is close to the effective resolution of the QFA, 50 µg. This confirms that electric field coupling detection noise is a non-negligible factor affecting the measurement accuracy of the accelerometer. A method of adding a high-pass filter to the front of the phase-shifting circuit is presented to suppress the noise of electric field coupling detection. This method attenuates the average value of the detected noise by about 78 dB, and reduces the average value of the detected noise to less than 0.1 µg, which provides a new approach and direction for effectively breaking through the performance of the QFA.

Artificial intelligence can accurately distinguish IgA nephropathy from diabetic nephropathy under Masson staining and becomes an important assistant for renal pathologists.

Introduction: Hyperplasia of the mesangial area is common in IgA nephropathy (IgAN) and diabetic nephropathy (DN), and it is often difficult to distinguish them by light microscopy alone, especially in the absence of clinical data. At present, artificial intelligence (AI) is widely used in pathological diagnosis, but mainly in tumor pathology. The application of AI in renal pathological is still in its infancy. Methods: Patients diagnosed as IgAN or DN by renal biopsy in First Affiliated Hospital of Zhejiang Chinese Medicine University from September 1, 2020 to April 30, 2022 were selected as the training set, and patients who diagnosed from May 1, 2022 to June 30, 2022 were selected as the test set. We focused on the glomerulus and captured the field of the glomerulus in Masson staining WSI at 200x magnification, all in 1,000 × 1,000 pixels JPEG format. We augmented the data from training set through minor affine transformation, and then randomly split the training set into training and adjustment data according to 8:2. The training data and the Yolov5 6.1 algorithm were used to train the AI model with constant adjustment of parameters according to the adjusted data. Finally, we obtained the optimal model, tested this model with test set and compared it with renal pathologists. Results: AI can accurately detect the glomeruli. The overall accuracy of AI glomerulus detection was 98.67% and the omission rate was only 1.30%. No Intact glomerulus was missed. The overall accuracy of AI reached 73.24%, among which the accuracy of IgAN reached 77.27% and DN reached 69.59%. The AUC of IgAN was 0.733 and that of DN was 0.627. In addition, compared with renal pathologists, AI can distinguish IgAN from DN more quickly and accurately, and has higher consistency. Discussion: We constructed an AI model based on Masson staining images of renal tissue to distinguish IgAN from DN. This model has also been successfully deployed in the work of renal pathologists to assist them in their daily diagnosis and teaching work.

The efficacy of acupuncture combined with other therapies in post stroke cognitive impairment: A network meta-analysis.

BACKGROUND: The network meta-analysis was used to evaluate the efficacy of acupuncture combined with other therapies in the treatment of post stroke cognitive impairment (PSCI). METHODS: The China National Knowledge Infrastructure, Wanfang DATA, Vip Chinese Periodic Service Platform, PUBMED, Cochrane Library, Web of Science, and Embase were searched for randomized controlled trials (RCTs) published before March 18, 2023. Two researchers independently reviewed articles and extracted data, and then qualified papers were included in the study. STATA 14.0 was used for network meta-analysis. RESULTS: A total of 29 articles including 2241 patients were included in this study. The treatment of the intervention group includes acupuncture combined with traditional Chinese medicine prescriptions (TCMP), acupuncture combined with hyperbaric oxygen (HBO), acupuncture combined with repetitive transcranial magnetic stimulation (rTMS), acupuncture combined with cognitive rehabilitation (CR), acupuncture combined with donepezil. The intervention of the control group includes acupuncture, HBO, rTMS, CR, TCMP, and donepezil. In terms of improving the score of Minimum Mental State Examination (MMSE), acupuncture combined with TCMP was most likely to be the best treatment (P < .05). In terms of improving the score of Montreal Cognitive Assessment (MoCA), acupuncture combined with TCMP was most likely to be the best treatment (P < .05). In terms of improving the total effective rate of clinical treatment, acupuncture combined with rTMS was most likely to be the best treatment (P < .05). CONCLUSION: Acupuncture combined with TCMP may be the best treatment method among all of the above treatments for PSCI.

Activation Enhancement and Grain Size Improvement for Poly-Si Channel Vertical Transistor by Laser Thermal Annealing in 3D NAND Flash.

The bit density is generally increased by stacking more layers in 3D NAND Flash. Lowering dopant activation of select transistors results from complex integrated processes. To improve channel dopant activation, the test structure of vertical channel transistors was used to investigate the influence of laser thermal annealing on dopant activation. The activation of channel doping by different thermal annealing methods was compared. The laser thermal annealing enhanced the channel activation rate by at least 23% more than limited temperature rapid thermal annealing. We then comprehensively explore the laser thermal annealing energy density on the influence of Poly-Si grain size and device performance. A clear correlation between grain size mean and grain size sigma, large grain size mean and sigma with large laser thermal annealing energy density. Large laser thermal annealing energy density leads to tightening threshold voltage and subthreshold swing distribution since Poly-Si grain size regrows for better grain size distribution with local grains optimization. As an enabler for next-generation technologies, laser thermal annealing will be highly applied in 3D NAND Flash for better device performance with stacking more layers, and opening new opportunities of novel 3D architectures in the semiconductor industry.

A multidimensional chromatography/high-resolution mass spectrometry approach for the in-depth metabolites characterization of two Astragalus species.

To address the chemical complexity is indispensable in a number of research fields. Herb metabolome is typically composed by more than one class of structure analogs produced via different biosynthetic pathways. Multidimensional chromatography (MDC), due to the greatly enhanced separation space, offers the potential solution to comprehensive characterization of herbal metabolites. Here, we presented a strategy, by integrating MDC and quadrupole time-of-flight mass spectrometry (QTOF-MS), to accomplish the in-depth herbal metabolites characterization. Using the metabolome of two Astragalus species (A. membranaceus var. mongholicus,AMM; A. membranaceus, AM) as the case, an off-line three-dimensional liquid chromatography (3D-LC) system was established: hydrophilic interaction chromatography using an XAmide column as the first dimension (1D) for fractionating the total extract, on-line reversed-phase × reversed-phase liquid chromatography separately configuring a CSH Fluoro-Phenyl column and a Cosmocore C18 column as the second dimension (2D) and the third dimension (3D) of chromatography to enable the explicit separation of three well fractionated samples. Moreover, the negative-mode collision-induced dissociation by QTOF-MS under the optimized condition could provide diversified fragments that were useful for the structural elucidation of AMM and AM. An in-house library (composed by 247 known compounds) and comparison with 43 reference standards were utilized to assist more reliable characterization. We could characterize 513 compounds from two Astragalus species (344 from AMM and 323 from AM), including 236 flavonoids, 150 triterpenoids, 18 organic acids, and 109 others. Conclusively, the established MDC approach gained excellent performance favoring the analogs-oriented in-depth characterization of herbal metabolites, but received uncompromising analytical efficiency.