Multidimensional Uncertainty-Aware Evidential Neural Networks

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.Comment: AAAI 202

The Role of Iron, Its Metabolism and Ferroptosis in Traumatic Brain Injury

Traumatic brain injury (TBI) is a structural and physiological disruption of brain function caused by external forces. It is a major cause of death and disability for patients worldwide. TBI includes both primary and secondary impairments. Iron overload and ferroptosis highly involved in the pathophysiological process of secondary brain injury. Ferroptosis is a form of regulatory cell death, as increased iron accumulation in the brain leads to lipid peroxidation, reactive oxygen species (ROS) production, mitochondrial dysfunction and neuroinflammatory responses, resulting in cellular and neuronal damage. For this reason, eliminating factors like iron deposition and inhibiting lipid peroxidation may be a promising therapy. Iron chelators can be used to eliminate excess iron and to alleviate some of the clinical manifestations of TBI. In this review we will focus on the mechanisms of iron and ferroptosis involving the manifestations of TBI, broaden our understanding of the use of iron chelators for TBI. Through this review, we were able to better find novel clinical therapeutic directions for further TBI study

The Application of Brain Organoid Technology in Stroke Research: Challenges and Prospects

Stroke is a neurological disease responsible for significant morbidity and disability worldwide. However, there remains a dearth of effective therapies. The failure of many therapies for stroke in clinical trials has promoted the development of human cell-based models, such as brain organoids. Brain organoids differ from pluripotent stem cells in that they recapitulate various key features of the human central nervous system (CNS) in three-dimensional (3D) space. Recent studies have demonstrated that brain organoids could serve as a new platform to study various neurological diseases. However, there are several limitations, such as the scarcity of glia and vasculature in organoids, which are important for studying stroke. Herein, we have summarized the application of brain organoid technology in stroke research, such as for modeling and transplantation purposes. We also discuss methods to overcome the limitations of brain organoid technology, as well as future prospects for its application in stroke research. Although there are many difficulties and challenges associated with brain organoid technology, it is clear that this approach will play a critical role in the future exploration of stroke treatment

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Construction of knowledge graph for fully mechanized coal mining equipment based on joint coding

Using knowledge graph technology for data management can achieve effective representation of fully mechanized coal mining equipment. The information with deep mining value can be obtained. The imbalanced data of fully mechanized coal mining equipment and the limited number of entities in certain categories of equipment affect the precision of entity recognition models. In order to solve the above problems, a knowledge graph construction method for fully mechanized coal mining equipment based on joint coding is proposed. Firstly, the fully mechanized coal mining equipment ontology model is constructed, determining the concepts and relationships. Secondly, the entity recognition model is designed. The model uses Token Embedding, Position Embedding, Sentence Embedding, and Task Embedding 4-layer Embedding structures and Transformer Encoder to encode fully mechanized coal mining equipment data, extract dependency relationships and contextual information features between words. The model introduces a Chinese character library, using the Word2vec model for encoding, extracting semantic rules between characters, and solving the problem of rare characters in fully mechanized coal mining equipment data. The model uses the GRU model to jointly encode the data of fully mechanized coal mining equipment and the character vectors encoded in the font library, and fuse vector features. The model uses the Lattice-LSTM model for character decoding to obtain entity recognition results. Finally, the model uses graph database technology to store and organize extracted knowledge in the form of graphs, completing the construction of knowledge graphs. Experimental verification is conducted on the dataset of fully mechanized coal mining equipment. The results show that the method improves the recognition accuracy of fully mechanized coal mining equipment entities by more than 1.26% compared to existing methods, which to some extent alleviates the low accuracy problem caused by insufficient data when constructing a knowledge graph of fully mechanized coal mining equipment in a small sample situation

Digital handover of China-Russia Eastern Gas Pipeline and docking with integrity management system

To achieve the construction goal of "full digital handover, full intelligent operation and full life cycle management" of the China-Russia Eastern Gas Pipeline Project, the basic process and working mechanism of digital handover were proposed, and the significance to pipeline construction and operation management was pointed out. The data docking channel was unblocked by unifying the data acquisition standard. With the full life cycle database as the carrier, the association, storage and sharing of data were realized. Hence, the digital handover was promoted in order to meet the demand of simultaneous delivery of entity pipelines and digital pipelines and ensure the traceability of data history. By establishing an interface with the data warehouse for data calls, the orderly transfer of data from the construction phase to the operation phase was ensured, and the docking application of the digital handover data and the pipeline integrity management system was realized. All of these not only present the value of data to the greatest extent, but also provide data guarantee for intelligent pipeline construction and integrity management

The Role of Nanomaterials in Stroke Treatment: Targeting Oxidative Stress

Stroke has a high rate of morbidity and disability, which seriously endangers human health. In stroke, oxidative stress leads to further damage to the brain tissue. Therefore, treatment for oxidative stress is urgently needed. However, antioxidative drugs have demonstrated obvious protective effects in preclinical studies, but the clinical studies have not seen breakthroughs. Nanomaterials, with their characteristically small size, can be used to deliver drugs and have demonstrated excellent performance in treating various diseases. Additionally, some nanomaterials have shown potential in scavenging reactive oxygen species (ROS) in stroke according to the nature of nanomaterials. The drugs’ delivery ability of nanomaterials has great significance for the clinical translation and application of antioxidants. It increases drug blood concentration and half-life and targets the ischemic brain to protect cells from oxidative stress-induced death. This review summarizes the characteristics and progress of nanomaterials in the application of antioxidant therapy in stroke, including ischemic stroke, hemorrhagic stroke, and neural regeneration. We also discuss the prospect of nanomaterials for the treatment of oxidative stress in stroke and the challenges in their application, such as the toxicity and the off-target effects of nanomaterials

Genome-Wide Identification, Evolution, and Female-Biased Expression Analysis of Odorant Receptors in <i>Tuta absoluta</i> (Lepidoptera: Gelechiidae)

The tomato leafminer, Tuta absoluta (Lepidoptera: Gelechiidae), is a highly destructive invasive pest targeting Solanaceae crops. Its olfactory system plays a crucial role in host location, mate finding, and other behavioral activities. However, there is a notable gap in the literature regarding the characterization of its chemosensory genes. In this study, we conducted a genome-wide identification of 58 odorant receptors (ORs) of T. absoluta. The identified ORs exhibit coding sequence (CDS) lengths ranging from 1062 bp to 1419 bp, encoding proteins of 354 to 473 amino acids. Gene structure analysis showed that the majority of these ORs consist of five, seven, eight, or nine exons, collectively representing 67% of the total ORs identified. Through chromosomal mapping, we identified several tandemly duplicate genes, including TabsOR12a, TabsOR12b, TabsOR12c, TabsOR21a, TabsOR21b, TabsOR34a, TabsOR34b, TabsOR34c, TabsOR62a, and TabsOR62b. The phylogenetic analysis indicated that six TabsORs were clustered within the lepidopteran sex pheromone receptor clade, while an expansion clade containing ten TabsORs resulted from tandem duplication events. Additionally, five TabsORs were classified into a specific OR clade in T. absoluta. Furthermore, through RNA-Seq and RT-qPCR analyses, we identified five TabsORs (TabsOR21a, TabsOR26a, TabsOR34a, TabsOR34c, and TabsOR36) exhibiting female-antennae-biased expression. Our study provides a valuable foundation to further investigations into the molecular and ecological functions of TabsORs, particularly in relation to oviposition behavior. These findings provide foundational data for the future exploration of the functions of female-biased expression OR genes in T. absoluta, thereby facilitating the further development of eco-friendly attract-and-kill techniques for the prevention and control of T. absoluta