Do safety cases have a role in aircraft certification?

AbstractSafety cases, as a means of demonstrating system safety, have been increasingly used as the basis for system assurance, especially in safety or mission-critical systems in fields such as offshore installation, railway operations, nuclear plants, and air traffic control. Despite the increased adoption of safety cases in the aforementioned areas, the usage of safety arguments is still limited in the certification of a civil aircraft design. This paper provides 1) a brief overview of the key regulations and guidelines in support of aero-system certification especially at the development stage; 2) a review of the history, the essence, and the practice of safety cases; 3) an analysis of the role of processes and safety arguments in aircraft certification; and 4) recommendations on the future work in terms of further application of safety cases in aircraft certification

EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition

How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized. Comparing to the existing features, the characterized deep EEG features could be considered to be more generic and independent of any specific EEG task. The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising cross-subject emotion recognition performance. It proves EEGFuseNet is capable of characterizing and fusing deep features that imply comparative cortical dynamic significance corresponding to the changing of different emotion states, and also demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner

HA-HI: Synergising fMRI and DTI through Hierarchical Alignments and Hierarchical Interactions for Mild Cognitive Impairment Diagnosis

Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a pivotal area of research. While various regional and connectivity features from functional MRI (fMRI) and diffusion tensor imaging (DTI) have been employed to develop diagnosis models, most studies integrate these features without adequately addressing their alignment and interactions. This limits the potential to fully exploit the synergistic contributions of combined features and modalities. To solve this gap, our study introduces a novel Hierarchical Alignments and Hierarchical Interactions (HA-HI) method for MCI and SCD classification, leveraging the combined strengths of fMRI and DTI. HA-HI efficiently learns significant MCI- or SCD- related regional and connectivity features by aligning various feature types and hierarchically maximizing their interactions. Furthermore, to enhance the interpretability of our approach, we have developed the Synergistic Activation Map (SAM) technique, revealing the critical brain regions and connections that are indicative of MCI/SCD. Comprehensive evaluations on the ADNI dataset and our self-collected data demonstrate that HA-HI outperforms other existing methods in diagnosing MCI and SCD, making it a potentially vital and interpretable tool for early detection. The implementation of this method is publicly accessible at https://github.com/ICI-BCI/Dual-MRI-HA-HI.git

Extraction, Isolation and Identification of Antimicrobial Substances from Bacillus amyloliquefaciens CMN1308

Four separation methods of antimicrobial substances produced by CMN1308 (Bacillus amyloliquefaciens) were evaluated and selected according to number of antimicrobial substances and its activity in vitro. The results showed that extraction by acid precipitation of the fermentation supernatant of CMN1308 was the best with a diameter of inhibition zone of pathogen fungi P. expansum of 12.3 mm in a laboratory bioassay. Applying a silica thin layer chromatography (TLC), SDS-PAGE and other separation technologies we isolate antimicrobial substances, and the separated band were cut off for mass spectrometry analysis. The TLC of crude extract of CMN1308 show a topical band corresponding with the surfactin standard (Rf value =0.75), proved that the strain CMN1308 can produce this surface active compound. The mycoprotein extracted from CMN1308 was separated by Tricine-SDS-PAGE modified with the addition of urea in the separation gel. After mass spectrometric analysis and protein characterization, the isolated mycoprotein showed a maximum ion peak at M/Z of 2679 and molecular weight of 29.5 kDa, matching with protein flagellin. The extracellular antimicrobial protein of strain CMN1308 display four bands after urea-Tricine-SDS-PAGE, but after mass spectrometry analysis only two bands were identified. Band “A” with a maximum ion peak at M/Z of 1926 and molecular weight of 49.8 kDa, aligned with NCBI database, matching with DLDH (dihydrolipoamide dehydrogenase enzyme). Band “D” show the maximum ion peak at M/Z of 2936 and molecular weight of 22.4 kD, matching with a chitin binding protein. Thus, the strain CMN1308 has the potential to be developed as a commercial biological control agent for chestnut common pathogenic fungi

High-resolution X-ray microdiffraction analysis of natural teeth

In situ microzone X-ray diffraction analysis of natural teeth is presented. From our experiment, layer orientation and continuous crystal variations in teeth could be conveniently studied using fast online measurements by high-resolution X-ray microdiffraction equipment

Determination of Camellia oleifera Abel. Germplasm Resources of Genetic Diversity in China using ISSR Markers

Camellia oleifera is one of the four woody oil plants in the world, which is widely cultivated in South China. To examine the genetic diversity of C. oleifera in China, the diversity and genetic relationships among and within major populations of 109 varieties of C. oleifera were analyzed using ISSR markers. Twenty-three ISSR primers out of 49 primers yielded approximately 487 legible bands. A total of 335 of these bands were polymorphic markers, and the ratio of polymorphism was 68.86%. From the results, Zhejiang province showed the highest populations genetic diversity (H value 0.18), while Guangxi population showed the lowest genetic diversity (H 0.0851). Base on the bands, the genetic similarity coefficient ranged from 0.61 to 0.93 using NTSYS2.10e software. When coefficient was 0.75, 109 cultivars were divided into 11 categories and categories I contain 79 varieties by UPGMA cluster analysis. The test varieties divided into 7 sub-groups when categories were 0.75, which show a close genetic relationship. Results advised that Hunan is the main producing area of C. oleifera, with enriched C. oleifera variety and complex topography, and therefore has a high genetic diversity. Meanwhile, the main varieties of C. oleifera in Hubei are imported from Hunan, which results in fewer varieties and reduces the genetic diversity of C. oleifera. The ISSR profiles can improve C. oleifera germplasm management and provide potential determine correlations between different varieties and its distribution in different province

Immunotherapeutic implications on targeting the cytokines produced in rhinovirus-induced immunoreactions

Rhinovirus is a widespread virus associated with several respiratory diseases, especially asthma exacerbation. Currently, there are no accurate therapies for rhinovirus. Encouragingly, it is found that during rhinovirus-induced immunoreactions the levels of certain cytokines in patients' serum will alter. These cytokines may have pivotal pro-inflammatory or anti-inflammatory effects via their specific mechanisms. Thus far, studies have shown that inhibitions of cytokines such as IL-1, IL-4, IL-5, IL-6, IL-13, IL-18, IL-25, and IL-33 may attenuate rhinovirus-induced immunoreactions, thereby relieving rhinovirus infection. Furthermore, such therapeutics for rhinovirus infection can be applied to viruses of other species, with certain practicability

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models

Overexpression of RRM2 decreases thrombspondin-1 and increases VEGF production in human cancer cells in vitro and in vivo: implication of RRM2 in angiogenesis

<p>Abstract</p> <p>Background</p> <p>In addition to its essential role in ribonucleotide reduction, ribonucleotide reductase (RNR) small subunit, RRM2, has been known to play a critical role in determining tumor malignancy. Overexpression of RRM2 significantly enhances the invasive and metastatic potential of tumor. Angiogenesis is critical to tumor malignancy; it plays an essential role in tumor growth and metastasis. It is important to investigate whether the angiogenic potential of tumor is affected by RRM2.</p> <p>Results</p> <p>We examined the expression of antiangiogenic thrombospondin-1 (TSP-1) and proangiogenic vascular endothelial growth factor (VEGF) in two RRM2-overexpressing KB cells: KB-M2-D and KB-HURs. We found that TSP-1 was significantly decreased in both KB-M2-D and KB-HURs cells compared to the parental KB and mock transfected KB-V. Simultaneously, RRM2-overexpressing KB cells showed increased production of VEGF mRNA and protein. In contrast, attenuating RRM2 expression via siRNA resulted in a significant increased TSP-1 expression in both KB and LNCaP cells; while the expression of VEGF by the two cells was significantly decreased under both normoxia and hypoxia. In comparison with KB-V, overexpression of RRM2 had no significant effect on proliferation in vitro, but it dramatically accelerated in vivo subcutaneous growth of KB-M2-D. KB-M2-D possessed more angiogenic potential than KB-V, as shown in vitro by its increased chemotaxis for endothelial cells and in vivo by the generation of more vascularized tumor xenografts.</p> <p>Conclusion</p> <p>These findings suggest a positive role of RRM2 in tumor angiogenesis and growth through regulation of the expression of TSP-1 and VEGF.</p