The Effect of Sulfuric Acid Treatment on Hard Seeds of \u3cem\u3eMelilotoides ruthenica\u3c/em\u3e

Melilotoides ruthenica is a perennial legume, and is characterized by cold resistence, drought resistence and high protein content (Luo 1993). It is used for hay and as a pasture plant, but because a high percentage of the seed are hard-seeded to allow reliable germination, seedling production is hindered (Du et al. 2007). The objective of this research was to study the effect of sulfuric acid treatment on hard-seeds of M. ruthenica and determine the optimal treatment concentration and treatment time

Bibliometric analysis of post-traumatic stress disorder in forensic medicine: Research trends, hot spots, and prospects

BackgroundPost-traumatic stress disorder (PTSD) has various risk factors, complex pathogenesis, and diverse symptoms, and is often comorbid with other injuries and diseases, making forensic diagnosis difficult.MethodsTo explore the current research status and trends of PTSD, we used the Web of Science Core Collection databases to screen PTSD-related literature published between 2010 and 2021 and CiteSpace to perform bibliometric analysis.ResultsIn recent years, PTSD-related research has grown steadily. The countries and institutions with the most research results were the United States and England, and King’s College London and Boston University, respectively. Publications were identified from 2,821 different journals, including 13 forensic-related journals, but the journal distribution was relatively scattered and there was a lack of professional core journals. Keyword co-occurrence and clustering identified many hot topics; “rat model,” “mental health,” and “satisfaction” were the topics most likely to have a clear effect on future research. Analysis extracted nine turning points from the literature that suggested that neural network centers, the hypothalamic–pituitary–adrenal axis, and biomarkers were new research directions. It was found that COVID-19 can cause severe psychological stress and induce PTSD, but the relationship needs further study. The literature on stress response areas and biomarkers has gradually increased over time, but specific systemic neural brain circuits and biomarkers remain to be determined.ConclusionThere is a need to expand the collection of different types of biological tissue samples from patients with different backgrounds, screen PTSD biomarkers and molecular targets using multi-omics and molecular biology techniques, and establish PTSD-related molecular networks. This may promote a systematic understanding of the abnormal activation of neural circuits in patients with PTSD and help to establish a personalized, accurate, and objective forensic diagnostic standard

Evolution of zinc oxide nanostructures through kinetics control †

In-depth understanding of the kinetics of the vapor deposition process is substantial for advancing this capable bottom-up nanostructure synthesis approach into a versatile large-scale nanomanufacturing technology. In this paper, we report a systematic study of the vapor deposition kinetics of ZnO nanomaterials under controlled atmosphere and properly refined deposition conditions. The experiments clearly evidenced the self-catalyzed growth of ZnO NWs via the formation of ZnO nanoflowers. This result illustrated how ZnO morphologies were associated with the discrepancy between oxidation rate and condensation rate of Zn. The capability of switching the NW morphologies and possibly mechanisms was demonstrated by kinetically controlling the deposition system. The high Zn composition during the deposition resulted in strongly luminescent NWs, which can be used for optical imaging applications. This research discovered a fundamental kinetics that governs the mechanisms and morphology selection of nanostructures in a non-catalyst growth system

NASICON-type air-stable and all-climate cathode for sodium-ion batteries with low cost and high-power density

The development of low-cost and long-lasting all-climate cathode materials for the sodium ion battery has been one of the key issues for the success of large-scale energy storage. One option is the utilization of earth-abundant elements such as iron. Here, we synthesize a NASICON-type tuneable Na4Fe3(PO4)2(P2O7)/C nanocomposite which shows both excellent rate performance and outstanding cycling stability over more than 4400 cycles. Its air stability and all-climate properties are investigated, and its potential as the sodium host in full cells has been studied. A remarkably low volume change of 4.0% is observed. Its high sodium diffusion coefficient has been measured and analysed via first-principles calculations, and its three-dimensional sodium ion diffusion pathways are identified. Our results indicate that this low-cost and environmentally friendly Na4Fe3(PO4)2(P2O7)/C nanocomposite could be a competitive candidate material for sodium ion batteries

Combatting global grassland degradation

Grasslands are under severe threat from ongoing degradation, undermining their capacity to support biodiversity, ecosystem services and human well-being. Yet, grasslands are largely ignored in sustainable development agendas. In this Perspective, we examine the current state of global grasslands and explore the extent and dominant drivers of their degradation. Socio-ecological solutions are needed to combat degradation and promote restoration. Important strategies include: increasing recognition of grasslands in global policy; developing standardized indicators of degradation; using scientific innovation for effective restoration at regional and landscape scales; and enhancing knowledge transfer and data sharing on restoration experiences. Stakeholder needs can be balanced through standardized assessment and shared understanding of the potential ecosystem service trade-offs in degraded and restored grasslands. The integration of these actions into sustainability policy will aid in halting degradation and enhancing restoration success, and protect the socio-economic, cultural and ecological benefits that grasslands provide

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

An optimally weighted user- and item-based collaborative filtering approach to predicting baseline data for Friedreich's Ataxia patients

Seventh Framework Programme of the European Union; Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany