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An efficient method for the synthesis of indolo[2,1-a]isoquinolines using calcium carbide as an alkyne source, 2-(2-bromophenyl)-1H-indoles as starting materials, and copper as a catalyst is described. The target products are synthesized via Sonogashira cross-coupling/nucleophilic addition tandem reactions. The advantages of this protocol include the use of inexpensive and easy-to-handle solid alkyne source instead of flammable and explosive gaseous acetylene, cheap and readily available raw materials, wide range of substrates, and simple reaction procedure. The method can also be extended to gram scale. In addition, the desired product can also be obtained by one-pot three-component reaction of phenylhydrazine hydrochlorides, o-bromoacetophenones and calcium carbide. [ABSTRACT FROM AUTHOR]

This paper presents a low-distortion and low-power multi-stage noise-shaping (MASH) architecture Sigma-Delta (Σ Δ) modulator with double-sampling. The modulator uses a cascade of integrator feed-forward (CIFF) structure as a cascaded modulator in a MASH architecture. Since the CIFF structure provides a feedforward path from the input signal to the quantizer, the input signal is directly transmitted to the output without loop processing, relaxing the linearity requirement of the modulator, reducing performance requirements for the OTA and harmonic distortion. Double-sampling technique is adopted in the first integrator to double the sampling rate, improving the accuracy of the modulator without the increase of power consumption. In order to eliminate the influence of capacitor mismatch in the sampling network, a fully floating differential switched capacitor (SC) feedback digital-to-analog (DAC) is used in the integrator. The simulation results show that the Σ Δ modulator achieves 113 dB signal-to-noise and distortion ratio (SNDR) and the power consumption is 3.15 mW from a 5 V power supply. [ABSTRACT FROM AUTHOR]

Citrus fiber, a by-product of citrus processing that has significant nutritional and bioactive properties, has gained attention as a promising raw material with extensive developmental potential in the food, pharmaceutical, and feed industries. However, the lack of in-depth understanding regarding citrus fiber, including its structure, modification, mechanism of action, and potential applications is holding back its development and utilization in functional foods and drugs. This review explores the status of extraction methods and modifications applied to citrus fiber to augment its health benefits. With the aim of introducing readers to the potential health benefits of citrus fibers, we have placed special emphasis on their regulatory mechanisms in the context of various conditions, including type 2 diabetes mellitus, cardiovascular disease, obesity, and cancer. Furthermore, this review highlights the applications and prospects of citrus fiber, aiming to provide a theoretical basis for the utilization and exploration of this valuable resource. [ABSTRACT FROM AUTHOR]

Host-microbe interaction and homeostasis are essential for the maintenance of host health. These microbes mainly include gut, skin, and oral microbes. The strong association between gut microbes and host health and disease has long been established. Notably, recent studies have found that changes in host health are largely influenced by factors of oral origin (e.g., microorganisms, saliva, and diet), and these findings support the possibility that oral microorganisms may be an important target for intervention in host health. Therefore, several studies have proposed the concepts of the oral-gut, the -brain, and -liver axes. However, the interaction between the oral microbiome and host health has not been elaborated in detail. In this review, the distribution and spatial structure of microorganisms in different oral tissues were introduced, and the effects of microorganisms on oral health were analyzed. Based on the oral-gut and -brain axes, the mechanism of oral microorganisms affecting host health and diseases, including neurodegenerative and liver diseases, was expounded. Therefore, food intervention strategies (e.g., Probiotics, Polyphenol, Vitamins, Toothpaste, etc.) to target oral microorganisms and protect host health are proposed. • Diets and prebiotics have a significant effect in regulating oral microbiota. • The oral microbiome has been confirmed to be related to many diseases. • Food intervention strategies would be a new way to improve oral health. [ABSTRACT FROM AUTHOR]

The rate of obesity is rapidly increasing and has become a health and economic burden worldwide. As recent studies have revealed that the gut microbiota is closely linked to obesity, researchers have used various approaches to modulate the gut microbiota to treat the condition. Dietary composition and energy intake strongly affect the composition and function of the gut microbiota. Intestinal microbial changes alter the composition of bile acids and fatty acids and regulate bacterial lipopolysaccharide production, all of which influence energy metabolism and immunity. Evidence also suggests that remodeling the gut microbiota through intake of probiotics, prebiotics, fermented foods, and dietary plants, as well as by fecal microbiota transplantation, are feasible methods to remediate obesity. [ABSTRACT FROM AUTHOR]

The role of programmed death ligand 1 (PD-L1) has been extensively investigated in adaptive immune system. However, increasing data show that innate immune responses are also affected by the immune checkpoint molecule. It has been demonstrated that regulation of PD-L1 signaling in macrophages may be a potential therapeutic method for acute respiratory distress syndrome (ARDS). However, the PD-L1 expression pattern in local macrophages and whole lung tissues remains mysterious, hindering optimization of the potential treatment program. Therefore, we aim to determine the PD-L1 expression pattern during ARDS. Our findings show that PD-L1 levels are markedly increased in lipopolysaccharide (LPS)-stimulated lung tissues, which might be attributable to an increase in the gene expression by immune cells, including macrophages and neutrophils. In vitro experiments are performed to explore the mechanism involved in LPS-induced PD-L1 production. We find that PD-L1 generation is controlled by transcription factors early growth response 1 (Egr-1) and CCAAT/enhancer binding protein delta (C/EBPδ). Strikingly, PD-L1 production is enhanced by phosphoinositide-3 kinase (PI3K)–protein kinase B (AKT) signaling pathway via up-regulation of Egr-1 and C/EBPδ expressions. Additionally, we observe that expressions of Egr-1 and C/EBPδ mutually reinforce each other. Moreover, we observe that PD-L1 is protective for ARDS due to its regulatory role in macrophage-associated inflammatory response. In summary, during LPS-induced ARDS, PD-L1 expression, which is beneficial for the disease, is increased via the PI3K-AKT1-Egr-1/C/EBPδ signaling pathway, providing theoretical basis for application of methods controlling PD-L1 signaling in macrophages for ARDS treatment in clinic. [ABSTRACT FROM AUTHOR]

The implementation of static artificial magnetic fields in ultracold atomic systems has become a powerful tool, e.g. for simulating quantum-Hall physics with charge-neutral atoms. Taking an interacting bosonic flux ladder as a minimal model, we investigate protocols for adiabatic state preparation via magnetic flux ramps. Considering the fact that it is actually the artificial vector potential (in the form of Peierls phases) that can be experimentally engineered in optical lattices, rather than the magnetic field, we find that the time required for adiabatic state preparation dramatically depends on which pattern of Peierls phases is used. This can be understood intuitively by noting that different patterns of time-dependent Peierls phases that all give rise to the same magnetic field ramp, generally lead to different artificial electric fields during the ramp. As an intriguing result, we find that an optimal choice allows for preparing the ground state almost instantaneously in the non-interacting system, which can be related to the concept of counterdiabatic driving. Remarkably, we find extremely short preparation times also in the strongly-interacting regime. Our findings open new possibilities for robust state preparation in atomic quantum simulators. [ABSTRACT FROM AUTHOR]

The present study aimed to explore the neural-related mechanism of the creative evaluation process in individual with different levels of creativity. The time course of evaluation process for creative objects was monitored by event-related potentials (EPRs). Forty-five participants were divided into the high-creativity performance (HCP) group and the low-creativity performance (LCP) group and then completed a creative object evaluation task. The behavioral results revealed that the HCP group showed higher accuracy rates on the creative object evaluation task than the LCP group did. The ERPs analysis demonstrated that in the early stage of creative object processing, ordinary objects elicited a more negative anterior N2 at 190–340 ms than the creative objects did in the LCP group. In the late stage of creative object processing, creative objects elicited a more positive ERPs deflection in the HCP group than in the LCP group at 350–500 ms and at 500–1000 ms. Taken together, these results indicate that different creativity performance individuals may express different attention patterns or top-down processing strategy in the early stage of creative object evaluation processing; and high-creativity performance individuals can better utilize both cognitive and non-cognitive evaluation processing in the late stage, to process the creative object effectively. [ABSTRACT FROM AUTHOR]

Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F 1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE. [ABSTRACT FROM AUTHOR]

In this era of big data, stream data classification which is one of typical data stream applications has become more and more significant and challengeable. In these applications, it is obvious that data classification is much more frequent than model training. The ratio of stream data to be classified is rapid and time-varying, so it is an important problem to classify the stream data efficiently with high throughput. In this paper, we first analyze and categorize the current data stream machine learning algorithms according to their data structures. Then, we propose stream data classification topology (SDC-Topology) on Storm. For the classification algorithms based on the matrix, we propose self-adaptive stream data classification framework (SASDC-Framework) for efficient stream data classification on Storm. In SASDC-Framework, all the data sets arriving at the same unit time are partitioned into subsets with the nearly best partition size and processed in parallel. To select the nearly best partition size for the stream data sets efficiently, we adopt bisection method strategy and inverse distance weighted strategy. Extreme learning machine, which is a fast and accurate machine learning method based on matrix calculating, is used to test the efficiency of our proposals. According to evaluation results, the throughputs based on SASDC-Framework are 8–35 times higher than those based on SDC-Topology and the best throughput is more than 40000 prediction requests per second in our environment. [ABSTRACT FROM AUTHOR]

Pectin is a complex and functionally rich natural plant polysaccharide that is widely used in food, medical, and cosmetic industries. It can be modified to improve its properties and expand its applications. Modification methods for natural pectin can be divided into physical, chemical, enzymatic, and compound methods. Different modification methods can result in modified pectins (MPs) exhibiting different physicochemical properties and biological activities. The objectives of this paper were to review the various pectin modification methods explored over the last decade, compare their differences, summarize the impact of different modification methods on the biological activity and physicochemical properties of pectin, and describe the applications of MPs in food and pharmaceutical fields. Finally, suggestions and perspectives for the development of MPs are discussed. This review offers a theoretical reference for the rational and efficient processing of pectin and the expansion of its applications. [Display omitted] • The modification methods of pectin were comprehensively summarized and discussed. • The relationship between structure and bioactivity of modified pectin was analyzed. • Modifications may broaden the scope of pectin applications in areas such as food and medicine. • Structural effects and properties-activity relationships of modified pectins need to be investigated. [ABSTRACT FROM AUTHOR]

Allergic rhinitis (AR) is a type of respiratory disease closely associated with chronic inflammation. Esculetin is a natural coumarin derivative and has been reported to possess anti‐allergic and anti‐inflammatory effects. However, the roles of esculetin in AR have not been studied. In this study, we aimed to examine the effect of esculetin on AR using an in vitro model. The human nasal epithelial cells (HNEpC) were stimulated by histamine for 24 hours with or without the pretreatment of esculetin. The mRNA levels and production of inflammatory cytokines including IL‐6 and IL‐8, as well as mucin 5AC (MUC5AC) were measured using qRT‐PCR and ELISA, respectively. The results showed that esculetin suppressed histamine‐induced expression and secretion of IL‐6, IL‐8, and MUC5AC in HNEpCs. Furthermore, we examined the effect of esculetin on NF‐κB pathway by detecting the expression levels of NF‐κB p65, p‐p65 and IκBα using western blot analysis. Esculetin treatment suppressed the histamine‐induced p‐p65 expression and p‐IκBα degradation. Inhibiting NF‐κB pathway suppressed histamine‐induced production of IL‐6, IL‐8, and MUC5AC in HNEpCs. These findings suggested that esculetin suppressed histamine‐induced production of inflammatory cytokines and mucin in HNEpCs, which were partly mediated by the inhibition of NF‐κB pathway. [ABSTRACT FROM AUTHOR]

To investigate the difference in texture features on diffusion weighted imaging (DWI) images between breast benign and malignant tumors. Patients including 56 with mass-like breast cancer, 16 with breast fibroadenoma, and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study, and allocated to the benign group (20 patients) and the malignant group (56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images, and five characteristic parameters including Angular Second Moment (ASM), Contrast, Correlation, Inverse Difference Moment (IDM), and Entropy were calculated. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis, and receiver operating characteristic curve (ROC) analysis was carried out to evaluate the diagnostic efficiency. The texture features ASM, Contrast, Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups (P ASM =0.014, P contrast =0.019, P correlation =0.010, P entropy =0.007). The area under the ROC curve was 0.685, 0.681, 0.754, and 0.683 respectively for the positive texture variables mentioned above, and that for the combined variables (ASM, Contrast, and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM, Contrast and Entropy were considered as the specific imaging variables for the differential diagnosis of breast benign and malignant tumors. The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors. [ABSTRACT FROM AUTHOR]

To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system (CNS) lymphoma. The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging (T2WI) was performed by use of GLCM texture plugin of Image J software, and the texture parameters including Angular Second Moment (ASM), Contrast, Correlation, Inverse Difference Moment (IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic (ROC) curve was plotted to compare the diagnostic efficacy. The conventional imaging features including cystic and necrosis changes (P = 0.000), 'Rosette' changes (P = 0.000) and 'incision sign' (P = 0.000), except 'flame-like edema' (P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma (P = 0.006, 0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables (Contrast, cystic and necrosis, 'Rosette' changes, and 'incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, 'Rosette' changes and 'incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. The texture features of T2WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma. [ABSTRACT FROM AUTHOR]

Due to the popularity of 3D videos, 2D-to-3D video conversion has become a hot research topic for the past few years. The most critical issue in 3D video synthesis is the estimation of depth maps for the video frames. Numerous efforts have been devoted in fully automatic and semi-automatic depth estimation approaches, although the discontinuity of depth field and the ambiguity of motion boundary are still the main challenges in depth estimation. This paper proposes a semi-automatic structure-aware sparse-to-dense depth estimation method, which leverages the tensor voting at two different levels to propagate depth across frames. In the first level, a 4D tensor voting is performed to remove outliers caused by inaccurate motion estimation. Noticing that the 4D tensors of correctly matched points should lie on the smooth layer in the manifold, we utilize the variety saliency defined by the eigen-system of the tensor for outlier removal. In the second level, a high-dimensional tensor voting algorithm, incorporating spatial location, motion, and color into the tensor representation, is devised to propagate the depth from the sparse points to the entire image domain. By projecting the input feature into the tangent space, the relation between the location, motion, color, and the depth can be established by voting process. Extensive experiments on public data set validate the effectiveness of the proposed method in comparison with state-of-the-art depth estimation approaches. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • TGF-β1 induces time- and dose- dependently miR-217 downregulation in ASMCs. • miR-217 inhibits TGF-β1-induced ASMC proliferation and migration. • ZEB1 is identified as a direct target for miR-217 in ASMC. • Restoration of ZEB1 reverses miR-217-mediated ASMC proliferation and migration. Abstract Aberrant proliferation and migration of airway smooth muscle cells (ASMCs) contribute to the pathogenesis of airway remodeling during asthma development. Here, the potential function of microRNA-217 (miR-217) on the cell proliferation and migration of TGF-β1-induced ASMCs and the involved mechanisms were investigated in this study. We found that miR-217 expression was apparently downregulated in a time and dose dependent characteristic in ASMCs exposed to transforming growth factor-β (TGF-β1) stimulation. Overexpression of miR-217 significantly inhibited TGF-β1-induced proliferation, migration, extracellular matrix (ECM) deposition, but promoted apoptosis in ASMCs, whereas, miR-217 inhibitor showed an opposed effect. Bioinformatics analyses revealed that the 3'-untranslated region (UTR) of ZEB1 was a potential target for miR-217, which was further confirmed by luciferase activity, qRT-PCR and western blot assay. In addition, rescue experiment also displayed that restoration of ZEB1 expression partially abrogated the inhibitory effect of miR-217 on TGF-β1-induced proliferation and migration in ASMCs. By chromatin immunoprecipitation (ChIP) assay, we further confirmed that the binding of ZEB1 to the fibronectin promoter in TGF-β1-treated ASMCs was reduced by miR-217 overexpression. Therefore, our findings suggested the potential protective role of miR-217 on the attenuation of cell proliferation and migration was through targeting ZEB1 in TGF-β1-stimulated ASMCs. [ABSTRACT FROM AUTHOR]

A sensitivity enhanced surface plasmon resonance (SPR) immunosensor based on Graphene oxide (GO) composite and staphylococcal protein A (SPA) co-modified photonic crystal fiber (PCF) is developed for human IgG detection. The PCF spliced between two section of multimode fibers (MMFs) is sputtered with gold film, which was then modified with graphene oxide and SPA for further immunosensing. Graphene oxide with abundant functional groups has a large surface area for more biomolecules immobilization and can enhance the interaction between Au film and external medium, leading to the sensitivity increased greatly. SPA can specifically bind the Fc region of the antibodies and has a high degree of orientation for capturing antibodies. Experimental results indicated that the refractive index (RI) sensitivity of graphene oxide modified Au-PCF SPR sensor reaches 4649.8 nm/RIU, which is about 1888 nm/RIU higher than that without graphene oxide film and the human IgG detection limit reaches as low as 10 ng/mL. Such a sensitivity enhanced immunosensor based on graphene oxide and SPA co-modified PCF shows a great promise in biosensing. [ABSTRACT FROM AUTHOR]

Micro/mini‐light emitting diode (LED) arrays are regarded as the most promising next generation of display devices. However, the inherent Lambertian radiation of LED chips results in a significant pixel crosstalk. To improve the display quality, high‐quality micro/mini‐LED display pixels with fully enclosed composite micro/nano‐packages and in situ nanoimprint technology are proposed and demonstrated in this work. The nanoimprint template is designed as a composite structure comprising an intermediate sandwich cavity array and thin‐bottom nanostructure layer. Contact is maintained between the template and LED chips array substrate. By vacuuming the sealed sandwich cavities, the thin‐bottom nanostructure layer is deformed to drive the upward protrusion of the liquid optical polymer. Moreover, the nanocavities on the thin nanostructure layer are fully filled by the polymer, forming a composite micro/nano‐package. The characteristics of the composite micro/nano‐package, such as the aperture and protrusion height, can be flexibly adjusted by changing the template structure or process parameters. A 6 × 8 array LED chips display device enclosed by a composite micro/nanopackage is developed. The divergence angle of the single LED chip decreases to 72° from the original 138°, the light‐extraction efficiency increases18.4%, and the luminescence spectrum and current‐voltage characteristics changes only minimally. [ABSTRACT FROM AUTHOR]

An efficient method for the construction of 2‐methyl‐1‐sulfonylindoles and 2,3‐dimethyl‐1‐sulfonylindoles using solid calcium carbide as an alkyne source and o‐sulfonamidobenzaldehyde hydrazones or o‐sulfonamidoacetophenone hydrazones as starting materials is described. This protocol has the salient feature of the use of an inexpensive, abundant and easy‐to‐handle solid alkyne source, effectively avoiding the use of flammable and explosive gaseous acetylene as an original alkyne source. Noble‐metal‐free conditions, wide substrate scope, high yield, and simple workup procedures are advantages of this method. The transformation can also be carried out on a gram scale. [ABSTRACT FROM AUTHOR]

In this paper, a sensitive-enhanced single-mode fiber—tapered hollow core fiber—single-mode fiber Mach–Zehnder interferometer is demonstrated for refractive index sensing. The sensitivity was improved by forming an up-taper at the two splicing joints and concave cone in hollow core fiber. The up-tapered regions served as a more effective mode splitter/combiner, and the tapered hollow core fiber was used to generate a stronger evanescent field to enhance the interaction of light with the analyte. According to the principles of interference between the cladding and fundamental modes, we performed refractive index measurements. The experiments indicated that the proposed sensor has a high refractive index sensitivity of 214.97 nm/RIU in the refractive index range of 1.333–1.379, with a minimum refractive index measurement resolution of 9.3 × 10−5. In addition, the sensor had a low temperature response of 2.96 pm/°C in the range from 50 to 85°C and a low cross sensitivity of 1.377 × 10−5RIU/°C. The proposed sensor is attractive for its high refractive index sensitivity, easy fabrication, low cross sensitivity, and good mechanical strength, making it of potential value for refractive index measurements for chemical and biological sensing. [ABSTRACT FROM PUBLISHER]

A compact and high sensitivity refractive index sensor based on a photonic crystal fibre Mach–Zehnder mode–mode interferometer is proposed. The sensing part is formed by in fibre SMF-PCF-SMF structure (SMF: single-mode fibre; PCF: photonic crystal fibre) using fusion splicing method. The fully collapse air holes of photonic crystal fibre make coupling of fibre core and cladding mode in the splicing collapse region which establish a Mach–Zehnder interferometer. The Mach–Zehnder interferometers with different photonic crystal fibre length are fabricated to investigate refractive index sensing characteristics. The refractive index measuring sensitivity can reach 224.2 nm/RIU (RIU: Refractive Index Unit) with a length of PCFL = 4 cm, experimentally. The proposed refractive index sensor is attractive due to its simple production process, compact size and high sensitivity. [ABSTRACT FROM PUBLISHER]

This paper presents a transductive multicomponent video segmentation algorithm, which is capable of segmenting the predefined object of interest in the frames of a video sequence. To ensure temporal consistency, a temporal coherent parametric min-cut algorithm is developed to generate segmentation hypotheses based on visual cues and motion cues. Furthermore, each hypothesis is evaluated by an energy function from foreground resemblance, foreground/background divergence, boundary strength, and visual saliency. In particular, the state-of-the-art R-convolutional neural network descriptor is leveraged to encode the visual appearance of the foreground object. Finally, the optimal segmentation of the frame can be attained by assembling the segmentation hypotheses through the Monte Carlo approximation. In particular, multiple foreground components are built to capture the variances of the foreground object in shapes and poses. To group the frames into different components, a tree-structured graphical model named temporal tree is designed, where visually similar and temporally coherent frames are arranged in branches. The temporal tree can be constructed by iteratively adding frames to the active nodes by probabilistic clustering. In addition, each component, consisting of frames in the same branch, is characterized by a support vector machine classifier, which is learned in a transductive fashion by jointly maximizing the margin over the labeled frames and the unlabeled frames. As the frames from the same video sequence follow the same distribution, the transductive classifiers achieve stronger generalization capability than inductive ones. Experimental results on the public benchmarks demonstrate the effectiveness of the proposed method in comparison with other state-of-the-art supervised and unsupervised video segmentation methods. [ABSTRACT FROM PUBLISHER]

Creative objects differ from ordinary objects in that they are created by human beings to contain novel, creative information. Previous research has demonstrated that ordinary object processing involves both a perceptual process for analyzing different features of the visual input and a higher-order process for evaluating the relevance of this visual information. However, it is unclear how and when these processes are influenced by the creative information of the object. This study utilized event-related potentials (ERPs) to investigate the time course of creative object processing. Behavioral results revealed that participants spent more time processing creative objects than they did ordinary objects. Analysis of scalp ERPs further revealed that creative objects elicited a more negative ERP deflection between 190 and 340 ms (N190–340) with an anterior scalp distribution. Additionally, creative objects elicited more positive ERP deflection than did ordinary objects between 400 and 700 ms (P400–700) with a right centro–parietal scalp distribution and between 700 and 1000 ms (late positive component) with a right anterior–central scalp distribution. Such results suggest that the processing of creative objects is composed of two distinct stages. The early perceptual stage involves the detection of visual differences exhibited by the creative objects, while the late stage involves the right-lateralized processes of understanding and encoding the creative information. [ABSTRACT FROM AUTHOR]

Hyaluronic acid (HA) is key to the stability of the internal environment of tissues. HA content in tissues gradually decreases with age, causing age-related health problems. Exogenous HA supplements are used to prevent or treat these problems including skin dryness and wrinkles, intestinal imbalance, xerophthalmia, and arthritis after absorption. Moreover, some probiotics are able to promote endogenous HA synthesis and alleviate symptoms caused by HA loss, thus introducing potential preventative or therapeutic applications of HA and probiotics. Here, we review the oral absorption, metabolism, and biological function of HA as well as the potential role of probiotics and HA in increasing the efficacy of HA supplements. [Display omitted] [ABSTRACT FROM AUTHOR]

Allergic rhinitis (AR) is a common chronic inflammatory condition of the nasal mucosal tissue. Platycodin D (PLD), a triterpenoid saponin isolated from the root of Platycodon grandiflorum , has anti-inflammatory effects in a mouse model of allergic asthma. However, the anti-inflammatory effects of PLD in the nasal mucosa have not been deeply investigated. The objective was to investigate the effect of PLD on inflammatory cytokines and mucus production from nasal epithelial cells. Our study showed that PLD inhibited the levels of granulocyte-macrophage colony-stimulating factor (GM-CSF) and eotaxin in interleukin (IL)-13-stimulated RPMI2650 cells. PLD also suppressed IL-13-induced mucin 5AC (MUC5AC) expression in RPMI2650 cells. Moreover, PLD treatment prevented IL-13-induced p-NF-κB p65 expression in RPMI2650 cells, as well as MAPK signaling pathway activation. Taken together, our results provided evidence that PLD inhibits IL-13-induced the expression of inflammatory cytokines and mucus in nasal epithelial cells by inhibiting the activation of NF-κB and MAPK signaling pathways. [ABSTRACT FROM AUTHOR]

We investigate the disorder effect on coherent fraction and the quantum phase transition of ultracold dilute Bose gases trapped in disordered optical lattices. Within the framework of Bogoliubov theory, an analytical expression for the particle density is derived and the dependence of coherent fraction on disorder strength as well as on lattice depth is discussed. In weak disorder regime, we find a decreased sensitivity of coherent fraction to disorder with the increase of on-site interaction strength. For strong disorder, the quantum phase boundary between superfluid phase and Bose glass phase in the disordered Bose-Hubbard system in weak interaction regime is discussed qualitatively. The obtained phase diagram is in agreement with the empirical square-root law. The dependence of the corresponding critical value of the disorder strength on optical lattice depth is presented as well, and may serve as a reference object for possible experimental investigation. Graphical abstract: [ABSTRACT FROM AUTHOR]

Probabilistic threshold query (PTQ), which returns all the objects satisfying the query with probabilities higher than a threshold, is widely used in uncertain database. Most previous work focused on the efficiency of query process, but paid no attention to the setting of thresholds. However, setting the extreme thresholds may lead to empty result or too many results. It is difficult for a user to set a suitable threshold for a query. In this paper, we propose a new framework for PTQs based on threshold classification using ELM, where the probability threshold is replaced by the range of result number which is more intuitive and easier to choose. We first introduce the features selected for the two most important PTQs, which are nearest neighbor (NN) and reverse nearest neighbor (RNN) queries. Then a threshold classification algorithm (TCA) using ELM is proposed to set a suitable threshold for the query, where plurality voting method is applied. Further, the PTQ processing integrated with TCA are presented, and a dynamic classification strategy is proposed subsequently. Extensive experiments show that compared with the thresholds those the users input directly, the thresholds chosen by ELM classifiers are more suitable, which further improves the performance of PTQs algorithms. In addition, ELM outperforms SVM with regard to both the response time and classification accuracy. [ABSTRACT FROM AUTHOR]

In this era of big data, analyzing large scale data efficiently and accurately has become a challenging problem. As one of the ELM variants, online sequential extreme learning machine (OS-ELM) provides a method to analyze incremental data. Ensemble methods provide a way to learn from data more accurately. MapReduce, which provides a simple, scalable and fault-tolerant framework, can be utilized for large scale learning. In this paper, we first propose an ensemble OS-ELM framework which supports any combination of bagging, subspace partitioning and cross validation. Then we design a parallel ensemble of online sequential extreme learning machine (PEOS-ELM) algorithm based on MapReduce for large scale learning. PEOS-ELM algorithm is evaluated with real and synthetic data with the maximum number of training data 5120K and the maximum number of attributes 512. The speedup of this algorithm reaches as high as 40 on a cluster with maximum 80 cores. The accuracy of PEOS-ELM algorithm is at the same level as that of ensemble OS-ELM executing on a single machine, which is higher than that of the original OS-ELM. [ABSTRACT FROM AUTHOR]

The number of cycle matchings increases exponentially with the number of subscriptions and the maximum length of cycle matchings, which needs a large amount of space to store intermediate results. Approximate cycle matching aims to store only a small part of intermediate results and find cycle matchings as many as possible. The existing solution prunes the intermediate results by a threshold of probability of a subscription to be matched, where the discrete degree of probabilities is neglected. In this paper, we propose an approximate dynamic cycle matching algorithm based on intermediate results classification using extreme learning machine. We first introduce a method of incorporating probability information into feature vector, and then propose the approximate cycle algorithm. Further, we propose a dynamic classification strategy considering that the data distribution of subscriptions may change as time goes on. The proposed approximate cycle matching algorithm and the dynamic classification strategy are evaluated in a simulated environment. The results show that compared with the approximate cycle matching based on probability threshold, the approximate cycle matching based on ELM classification is faster, and the dynamic classification strategy is more efficient and convenient. ELM is more suitable for approximate dynamic cycle matching than SVM with regards to response time. [ABSTRACT FROM AUTHOR]

In this age of big data, analyzing big data is a very challenging problem. MapReduce is a simple, scalable and fault-tolerant data processing framework that enables us to process a massive volume of data. Many machine learning algorithms have been designed based on MapReduce, but there are only a few works related to parallel extreme learning machine (ELM) which is a fast and accurate learning algorithm. Online sequential extreme learning machine (OS-ELM) is one of improved ELM algorithms to support online sequential learning efficiently. In this paper, we first analyze the dependency relationships of matrix calculations of OS-ELM, then propose a parallel online sequential extreme learning machine (POS-ELM) based on MapReduce. POS-ELM is evaluated with real and synthetic data with the maximum number of training data 1280 K and the maximum number of attributes 128. The experimental results show that the training accuracy and testing accuracy of POS-ELM are at the same level as those of OS-ELM and ELM, and it has good scalability with regard to the number of training data and the number of attributes. Compared to original ELM and OS-ELM where the capability to process large scale data is bounded by the limitation of resources within a single processing unit, POS-ELM can deal with much larger scale data. The larger the number of training data is, the higher the speedup of POS-ELM is. It can be concluded that POS-ELM has more powerful capability than both ELM and OS-ELM for large scale learning. [ABSTRACT FROM AUTHOR]

Highlights: [•] The nest fungus Cyathus gansuensis was fermented on rice. [•] Seven new cyathane diterpenes (Cyathins J–P) were purified and identified. [•] Their structures were elucidated by NMR and MS data analysis. [•] New diterpenes (Cyathins J, K and M) presented moderate NO inhibitory activity. [Copyright &y& Elsevier]

Mobile object index should support efficient update operations besides efficient query operations. In this paper, we consider the issue of the efficient updating of mobile object index. Based on a model for the mobile data, we introduce a method of incorporating statistical information of the regions covered by the mobile objects into feature vectors. We then propose a novel architecture of mobile object index, where R-tree is used to index the occupied regions instead of the mobile objects themselves and extreme learning machine (ELM) is used to classify the regions. Further, we describe several related algorithms and the update strategy based on the classification of the regions. The proposed strategy and algorithms are evaluated in a simulated environment. The experiments demonstrate that the proposed update strategy based on region classification using ELM can achieve higher performance with respect to I/O operations. Compared to the strategy without region classification, the proposed method can reduce the number of I/O operations more than 80%. [ABSTRACT FROM AUTHOR]

• It is the first electrochemical immunosensor based on Ce-MOF@Au and Au@Pt core–shell to detect 1-Aminohydantoin (AHD). • The competitive-type electrochemical immunosensor exhibited good specificity. • The detection limit of the immunosensor was 1.35 × 10-7 μg/L. A novel competitive-type electrochemical immunosensor based on square wave voltammetry (SWV) response was developed for the quantitative detection of 1-Aminohydantoin (AHD). To improve the conductivity of this immunosensor nanocomposites with good electrical conductivity were prepared as a signal amplification platform for the immunosensor by growing Au nanoparticles on the surface of Ce-based metal–organic framework (Ce-MOF). In addition, methylene blue (MB)-loaded Au@Pt and coating antigen (OVA-AHD) connected as a signal label. When the target was introduced, it competed with the coating antigen for the Ab, which led to a reduction in the number of signal probes bound to the Ab. The concentration of AHD can be determined by SWV detection of the MB signal loaded on the signal labels. Under optimal conditions, the wide linear range of 0.001–1000 μg /L and a low detection limit of 1.35 × 10-7 μg/L were achieved. Ultimately, the developed method displayed excellent specificity in practical applications, providing a promising probability to detect nitrofuran metabolites residues to guarantee food safety. [ABSTRACT FROM AUTHOR]

A review is presented on high temperature measurement technology based on sapphire optical fiber. This review paper focuses on the sensing theory, sensor structures and sensing performances of different sapphire fiber sensors. Firstly, there is discussion on pyrometer made from sapphire fiber and description on its corresponding sensing properties. Secondly, there are summary on sapphire fiber Bragg grating (SFBG) inscribed by different methods for high temperature measurement. Thirdly, there are analysis on sapphire fiber sensors based on Fabry Perot (FP) interference with different structures. All above, subsequently, lead to the discussion on microwave interferometry and Raman scattering-based sapphire fiber sensors. Then a sensing performance comparison of sapphire pyrometers based on different detection principles are discussed. On the premise of all above, a future prospect and development trend of high-temperature sapphire fiber sensor are addressed as well. [ABSTRACT FROM AUTHOR]

Previously, we isolated a novel Phocaeicola strain, Phocaeicola faecalis FXJYN30E22, from the feces of a healthy human from China. Metagenomic analysis revealed that the distribution of FXJYN30E22 differed in the intestinal tract of different hosts. We aimed to determine whether FXJYN30E22 protects against ulcerative colitis by employing a mouse model. In this study, dextran sulfate sodium was used to construct the UC model. The disease activity index, colon length, body weight changes, and histological scores were used as the pathological indicators to assess the anti-inflammatory effect of P. faecalis FXJYN30E22. Further, cytokine levels, tight junction mRNA expression levels, and short-chain fatty acid (SCFA) concentrations were also analyzed. Phocaeicola faecalis FXJYN30E22 could reduce the DSS-induced increase in DAI score, and enhance the colon length and body weight. Phocaeicola faecalis FXJYN30E22 could enhance TJ protein concentration and modulate the level of cytokines to reach levels close to those of the control group. FXJYN30E22 could also upregulate the concentrations of SCFA, which include acetate and butyrate. Based on the correlation analysis, four factors, including interleukin (IL)-6, IL-10, IL-1β levels, and propionate concentration, were related to the protective roles of FXJYN30E22 in UC mice to different degrees. According to an analysis of the genomic information, the potential protective effects of strain FXJYN30E22 may be associated with the secretion of SCFA by specific genes. These findings suggest that oral P. faecalis FXJYN30E22 could help maintain the epithelial barrier by regulating cytokine levels and secreting SCFA. [ABSTRACT FROM AUTHOR]

• Construct a multi-parameter collision automated simulation framework, integrate the Monte Carlo sampling algorithm, and create a collision damage dataset. • The MLP + XGBoost fusion regression algorithm is used to predict and analyze the cyclist's head collision injury dataset. • The NCP model is introduced to replace the traditional trajectory prediction algorithm based on LSTM and GRU to reduce the complexity of the network model. • An autonomous vehicle trajectory planning algorithm based on cyclist head injury prediction was developed. Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC 15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario. [ABSTRACT FROM AUTHOR]

In order to solve the problem of cervical spondylosis in the early stage of various cervical spondylosis, effective treatment can prevent the deterioration of the disease. This paper presents the results of a clinical trial examining magnetic resonance imaging in the treatment of cervical spondylosis with flotation therapy and selected 68 patients with cervical spondylosis. According to research commodity, using a rigorous randomized controlled trial, 34 cases were divided into a control group (acupuncture group). The needles were kept for 30 minutes once a day. The treatment group (acupuncture combined with floating acupuncture group) was treated with acupuncture on the 1st, 3rd, and 5th days and floating acupuncture on the 2nd, 4th, and 6th days, respectively. Both groups were treated for 6 consecutive days and rested for 1 day. After 2 weeks of treatment, the simplified McGill Pain Scale (MPQ), visual analogue scale (VAS), and neck pain scale (NPQ) were observed and recorded to compare the curative effects. Finally, Excel software is used to manage the data, and SPSS21.0 is used for statistical analysis. Measurements of gender, age, disease, VAS, simple MPQ, and NPQ of the two groups were compared in the two groups, P > 0.05 , which was not significant and comparable. After treatment, VAS, simple MPQ, and NPQ of the two groups were compared in and between groups, the total P < 0.05 , with the mean data. Topics. Acupuncture combined with float needle and acupuncture therapy can improve the pain and breathing of cervical spondylosis and improve the quality of life of patients, but acupuncture combined with needle float is more pronounced than acupuncture groups. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer is one of the most common malignancy in the world. It has been reported that cancer stem cells (CSCs) serve as the primary drivers of tumorigenesis and tumor progression. There is an urgent need to explore novel molecules that regulate CSCs or their signatures. Increasing evidence has shown that miRNAs are involved in tumorigenesis and progression. Here, we aim to explore the regulatory effect and mechanism of miR-3065-3p on the stemness of colorectal cancer.Methods: The expression of miR-3065-3p in colorectal cancer and the association of miR-3065-3p expression with prognosis of patients with colorectal cancer were analyzed using TCGA dataset or clinical cases. Gain or loss of function in different models, including colorectal cancer cell lines and orthotopic xenograft or liver metastatic mouse model, were used to investigate the effects of miR-3065-3p on colorectal cancer stemness and metastasis in vitro and in vivo. Cancer stemness was analyzed by detecting the ability of migration and invasion, NANOG, OCT4, and SOX2 expression, ALDH activity and sphere formation. In addition, the interaction of miR-3065-3p and cytokine receptor-like factor 1 (CRLF1) was analyzed theoretically and identified by the luciferase reporter assay. Moreover, the correlation between CRLF1 expression and miR-3065-3p was analyzed in colorectal cancer tissues. Finally, the effect of CRLF1 on the stemness and metastasis of colorectal cancer in vitro and in vivo was assessed.Results: In this report, we found that miR-3065-3p was overexpressed in colorectal cancer and that its high expression was associated with poor prognosis of patients with colorectal cancer. miR-3065-3p promotes the stemness and metastasis of colorectal cancer. Furthermore, CRLF1 was the downstream target of miR-3065-3p and inhibited the stemness of colorectal cancer. In addition, CRLF1 expression was negatively correlated with miR-3065-3p in colorectal cancer tissues. And, CRLF1 mediated the effects of miR-3065-3p on promoting stemness of colorectal cancer cells.Conclusion: Our data suggest that miR-3065-3p promoted the stemness and metastasis of colorectal cancer by targeting CRLF1. miR-3065-3p might serve as a promising prognostic marker as well as a therapeutic target for colorectal cancer. [ABSTRACT FROM AUTHOR]

To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery (T2-FLAIR) images. We acquired cerebral 3D T2-FLAIR images of thirty patients who were diagnosed with ischemic white matter lesion (WML) with MR-1.5T and MR-3.0T scanners. Histogram texture features which included mean signal intensity (Mean), Skewness and Kurtosis, and gray level co-occurrence matrix (GLCM) texture features which included angular second moment (ASM), Contrast, Correlation, Inverse difference moment (IDM) and Entropy, of regions of interest located in the area of WML and normal white matter (NWM) were measured by ImageJ software. The texture parameters acquired with MR-1.5T scanning were compared with MR-3.0T scanning. The Mean of both WML and NWM obtained with MR-1.5T scanning was significantly lower than that acquired with MR-3.0T (p <0.001), while Skewness and Kurtosis between MR-1.5T and MR-3.0T scanning showed no significant difference (p >0.05). ASM, Correlation and IDM of both WML and NWM acquired with MR-1.5T revealed significantly lower values than those with MR-3.0T (p <0.001), while Contrast and Entropy acquired with MR-1.ST showed significantly higher values than those with MR-3.0T (p <0.001). MR field strength showed no significant effect on histogram textures, while had significant effect on GLCM texture features of cerebral T2-FLAIR images, which indicated that it should be cautious to explain the texture results acquired based on the different MR field strength. [ABSTRACT FROM AUTHOR]

As a member of the PGAMs family, PGAM2 has been proved to catalyze the reversible reaction of 3-phosphoglycerate (3-PGA) to 2-phosphoglycerate (2-PGA) in the glycolytic pathway. However, it is unclear whether PGAM2 has a role in regulating differentiation in goat intramuscular preadipocytes. Here, this study was carried to clone the open reading frame (ORF) of goat PGAM2, elucidate its molecular and expressional characteristics, and evaluate the involvement in adipogenesis of intramuscular preadipocytes. According to our findings, the cloned goat PGAM2 gene was 784 bp in full length, including 762 bp of ORF and encoding a protein of 253 amino acids. The expressional level of PGAM2 peaked at 48 hours after induced adipogenic differentiation and was highest in the skeletal muscle of triceps. Moreover, overexpression of PGAM2 transfected by its overexpression plasmid promotes lipid accumulation of goat intramuscular adipocyte as shown by Oil Red O and bodipy staining, accompanied by up-regulating the mRNA levels of peroxisome proliferator-activated receptor γ (PPARγ) (p < 0.001), sterol regulatory element-binding protein 1 (SREBP1) (p < 0.001), CCAAT/Enhancer-binding protein α (C/EBPα) (p < 0.01) and lipoprotein lipase (LPL) (p < 0.01). Taken together, these findings indicate that PGAM2 is a positive regulator for goat intramuscular adipocytes and provide new insights into improvement intramuscular fat deposition in goat meat. [ABSTRACT FROM AUTHOR]

Analysis of the change of the cells in bile is an evolving research field in biliary pathophysiology and has potential value in diagnosis and therapy. However, laboratory studies of cell in bile across the world are scarce. Bile was collected from the clinical patients with cholangiocarcinoma (CC). To optimize the cell separation method in bile of patients with CC, we studied the factors that may affect cell vitality in bile including the dilution buffer, centrifugal force, centrifugal time, and store time and temperature. Then these factors were modified and performance was evaluated by flow cytometry with respect to the percentage and total yield of viable cells. The separated cells from bile were stained with CD3, CD4, CD8, CD56, TCRγ/δ, CD16, CD14, HLA-DR, CD33, CD15, CD11b, lineage cocktail (CD3, CD14, CD19, CD20, and CD56), CD66b, and CD45 antibodies. The different buffer solutions were joined in bile of patients with CC; experiment results show that the different dilutions have nearly no effect on the ratio of cells in bile by flow cytometry. The best centrifugal procedure was 300 g, 10 min. Bile should be stored at 4°C rather than at normal temperature. Our study further showed that the shorter time of the bile storage, the higher viability of the cell, and immune cells existed in cells isolated from bile. Evaluating bile cell viability is necessary to evaluate method performance. [ABSTRACT FROM AUTHOR]

To evaluate the value of texture features derived from intravoxel incoherent motion (IVIM) parameters for differentiating pancreatic neuroendocrine tumor (pNET) from pancreatic adenocarcinoma (PAC). Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used (from 0 to 800 s/mm2). Based on IVIM model, perfusion-related parameters including perfusion fraction (f), fast component of diffusion (D fast) and true diffusion parameter slow component of diffusion (D slow) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment (ASM), Inverse Difference Moment (IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t -test or Mann-Whitney U test were performed for the between-group comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic efficiency. The mean f value of the pNET group were significantly higher than that of the PAC group (27.0% vs. 19.0%, P = 0.001), while the mean values of D fast and D slow showed no significant differences between the two groups. All texture features (ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups (P = 0.000-0.043). Binary logistic regression analysis showed that texture ASM of D fast and texture Correlation of D slow were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters (AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of D fast combined with Correlation of D slow in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC (AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854). Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC. [ABSTRACT FROM AUTHOR]

To improve the diagnostic accuracy of adenoid hypertrophy (AH) in children and prevent further complications in time, it is important to study and quantify the effects of different degrees of AH on pediatric upper airway (UA) aerodynamics. In this study, based on computed tomography (CT) scans of a child with AH, UA models with different degrees of obstruction (adenoidal-nasopharyngeal (AN) ratio of 0.9, 0.8, 0.7, and 0.6) and no obstruction (AN ratio of 0.5) were constructed through virtual surgery to quantitatively analyze the aerodynamic characteristics of UA with different degrees of obstruction in terms of the peak velocity, pressure drop (△P), and maximum wall shear stress (WSS). We found that two obvious whirlpools are formed in the anterior upper part of the pediatric nasal cavity and in the oropharynx, which is caused by the sudden increase in the nasal cross-section area, resulting in local flow separation and counterflow. In addition, when the AN ratio was ≥ 0.7, the airflow velocity peaked at the protruding area in the nasopharynx, with an increase 1.1–2.7 times greater than that in the nasal valve area; the △P in the nasopharynx was significantly increased, with an increase 1.1–6.8 times greater than that in the nasal cavity; and the maximum WSS of the posterior wall of the nasopharynx was 1.1–4.4 times larger than that of the nasal cavity. The results showed that the size of the adenoid plays an important role in the patency of the pediatric UA. [ABSTRACT FROM AUTHOR]

Sargentodoxa cuneata , containing syringaresinol and its glycoside liriodendrin as the main bioactive compounds, is a well-known traditional Chinese medicine for treating intestinal inflammation. In our preliminary study, liriodendrin inhibited NF-kB activation in sepsis-induced acute lung injury. The present study was designed to investigate its effect on dextran sulfate sodium (DSS)-induced colitis in a mouse model and to explore the possible related mechanisms. Experimental colitis was established by giving mice drinking water containing 3% (w/v) DSS for 7 days. The mice were pretreated with liriodendrin (100 mg/kg/day, intragastrically) 3 days before DSS treatment. We determined the effects of liriodendrin on disease activity index (DAI), colon length, histopathological examination, antioxidants, and anti-inflammatory activities. Our results showed that liriodendrin greatly decreased MPO and MDA activities and significantly increased SOD and GPx activities in the colon. Moreover, liriodendrin improved DAI, colon length and histological damage in colon and reduced the levels of pro-inflammatory cytokines, such as TNF-a, IL-1β and IL-6. Meanwhile, assessments by western blot revealed that liriodendrin significantly suppressed the activation of Akt and NF-κB pathways and up-regulated the expression of ERβ in the colon. In vitro, liriodendrin down-regulated production of pro-inflammatory cytokines and suppressed NF-κB signalling pathways in LPS-induced RAW 264.7 macrophages in a concentration-dependent manner. In addition, syringaresinol, the hydrolysate of liriodendrin, more potently down-regulated production of pro-inflammatory cytokines and suppressed NF-κB and Akt signalling pathways in LPS-induced RAW 264.7 macrophages,which were abolished by using a pure ER antagonist, ICI182, 780. Taken together, liriodendrin-mediated suppression of inflammatory damage in the colon may be attributable to the in vivo transformation to syringaresinol and liriodendrin may be a promising therapeutic approach preventive agent for colitis treatment. [ABSTRACT FROM AUTHOR]

The neural mechanisms underlying the processing of novel metaphors created from poetic contexts have been investigated in previous experiments. Considering the different attributes of metaphors, the current study compared the temporal dynamics for processing two kinds of novel metaphors with event-related potentials (ERPs): scientific metaphors and poetic metaphors. Amplitudes of the N400 (350–450 ms) were similar for scientific metaphors and poetic metaphors. Within the LPC window (550–850 ms), ERPs associated with these two kinds of novel metaphors were observed every 100 ms in three successive subwindows. We found that due to a sustained late negativity overlapping in time and space with the LPC scientific metaphors seemingly elicited the least positive LPC throughout. More importantly, with the passage of time, the LPC waveforms for scientific metaphors diverged from those for poetic metaphors and conventional metaphors while LPC waveforms for poetic metaphors converged with those for conventional metaphors. The reported results indicate the possible different time courses for processing novel metaphors with different contextual structures and different functions. And the findings are compatible with recent brain imaging studies and complement them by adding such new dimensions as the temporal dynamics and the properties of novel metaphors. [ABSTRACT FROM AUTHOR]

Imaging mass cytometry (IMC) is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry/cytochemistry. A unique high-dimensional perspective comprehensively and accurately depicts the complex interaction of phenotype, signalling pathway and tumour microenvironment and is widely used in solid tumours. However, the application scenarios of IMC in basic medicine and clinical research in solid tumours lack systematic introduction and classification. This paper reviews the application of IMC in depicting the panorama of the tumour microenvironment, revealing tumour spatial heterogeneity, clarifying tumour pharmacological mechanisms, assisting in new drug development, and dynamically evaluating the efficacy of immunotherapy in solid tumours. • IMC is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry. • This paper reviews the application of IMC in basic laboratory studies, preclinical studies and clinical observations in solid tumours. • It can depict the complex phenotypes in tissue and tumour microenvironment. • IMC is a powerful tool for solid tumour transformation research and precision treatment as new technology. • The existing research cases are classified and summarized, meanwhile the clinical trials using IMC are listed. [ABSTRACT FROM AUTHOR]

The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, referred to as the panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground-measured PR reached 0.935, and the root mean square error values for the estimations of the heading date and effective tiller percentage were 0.687 d and 4.84%, respectively. Based on the analysis of the results, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAVs and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops in future research. [ABSTRACT FROM AUTHOR]