WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode.

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage of current untargeted and widely-targeted methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0. It enables the construction of a high-coverage library containing 2111 plant volatiles; the development of a comprehensive-selective ion monitoring (cSIM) acquisition method that contains the fewest but sufficient ions for most plant volatiles, including the selection of characteristic qualitative ions with minimal ions number for each compound and the optimized segmentation of acquisition method; and finally, the automatic qualitative and semi-quantitative analysis of cSIM data. Furthermore, the library and acquisition method can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. WTV 2.0 increased the median signal-to-noise ratio by 7.6-fold compared to the untargeted method, doubled the annotation coverage compared to the untargeted and WTV 1.0 methods in tomato fruit, and leading to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface, and is applicable to volatiles and primary metabolites profiling in any species.

Traceability Research on Dendrobium devonianum Based on SWATHtoMRM.

SWATHtoMRM technology was used in this experiment to further identify and trace the sources of Dendrobium devonianum and Dendrobium officinale produced in the same area using TOF and MS-MRM. After the conversion of the R package of SWATHtoMRM, 191 MRM pairs of positive ions and 96 pairs of negative ions were obtained. Dendrobium devonianum and Dendrobium officinale can be separated very well using the PCA and PLS-DA analysis of MRM ion pairs; this shows that there are obvious differences in chemical composition between Dendrobium devonianum and Dendrobium officinale, which clearly proves that the pseudotargeted metabolomics method based on SWATHtoMRM can be used for traceability identification research. A total of 146 characteristic compounds were obtained, with 20 characteristic compounds in Dendrobium devonianum. The enrichment pathways of the characteristic compounds were mainly concentrated in lipids and atherosclerosis, chagas disease, fluid shear stress and atherosclerosis, proteoglycans in cancer, the IL-17 signaling pathway, the sphingolipid signaling pathway, diabetic cardiomyopathy, arginine and proline metabolism, etc., among which the lipid and atherosclerosis pathways were more enriched, and 11 characteristic compounds affected the expression levels of IL-1, TNFα, CD36, IL-1ß, etc. These can be used as a reference for research on variety improvement and active substance accumulation in Dendrobium devonianum and Dendrobium officinale.

Universal Entanglement and Correlation Measure in Two-Dimensional Conformal Field Theories.

We calculate the amount of entanglement shared by two intervals in the ground state of a (1+1)-dimensional conformal field theory (CFT), quantified by an entanglement measure E based on the computable cross norm (CCNR) criterion. Unlike negativity or mutual information, we show that E has a universal expression even for two disjoint intervals, which depends only on the geometry, the central charge c, and the thermal partition function of the CFT. We prove this universal expression in the replica approach, where the Riemann surface for calculating E at each order n is always a torus topologically. By analytic continuation, the result of n=1/2 gives the value of E. Furthermore, the results of other values of n also yield meaningful conclusions: The n=1 result gives a general formula for the two-interval purity, which enables us to calculate the Rényi-2 N-partite information for N≤4 intervals; while the n=∞ result bounds the correlation function of the two intervals. We verify our findings numerically in the spin-1/2 XXZ chain, whose ground state is described by the Luttinger liquid.

Response of vegetation phenology to urbanization in urban agglomeration areas: A dynamic urban-rural gradient perspective.

Being an important theme in global warming, the response of vegetation phenology to urbanization has become an increasing concern at both the local and global levels. Previous studies have focused on spatial or temporal responses across urban-rural gradients; thus, the influence of urbanization on vegetation phenology along the dynamic urbanization gradient has not been well quantified. In this study, we comprehensively analyzed the response of vegetation phenology to urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area (GHM-GBA) from a dynamic urban-rural gradient perspective. The results show that the response of vegetation phenology to urbanization level has a distinct spatiotemporal difference across the urban-rural gradient. Compared to rural areas, the change rate of advancements in the start-of-season (SOS) in urban domains was 1.16 DOY/year and that of the end-of-season (EOS) was 0.63 days/year from 2001 to 2020. In the GHM-GBA region, 61.03 % of the remote sensing pixels showed an advancing trend for SOS and 55.75 % for EOS. Urbanization advanced the SOS and EOS but did not extend the growing season length, and the SOS and EOS were advanced by 7 and 6 days along the urban-to-rural gradient, respectively. For every 10 % increase in urbanization levels, the SOS and EOS advanced by 1.085 and 1.091 days across the urban-rural gradient, respectively; the spring land surface temperature (LST) advanced the SOS at a rate of 1.71 days/°C, while the autumn LST advanced the EOS at a rate of 1.88 days/°C. The phenological shift in the urban-rural gradient was more significant than that over time, which was mainly because of land surface warming under different urbanization levels. These quantitative findings are of great importance for understanding the complicated impacts of urbanization on vegetation phenology and for developing models to predict vegetation phenological changes under future urbanization.

Fundamental Limitation on the Detectability of Entanglement.

Entanglement detection is essential in quantum information science and quantum many-body physics. It has been proved that entanglement exists almost surely for a random quantum state, while the realizations of effective entanglement criteria usually consume exponentially many resources with regard to system size or qubit number, and efficient criteria often perform poorly without prior knowledge. This fact implies a fundamental limitation might exist in the detectability of entanglement. In this work, we formalize this limitation as a fundamental trade-off between the efficiency and effectiveness of entanglement criteria via a systematic method to evaluate the detection capability of entanglement criteria theoretically. For a system coupled to an environment, we prove that any entanglement criterion needs exponentially many observables to detect the entanglement effectively when restricted to single-copy operations. Otherwise, the detection capability of the criterion will decay double exponentially. Furthermore, if multicopy joint measurements are allowed, the effectiveness of entanglement detection can be exponentially improved, which implies a quantum advantage in entanglement detection problems. Our results may shed light on why quantum phenomena are difficult to observe in large noisy systems.

Case report: Tongdu Xingshen acupuncture for a patient with persistent vegetative state after herpes simplex virus encephalitis.

Introduction: A persistent vegetative state (PVS) can be caused by traumatic or non-traumatic brain injury. PVS is a complex clinical condition with numerous complications. Nursing care, medical treatment, and comprehensive rehabilitation are necessary to improve the outcomes of PVS. However, the prognosis remains unsatisfactory. Acupuncture therapy has been used as a rehabilitation strategy to treat patients with PVS in China, showing better results in the recovery of consciousness, intellectual capability, and motor function. Case description: We present the case of a 4-month-long PVS after herpes simplex virus encephalitis (HSVE) in a 3.5-year-old boy who underwent Tongdu Xingshen acupuncture integrated with Western medicine and rehabilitation. The patient regained consciousness post-treatment. His intelligence and motor function gradually recovered after seven treatment sessions. Conclusion: Tongdu Xingshen acupuncture is a potential complementary therapy to optimize clinical outcomes in PVS.

Design of Blended Teaching Model Based on Emotion Recognition and Language Learning.

With the vigorous development of Internet technology, great changes have taken place in all aspects of human society. This change is also having an increasingly significant impact on the education sector. It is even a trend to subvert the tradition. This also makes the student's identity a passive recipient of knowledge. At the same time, the final orientation of China's education model is the result of examinations, and there is little guidance for students' interest. Of course, traditional teaching also has the ability to enable students' subject knowledge to be systematically established, and to communicate with teachers in the teaching process, which can improve students' learning efficiency in the classroom. In the face of the information explosion in today's society and the rapid development of the above-mentioned technologies, the change of students' learning mode of knowledge has also ushered in an opportunity to change. The purpose of this paper is to study the establishment of a blended teaching model that combines traditional classrooms with network applications. With the help of the characteristics of network big data, it transforms students' passive learning identities, and combines offline traditional learning classrooms with online learning. In addition to the advantages mentioned above, the advantage of online learning is that some network science and technology can be used for the online learning platform to serve the entire teaching process. Therefore, this paper proposes a blended teaching model based on the network platform for students' emotion recognition and language learning result analysis. And from the experimental results in this paper, it can be seen that the recognition evaluation rate of HTMC, the feature of emotion recognition, is 71.52% and the recognition frequency of ETMC is 73.89%. The above two recognition parameters can better reflect the emotional changes in the mixed teaching process.

Spatial identification of restored priority areas based on ecosystem service bundles and urbanization effects in a megalopolis area.

Rapid urbanization has altered the structure and function of urban ecosystems with respect to the demand for planning ecological restoration to inhibit ecological degradation. However, there is still a challenge to quickly and effectively identify the restored priority areas to maximize ecological service (ES) supply and enhance human well-being. Taking the Shenzhen metropolitan region as a case study area, this study identified the restored priority sites based on the ES bundles evolution and urbanization effects. The ES bundles were identified by analyzing the spatial dynamics under the hybrid urban landscape, then the impact of urbanization on the ES bundles was explored using linear regression analysis characterized by different levels of urbanization in different stages. Furthermore, the spatial statistics were used to identify the priority sites. The results showed that 68.78% of the grids had changed their ES bundles in terms of their quantities, types, and sites in Shenzhen during 1978-2018. The urbanization driver spatially shifts from provision of service to regulation and cultural ecosystem services and significantly negatively influences the composition and structure of the ES bundles in different urbanization stages. 1196 of the 1 square kilometer grids, which accounted for 54.17%, were identified to prioritize for ecological restoration in Shenzhen. However, only 4.08% of them need to set as the key ecological restoration site. This study explored an effectively spatial way to implement ecological restoration planning in a rapidly urbanized area.

Detecting Entanglement in Quantum Many-Body Systems via Permutation Moments.

Multipartite entanglement plays an essential role in both quantum information science and many-body physics. Because of the exponentially large dimension and complex geometric structure of the state space, the detection of entanglement in many-body systems is extremely challenging in reality. Conventional means, like entanglement witness and entropy criterion, either highly depend on the prior knowledge of the studied systems or the detection capability is relatively weak. In this Letter, we propose a framework for designing multipartite entanglement criteria based on permutation moments, which have an effective implementation with either the generalized control-swap quantum circuits or the random unitary techniques. As an example, in the bipartite scenario, we develop an entanglement criterion that can detect bound entanglement and show strong detection capability in the multiqubit Ising model with a long-range XY Hamiltonian. In the multipartite case, the permutation-moment-based criteria can detect entangled states that are not detectable by any criteria extended from the bipartite case. Our framework also shows potential in entanglement quantification and entanglement structure detection.

Accurate inference of gene regulatory interactions from spatial gene expression with deep contrastive learning.

MOTIVATION: Reverse engineering of gene regulatory networks (GRNs) has long been an attractive research topic in system biology. Computational prediction of gene regulatory interactions has remained a challenging problem due to the complexity of gene expression and scarce information resources. The high-throughput spatial gene expression data, like in situ hybridization images that exhibit temporal and spatial expression patterns, has provided abundant and reliable information for the inference of GRNs. However, computational tools for analyzing the spatial gene expression data are highly underdeveloped. RESULTS: In this study, we develop a new method for identifying gene regulatory interactions from gene expression images, called ConGRI. The method is featured by a contrastive learning scheme and deep Siamese convolutional neural network architecture, which automatically learns high-level feature embeddings for the expression images and then feeds the embeddings to an artificial neural network to determine whether or not the interaction exists. We apply the method to a Drosophila embryogenesis dataset and identify GRNs of eye development and mesoderm development. Experimental results show that ConGRI outperforms previous traditional and deep learning methods by a large margin, which achieves accuracies of 76.7% and 68.7% for the GRNs of early eye development and mesoderm development, respectively. It also reveals some master regulators for Drosophila eye development. AVAILABILITYAND IMPLEMENTATION: https://github.com/lugimzheng/ConGRI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a widely targeted volatilomics method for profiling volatilomes in plants.

Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants. Although gas chromatography-mass spectrometry-based untargeted metabolomics is commonly used to assess plant volatiles, it suffers from high spectral convolution, low detection sensitivity, a limited number of annotated metabolites, and relatively poor reproducibility. Here, we report a widely targeted volatilomics (WTV) method that involves using a "targeted spectra extraction" algorithm to address spectral convolution, constructing a high-coverage MS2 spectral tag library to expand volatile annotation, adapting a multiple reaction monitoring mode to improve sensitivity, and using regression models to adjust for signal drift. The newly developed method was used to profile the volatilome of rice grains. Compared with the untargeted method, the newly developed WTV method shows higher sensitivity (for example, the signal-to-noise ratio of guaicol increased from 4.1 to 18.8), high annotation coverage (the number of annotated volatiles increased from 43 to 132), and better reproducibility (the number of volatiles in quality control samples with relative standard deviation value below 30.0% increased from 14 to 92 after normalization). Using the WTV method, we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions. The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance. These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.

A GIS-Based Approach for Flood Risk Zoning by Combining Social Vulnerability and Flood Susceptibility: A Case Study of Nanjing, China.

The identification of vulnerable people and places to flood is crucial for effective disaster risk management. Here, we combine flood hazard and social vulnerability index to capture the potential risk of flood. In this paper, Nanjing was taken as the case study to explore the spatial pattern of social vulnerability towards flood at the community scale by developing an index system. Based on the flood risk results of ArcSWAT, the risk of flood disaster in Nanjing was evaluated. The results show the following. (1) Social vulnerability exhibits a central-peripheral pattern in general, which means that the social vulnerability degree is high in the central city and decreases gradually to the suburbs. (2) The susceptibility to flood disaster has a similar circle-layer pattern that is the highest in the urban centre, lower in the exurban areas, and the lowest in the suburb areas. (3) By using the GIS-based zoning approach, communities are classified into four types by comprehensively considering their flood susceptibility and social vulnerability. The spatial pattern is explained, and policy recommendation for reducing flood risk is provided for each type of community. The research has important reference significance for identifying the spatial pattern of social vulnerability to flood and then formulating targeted adaptation countermeasures.

Using Artificial Intelligence-Based Collaborative Teaching in Media Learning.

With the advent of the 5G era, humans must not only learn the knowledge and skills of cross-border integration but must also get to grips with the breadth and efficiency of artificial intelligence (AI) technology in order to jointly overcome current difficulties and create a happy and beautiful life. In this article, we use the example of an elementary school to discuss the decision-making factors that influence teachers when choosing AI technology, where the digital content of schools is imported into artificial intelligence-based collaborative teaching. After discussing the relevant literature, this study will introduce the concept of digital media education, and then compare the development and application of smart technology and human-computer collaborative teaching methods, describing three key aspects and factors that influence elementary school teachers' choice of AI technology. There are 12 evaluation criteria in total. After the completion of expert questionnaires and data analysis, it was found that the main factors affecting teachers' choice of AI technology are "collaborative tasks," "functional characteristics," and "modeling characteristics." In terms of evaluation criteria, the four most important aspects were found to be "learning assistance," "security," "teaching observation," and "record review." The results of this research analysis will help provide a reference for digital content development and individual recommendation services. In future work, this study can further discuss teaching innovations in digital media education, aimed at improving the quality and effectiveness of teaching and learning.

How to Develop Entrepreneurial Talent More Effectively? A Comparison of Different Entrepreneurship Educational Methods.

Recently, scholars have begun to shift focus toward the effectiveness of different teaching methods for entrepreneurship education. However, the establishment of a unified and clear standard for the division of entrepreneurship educational methods remains unfulfilled, affecting the accuracy of research conclusions. In the present study, for the first time, the aim was to divide the entrepreneurship educational method into the classroom teaching method (CTM) and the extracurricular activity method (EAM) from the perspective of competency level training. On the basis of the modified planning behavior theory, the influence of entrepreneurship education on entrepreneurial intention (EI) was explored. In the present study, 514 college students of 14 universities in China were surveyed. The results reveal that the CTM and EAM had a direct positive bearing on EI, with indirect impact exerted by attitude toward entrepreneurship (ATE) and perceived behavioral control (PBC). Although the direct effects of the two teaching methods were similar, EAM could effectively improve ATE and PBC, thereby resulting in a positive effect on EI to a greater extent. Further observations were made that the participation of research University students in CTM was significantly lower than that of applied University students, leading to lower EI. Additionally, higher EI could be attributed to the more active participation in EAM of male students than female students, while no significant difference was indicated between different majors in EI. The results are of significant reference value for promoting the reform of entrepreneurship education and improving the quality of entrepreneurship education in colleges and universities.

The effect of combined transcranial pulsed current stimulation and transcutaneous electrical nerve stimulation on lower limb spasticity in children with spastic cerebral palsy: a randomized and controlled clinical study.

BACKGROUND: In the current study, we applied a combination of non-invasive neuromodulation modalities concurrently with multiple stimulating electrodes. Specifically, we used transcranial pulsed current stimulation (tPCS) and transcutaneous electrical nerve stimulation (TENS) as a novel strategy for improving lower limb spasticity in children with spastic cerebral palsy (SCP) categorized on levels III-V of the Gross Motor Function Classification System (GMFCS) with minimal side effects. METHODS: Sixty-three SCP children aged 2-12 years, who were classified on levels III-V of the GMFCS were randomly assigned to one of two groups, resulting in 32 children in the experimental group and 31 children in the control group. The experimental group underwent a combination therapy of tPCS (400 Hz, 1 mA cerebello-cerebral stimulation) and TENS (400 Hz, max 10 mA) for 30 min, followed by 30 min of physiotherapy five times per week for 12 weeks. The control group underwent physiotherapy only 30 mins per day five times per week for 12 weeks. In total, all groups underwent 60 treatment sessions. The primary outcome measures were the Modified Ashworth Scale (MAS) and Modified Tardieu Scale (MTS). Evaluations were performed 3 days before and after treatment. RESULTS: We found a significant improvement in MAS and MTS scores of the lower limbs in the experimental group compared to the control group in the hip adductors (Left: p = 0.002; Right: p = 0.002), hamstrings (Left: p = 0.001; Right: p < 0.001, and gastrocnemius (Left: p = 0.001; Right: p = 0.000). Moreover, MTS scores of R1, R2 and R2-R1 in left and right hip adduction, knee joint, and ankle joint all showed significant improvements (p ≤ 0.05). Analysis of MAS and MTS scores compared to baseline scores showed significant improvements in the experimental group but declines in the control group. CONCLUSION: These results are among the first to demonstrate that a combination of tPCS and TENS can significantly improve lower limb spasticity in SCP children classified on GMFCS levels III-V with minimal side effects, presenting a novel strategy for addressing spasticity challenges in children with severe SCP. TRIAL REGISTRATION: ChiCTR.org, ChiCTR1800020283, Registration: 22 December 2018 (URL: http://www.chictr.org.cn/showproj.aspx?proj=33953 ).

Modelling impacts of water diversion on water quality in an urban artificial lake.

As an important form of urban water resource, urban artificial lakes are severely affected by rapid urbanization and interference from human activities. These small lakes are characterized by their unique irregular shape, fragile ecosystem, and relatively closed, stagnant waterbodies. However, few studies have focused on their hydrodynamics and water quality, in particular the restoration methods and mechanisms remaining unclear. The present study applied the MIKE 21 FM model to investigate the effects of water diversion on water quality in a typical urban artificial lake. By considering different flow arrangements, several model scenarios were set up to predict the impacts of water diversion on selected water quality parameter. The results showed that the effectiveness of water diversion was directly related to flow velocity, the relative position to the fresh water inlet, the amount and quality of fresh water and water remaining to be diluted, and the circulation direction of flow field. The inflow-outflow arrangement was the primary factor determining the flow field and NH3-N variation trends across the lake, and an increased discharge exhibited unequal effects in individual zones. Wind was also important for the formation of flow circulation and pollutant variation. Methods were proposed for enhancing water quality in urban small-scale lakes, including changing the way diversion projects are managed, improving the quality of diverted flow, enhancing flow fluidity, or utilizing wind effects and local topography.

The Chromosome-Level Genome of Miracle Fruit (Synsepalum dulcificum) Provides New Insights Into the Evolution and Function of Miraculin.

Miracle fruit (Synsepalum dulcificum) is a rare valuable tropical plant famous for a miraculous sweetening glycoprotein, miraculin, which can modify sour flavors to sweet flavors tasted by humans. Here, we present a chromosome-level high-quality genome of S. dulcificum with an assembly genome size of ∼550 Mb, contig N50 of ∼14.14 Mb, and 37,911 annotated protein-coding genes. Phylogenetic analysis revealed that S. dulcificum was most closely related to Camellia sinensis and Diospyros oleifera, and that S. dulcificum diverged from the Diospyros genus ∼75.8 million years ago (MYA), and that C. sinensis diverged from Synsepalum ∼63.5 MYA. Ks assessment and collinearity analysis with S. dulcificum and other species suggested that a whole-genome duplication (WGD) event occurred in S. dulcificum and that there was good collinearity between S. dulcificum and Vitis vinifera. On the other hand, transcriptome and metabolism analysis with six tissues containing three developmental stages of fleshes and seeds of miracle fruit revealed that Gene Ontology (GO) terms and metabolic pathways of "cellular response to chitin," "plant-pathogen interaction," and "plant hormone signal transduction" were significantly enriched during fruit development. Interestingly, the expression of miraculin (Chr10G0299340) progressively increased from vegetative organs to reproductive organs and reached an incredible level in mature fruit flesh, with an fragments per kilobase of transcript per million (FPKM) value of ∼113,515, which was the most highly expressed gene among all detected genes. Combining the unique signal peptide and the presence of the histidine-30 residue together composed the main potential factors impacting miraculin's unique properties in S. dulcificum. Furthermore, integrated analysis of weighted gene coexpression network analysis (WGCNA), enrichment and metabolite correlation suggested that miraculin plays potential roles in regulating plant growth, seed germination and maturation, resisting pathogen infection, and environmental pressure. In summary, valuable genomic, transcriptomic, and metabolic resources provided in this study will promote the utilization of S. dulcificum and in-depth research on species in the Sapotaceae family.