Alzheimer's disease risk allele of PICALM causes detrimental lipid droplets in microglia.

Despite genome-wide association studies of late-onset Alzheimer's disease (LOAD) having identified many genetic risk loci1-6, the underlying disease mechanisms remain largely unknown. Determining causal disease variants and their LOAD-relevant cellular phenotypes has been a challenge. Leveraging our approach for identifying functional GWAS risk variants showing allele-specific open chromatin (ASoC)7, we systematically identified putative causal LOAD risk variants in human induced pluripotent stem cells (iPSC)-derived neurons, astrocytes, and microglia (MG) and linked PICALM risk allele to a previously unappreciated MG-specific role of PICALM in lipid droplet (LD) accumulation. ASoC mapping uncovered functional risk variants for 26 LOAD risk loci, mostly MG-specific. At the MG-specific PICALM locus, the LOAD risk allele of rs10792832 reduced transcription factor (PU.1) binding and PICALM expression, impairing the uptake of amyloid beta (Aß) and myelin debris. Interestingly, MG with PICALM risk allele showed transcriptional enrichment of pathways for cholesterol synthesis and LD formation. Genetic and pharmacological perturbations of MG further established a causal link between the reduced PICALM expression, LD accumulation, and phagocytosis deficits. Our work elucidates the selective LOAD vulnerability in microglia for the PICALM locus through detrimental LD accumulation, providing a neurobiological basis that can be exploited for developing novel clinical interventions.

Functionalized lignin nanoparticles assembled with MXene reinforced polypropylene with favorable UV-aging resistance, electromagnetic shielding effects and superior fire-safety.

Deterioration in mechanical performances and aging resistance due to the introduction of flame retardants is a major obstacle for bio-based fire-safety polypropylene (PP). Herein, we reported a kind of functionalized lignin nanoparticles assembled with MXene (MX@LNP), and applied it to construct the flame-retardant PP composites (PP-MA) with superior fire safety, excellent mechanical performance, electromagnetic shielding effects and aging resistance. Specifically, the PP-MA doped with only 18 wt% flame-retardant additives (PP-MA18) achieved the UL-94 V-0 rating. In comparison to pure PP, PP-MA18 presented a greatly decreased peak of heat release rate (pHRR), total heat rate (THR), and peak smoke production rate (pSPR) by 79.7 %, 69.0 % and 75.8 %, respectively, and satisfactory decrease in total flammable and toxic volatiles evolved. The formed fine solid microstructure of carbon residuals effectively promoted the compactness of char layers. More importantly, the nano-effect and the strong interface interaction between the complexed MX@LNP and PP enhanced the tensile strength (45.78 MPa) and elongation at break (725.95 %) of PP-MA. Additionally, the significant ultraviolet absorption and electromagnetic wave dissipation performance of MXene and lignin enabled excellent aging resistance and electromagnetic shielding effects of PP-MA compared with PP. This achieved MX@LNP afforded a novel approach for developing flame retardant materials with excellent application performance.

3D Visual Discomfort Assessment With a Weakly Supervised Graph Convolution Neural Network Based on Inaccurately Labeled EEG.

Visual discomfort significantly limits the broader application of stereoscopic display technology. Hence, the accurate assessment of stereoscopic visual discomfort is a crucial topic in this field. Electroencephalography (EEG) data, which can reflect changes in brain activity, have received increasing attention in objective assessment research. However, inaccurately labeled data, resulting from the presence of individual differences, restrict the effectiveness of the widely used supervised learning methods in visual discomfort assessment tasks. Simultaneously, visual discomfort assessment methods should pay greater attention to the information provided by the visual cortical areas of the brain. To tackle these challenges, we need to consider two key aspects: maximizing the utilization of inaccurately labeled data for enhanced learning and integrating information from the brain's visual cortex for feature representation purposes. Therefore, we propose the weakly supervised graph convolution neural network for visual discomfort (WSGCN-VD). In the classification part, a center correction loss serves as a weakly supervised loss, employing a progressive selection strategy to identify accurately labeled data while constraining the involvement of inaccurately labeled data that are influenced by individual differences during the model learning process. In the feature extraction part, a feature graph module pays particular attention to the construction of spatial connections among the channels in the visual regions of the brain and combines them with high-dimensional temporal features to obtain visually dependent spatio-temporal representations. Through extensive experiments conducted in various scenarios, we demonstrate the effectiveness of our proposed model. Further analysis reveals that the proposed model mitigates the impact of inaccurately labeled data on the accuracy of assessment.

Recombinant therapeutic proteins degradation and overcoming strategies in CHO cells.

Mammalian cell lines are frequently used as the preferred host cells for producing recombinant therapeutic proteins (RTPs) having post-translational modified modification similar to those observed in proteins produced by human cells. Nowadays, most RTPs approved for marketing are produced in Chinese hamster ovary (CHO) cells. Recombinant therapeutic antibodies are among the most important and promising RTPs for biomedical applications. One of the issues that occurs during development of RTPs is their degradation, which caused by a variety of factors and reducing quality of RTPs. RTP degradation is especially concerning as they could result in reduced biological functions (antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity) and generate potentially immunogenic species. Therefore, the mechanisms underlying RTP degradation and strategies for avoiding degradation have regained an interest from academia and industry. In this review, we outline recent progress in this field, with a focus on factors that cause degradation during RTP production and the development of strategies for overcoming RTP degradation. KEY POINTS: • The recombinant therapeutic protein degradation in CHO cell systems is reviewed. • Enzymatic factors and non-enzymatic methods influence recombinant therapeutic protein degradation. • Reducing the degradation can improve the quality of recombinant therapeutic proteins.

Novel Techniques and Models for Studying the Role of the Gut Microbiota in Drug Metabolism.

The gut microbiota, known as the second human genome, plays a vital role in modulating drug metabolism, significantly impacting therapeutic outcomes and adverse effects. Emerging research has elucidated that the microbiota mediates a range of modifications of drugs, leading to their activation, inactivation, or even toxication. In diverse individuals, variations in the gut microbiota can result in differences in microbe-drug interactions, underscoring the importance of personalized approaches in pharmacotherapy. However, previous studies on drug metabolism in the gut microbiota have been hampered by technical limitations. Nowadays, advances in biotechnological tools, such as microbially derived metabolism screening and microbial gene editing, have provided a deeper insight into the mechanism of drug metabolism by gut microbiota, moving us toward personalized therapeutic interventions. Given this situation, our review summarizes recent advances in the study of gut-microbiota-mediated drug metabolism and showcases techniques and models developed to navigate the challenges posed by the microbial involvement in drug action. Therefore, we not only aim at understanding the complex interaction between the gut microbiota and drugs and outline the development of research techniques and models, but we also summarize the specific applications of new techniques and models in researching gut-microbiota-mediated drug metabolism, with the expectation of providing new insights on how to study drug metabolism by gut microbiota.

Brain Lipids and Lipid Droplet Dysregulation in Alzheimer's Disease and Neuropsychiatric Disorders.

Background: Lipids are essential components of the structure and for the function of brain cells. The intricate balance of lipids, including phospholipids, glycolipids, cholesterol, cholesterol ester, and triglycerides, is crucial for maintaining normal brain function. The roles of lipids and lipid droplets and their relevance to neurodegenerative and neuropsychiatric disorders (NPDs) remain largely unknown. Summary: Here, we reviewed the basic role of lipid components as well as a specific lipid organelle, lipid droplets, in brain function, highlighting the potential impact of altered lipid metabolism in the pathogenesis of Alzheimer's disease (AD) and NDPs. Key Messages: Brain lipid dysregulation plays a pivotal role in the pathogenesis and progression of neurodegenerative and NPDs including AD and schizophrenia. Understanding the cell type-specific mechanisms of lipid dysregulation in these diseases is crucial for identifying better diagnostic biomarkers and for developing therapeutic strategies aiming at restoring lipid homeostasis.

Improved brain community structure detection by two-step weighted modularity maximization.

The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.

Association Analysis of Transcriptome and Targeted Metabolites Identifies Key Genes Involved in Iris germanica Anthocyanin Biosynthesis.

The anthocyanin biosynthetic pathway is the main pathway regulating floral coloration in Iris germanica, a well-known ornamental plant. We investigated the transcriptome profiles and targeted metabolites to elucidate the relationship between genes and metabolites in anthocyanin biosynthesis in the bitone flower cultivar 'Clarence', which has a deep blue outer perianth and nearly white inner perianth. In this study, delphinidin-, pelargonidin-, and cyanidin-based anthocyanins were detected in the flowers. The content of delphinidin-based anthocyanins increased with the development of the flower. At full bloom (stage 3), delphinidin-based anthocyanins accounted for most of the total anthocyanin metabolites, whereas the content of pelargonidin- and cyanidin-based anthocyanins was relatively low. Based on functional annotations, a number of novel genes in the anthocyanin pathway were identified, which included early biosynthetic genes IgCHS, IgCHI, and IgF3H and late biosynthetic genes Ig F3'5'H, IgANS, and IgDFR. The expression of key structural genes encoding enzymes, such as IgF3H, Ig F3'5'H, IgANS, and IgDFR, was significantly upregulated in the outer perianth compared to the inner perianth. In addition, most structural genes exhibited their highest expression at the half-color stage rather than at the full-bloom stage, which indicates that these genes function ahead of anthocyanins synthesis. Moreover, transcription factors (TFs) of plant R2R3-myeloblastosis (R2R3-MYB) related to the regulation of anthocyanin biosynthesis were identified. Among 56 R2R3-MYB genes, 2 members belonged to subgroup 4, with them regulating the expression of late biosynthetic genes in the anthocyanin biosynthetic pathway, and 4 members belonged to subgroup 7, with them regulating the expression of early biosynthetic genes in the anthocyanin biosynthetic pathway. Quantitative real-time PCR (qRT-PCR) analysis was used to validate the data of RNA sequencing (RNA-Seq). The relative expression profiles of most candidate genes were consistent with the FPKM of RNA-seq. This study identified the key structural genes encoding enzymes and TFs that affect anthocyanin biosynthesis, which provides a basis and reference for the regulation of plant anthocyanin biosynthesis in I. germanica.

The relationship between self-control and mental health problems among Chinese university students.

Background: Mental health issues are often associated with poor self-control. Therefore, effective interventions against mental health problems should include self-control training. However, it is unclear whether the effect of self-control varies across different types of mental health problems. Methods: A cross-sectional survey was conducted using the convenience sampling method at five universities in Chongqing, China, where 1,409 students reported their demographic information, level of self-control, and symptoms of irritability, depression, and anxiety. Descriptive statistical methods and a network analysis approach were employed to explore the relationship between self-control and symptoms of irritability, depression, and anxiety among 1,409 students. The bridging links between self-control and the three mental health problems were analyzed. Results: The findings revealed a negative correlation between self-control and symptoms of irritability, depression, and anxiety among university students. Impulse control was found to be the bridge between self-control and irritability or anxiety symptoms, while resistance to temptation was the bridge between self-control and depressive symptoms. Conclusion: These results demonstrate the different relationship between self-control with irritability, anxiety, and depressive symptoms. The findings of this study may shed light on future mental health interventions for university students during potential public health emergencies, such as prior knowledge of the main types of psychological problems among university students, which may allow for the development of precise self-control intervention strategies, such as targeting impulsivity or resistance to temptation.

Hepatocytic lipocalin-2 controls HDL metabolism and atherosclerosis via Nedd4-1-SR-BI axis in mice.

High-density lipoprotein (HDL) metabolism is regulated by complex interplay between the scavenger receptor group B type 1 (SR-BI) and multiple signaling molecules in the liver. Here, we show that lipocalin-2 (Lcn2) is a key regulator of hepatic SR-BI, HDL metabolism, and atherosclerosis. Overexpression of human Lcn2 in hepatocytes attenuates the development of atherosclerosis via SR-BI in western-diet-fed Ldlr-/- mice, whereas hepatocyte-specific ablation of Lcn2 has the opposite effect. Mechanistically, hepatocyte Lcn2 improves HDL metabolism and alleviates atherogenesis by blocking Nedd4-1-mediated SR-BI ubiquitination at K500 and K508. The Lcn2-improved HDL metabolism is abolished in mice with hepatocyte-specific Nedd4-1 or SR-BI deletion and in SR-BI (K500A/K508A) mutation mice. This study identifies a regulatory axis from Lcn2 to HDL via blocking Nedd4-1-mediated SR-BI ubiquitination and demonstrates that hepatocyte Lcn2 may be a promising target to improve HDL metabolism to treat atherosclerotic cardiovascular diseases.

The expression and clinical value of LRP11, FUBP1 and TET1 in cervical cancer.

Cervical cancer is the second leading cause of cancer death among women worldwide. Identification of effective genes along with biological markers as targeting agents is very necessary for the diagnosis and treatment of this disease. Bioinformatics techniques along with genetic and molecular investigations have provided the possibility of studying different levels of information such as the genome, transcriptome, proteome, and metabolize with high depth and accuracy. The collection of these data provides comprehensive and valuable information about the investigated phenotypes, including complex diseases such as cancer. In this study, we examined three genes LRP11, FUBP1, and TET1 related to cervical cancer. The results of this study showed that the level of expression of these genes is high in lymph nodes and the thyroid and is less in the pancreas and liver. Also, the expression level of the FUBP1 gene is higher than that of LRP11, and the expression level of the LRP11 gene is higher than that of TET1. Regarding the structure and proteomics of the studied genes, it can be seen that due to the presence of more domains in the LRP11 and FUBP1 genes, these genes probably independently participate in various functions and have a wider range of activity than the TET1 gene. Also, the analysis of the stability of the examined genes showed that the stability of the FUBP1 gene is relatively higher than that of the TET1 gene, and this gene is also more stable than the LRP11 gene. Considering that these genes are effective key genes for the early detection of cervical cancer, it is hoped that they will be used as markers in the diagnosis and treatment of cervical cancer.

Probing the Effect of Photovoltaic Material on Voc in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning.

The power conversion efficiency (PCE) of ternary polymer solar cells (PSCs) with non-fullerene has a phenomenal increase in recent years. However, improving the open circuit voltage (Voc) of ternary PSCs with non-fullerene still remains a challenge. Therefore, in this work, machine learning (ML) algorithms are employed, including eXtreme gradient boosting, K-nearest neighbor and random forest, to quantitatively analyze the impact mechanism of Voc in ternary PSCs with the double acceptors from the two aspects of photovoltaic materials. In one aspect of photovoltaic materials, the doping concentration has the greatest impact on Voc in ternary PSCs. Furthermore, the addition of the third component affects the energy offset between the donor and acceptor for increasing Voc in ternary PSCs. More importantly, to obtain the maximum Voc in ternary PSCs with the double acceptors, the HOMO and LUMO energy levels of the third component should be around (-5.7 ± 0.1) eV and (-3.6 ± 0.1) eV, respectively. In the other aspect of molecular descriptors and molecular fingerprints in the third component of ternary PSCs with the double acceptors, the hydrogen bond strength and aromatic ring structure of the third component have high impact on the Voc of ternary PSCs. In partial dependence plot, it is clear that when the number of methyl groups is four and the number of carbonyl groups is two in the third component of acceptor, the Voc of ternary PSCs with the double acceptors can be maximized. All of these findings provide valuable insights into the development of materials with high Voc in ternary PSCs for saving time and cost.

A DFT study on methanol decomposition over single atom Pt/CeO2 catalysts: the effect of the position of Pt.

Pt/CeO2 catalysts exhibit excellent catalytic performance for the methanol dehydrogenation (MD) reaction. In this work, MD reactions on three systems of Pt1/CeO2(110)), Pt7/CeO2(110), and Pt1/Ce1-xO2(110) are investigated via density functional theory (DFT) calculations. The CH3OH adsorption, electronic structure of the catalyst, and mechanism of methanol decomposition (MD) are systematically calculated. The results reveal that the d-band center of the Pt atom moves away from the Fermi level in the order of Pt1/CeO2(110) < Pt7/CeO2(110) < Pt1/Ce1-xO2(110), and the order of the activity of the MD reaction is Pt1/CeO2(110) < Pt7/CeO2(110) < Pt1/Ce1-xO2(110). The results of the microkinetic dynamics simulation verify that only Pt1/Ce1-xO2(110) is conducive to the decomposition of methanol at low temperatures (373 K), and the products CO and H2 are easily dissociated from the catalyst surface. This work uncovers that both the small size and the Ce vacancy substituted sites of Pt favor the performance of the Pt/CeO2 catalyst, and provides theoretical guidance for the construction and design of efficient metal-support catalysts for the MD reaction.

Efficacy of Zidovudine-Amikacin Combination Therapy In Vitro and in a Rat Tissue Cage Infection Model against Amikacin-Resistant, Multidrug-Resistant Enterobacteriales.

Multidrug-resistant (MDR) Enterobacteriales infections have become an urgent global threat to public health. The aim of this study was to evaluate the efficacy of zidovudine-amikacin combination therapy in vitro and in vivo. Molecular characteristics and antibiotic resistance profiles of 53 amikacin-resistant MDR, extensively drug-resistant (XDR), or pan-drug-resistant (PDR) clinical isolates were examined via PCR and susceptibility testing. Checkerboard assays were performed for these 53 isolates to assess in vitro synergistic effects of the zidovudine-amikacin combination, and static time-kill experiments were performed for four XDR or PDR Enterobacteriales isolates. A Galleria mellonella model and a rat tissue cage infection model were established to assess in vivo synergistic effects. The aac(6')-Ib gene was detected in 25 (47.2%) isolates, followed by armA in 5 (9.4%) isolates, rmtB in 27 (50.9%) isolates, and rmtC in 3 (5.8%) isolates. Checkerboard assays showed the synergy of this combination against 38 (71.7%) isolates. The time-kill assays further confirmed that zidovudine strongly synergized with amikacin against four XDR or PDR Enterobacteriales isolates. The Galleria mellonella model study showed that the survival benefit of zidovudine-amikacin combination therapy was significantly better than that of monotherapy for those four Enterobacteriales isolates. Furthermore, the rat tissue cage infection model study showed that zidovudine-amikacin combination therapy displayed more potent bactericidal activity than monotherapy after 3 and 7 days of treatment for the above four isolates. Our data support the idea that the zidovudine-amikacin combination could be a plausible alternative therapy against infections with amikacin-resistant MDR Enterobacteriales, especially with XDR and PDR Enterobacteriales. IMPORTANCE Our study revealed for the first time that the zidovudine-amikacin combination shows a significant bactericidal effect against amikacin-resistant MDR, XDR, and PDR Enterobacteriales. Second, using in vitro and in vivo approaches, our study showed that zidovudine strongly synergized with amikacin against amikacin-resistant MDR Enterobacteriales isolates. Most importantly, with regard to survival benefit, pharmacokinetics, and bactericidal effects, our in vivo experiment demonstrated the effectiveness of zidovudine-amikacin.

HIV Tat Expression and Cocaine Exposure Lead to Sex- and Age-Specific Changes of the Microbiota Composition in the Gut.

The balance of microbial communities in the gut is extremely important for normal physiological function. Disruption of the balance is often associated with various disorders and diseases. Both HIV infection and cocaine use are known to change the gut microbiota and the epithelial barrier integrity, which contribute to inflammation and immune activation. Our recent study shows that Tat expression and cocaine exposure result in changes of genome-wide DNA methylation and gene expression and lead to worsen the learning and memory impairments. In the current study, we extended the study to determine effects of Tat and cocaine on the gut microbiota composition. We found that both Tat expression and cocaine exposure increased Alteromonadaceae in 6-month-old female/male mice. In addition, we found that Tat, cocaine, or both increased Alteromonadaceae, Bacteroidaceae, Cyanobiaceae, Erysipelotrichaceae, and Muribaculaceae but decreased Clostridiales_vadinBB60_group, Desulfovibrionaceae, Helicobacteraceae, Lachnospiraceae, and Ruminococcaceae in 12-month-old female mice. Lastly, we analyzed changes of metabolic pathways and found that Tat decreased energy metabolism and nucleotide metabolism, and increased lipid metabolism and metabolism of other amino acids while cocaine increased lipid metabolism in 12-month-old female mice. These results demonstrated that Tat expression and cocaine exposure resulted in significant changes of the gut microbiota in an age- and sex-dependent manner and provide additional evidence to support the bidirectional gut-brain axis hypothesis.

Activating transcription factor 3, glucolipid metabolism, and metabolic diseases.

Lipids and glucose exert many essential physiological functions, such as providing raw materials or energy for cellular biosynthesis, regulating cell signal transduction, and maintaining a constant body temperature. Dysregulation of lipid and glucose metabolism can lead to glucolipid metabolic disorders linked to various metabolic diseases, such as obesity, diabetes, and cardiovascular disease. Therefore, intervention in glucolipid metabolism is a key therapeutic strategy for the treatment of metabolic diseases. Activating transcription factor 3 (ATF3) is a transcription factor that acts as a hub of the cellular adaptive-response network and plays a pivotal role in the regulation of inflammation, apoptosis, DNA repair, and oncogenesis. Emerging evidence has illustrated the vital roles of ATF3 in glucolipid metabolism. ATF3 inhibits intestinal lipid absorption, enhances hepatic triglyceride hydrolysis and fatty acid oxidation, promotes macrophage reverse cholesterol transport, and attenuates the progression of western diet-induced nonalcoholic fatty liver disease and atherosclerosis. In addition to its role in lipid metabolism, ATF3 has also been identified as an important regulator of glucose metabolism. Here, we summarize the recent advances in the understanding of ATF3, mainly focusing on its role in glucose and lipid metabolism and potential therapeutic implications.

Self-assembly preparation of Zn2+ -immobilized silica hybrid monolith and application in solid-phase micro-extraction of ß-agonists.

Exploiting adsorbents with highly efficient extraction performance is of great promise for extracting small organic molecules from biological samples. In this work, a novel Zn2+ -immobilized chitosan@silica hybrid monolith was prepared through a simple self-assembly Zn2+ -immobilization process. Exploited as an adsorbent in solid-phase micro-extraction for extracting trace ß-agonists, the monolith exhibited high extraction efficiencies for salbutamol, clenbuterol, and ractopamine with the enrichment factors approaching 120, 85, and 52, respectively. These could be attributed to the effective interaction between Zn2+ ions and the target molecule via coordination or other intermolecular interactions. Under optimized extraction operations, a sensitive determination was successfully developed coupling with high-performance liquid chromatography-ultraviolet detection. The linear range was 0.17-58.8, 0.12-68.5, and 0.18-65.5 ng/ml for salbutamol, clenbuterol, and ractopamine. The limits of detection of the ß-agonists were from 0.04 to 0.07 ng/ml, and the limits of quantification were from 0.12 to 0.18 ng/ml. The recoveries of spiking in mutton samples were observed in the range of 85.9%-95.7%, with relative standard deviations <8.0% (n = 3). Application tests demonstrated this newly developed determination was practical, accurate, and convenient for detecting trace content ß-agonists in meat.

Research on traceability strategy of food supply chain considering delay effect.

The traceability system has significantly contributed to ensure food safety and quality. However, the biggest difficulty in food traceability is the numerous links from field to table, and there is no stable strategic partnership between supply chain members and the lack of social responsibility of some practitioners. Thus, this study aims to seek the best traceability strategy for companies in centralized model and decentralized model, respectively. Therefore, we have constructed a differential game model based on the delay effect to determine the optimal traceability level and traceable goodwill and compare the profits of the food supply chain (FSC). The results show that the delay time is positively related to the level of traceability effort and has a high impact on the traceable goodwill. Companies in the FSC can formulate optimal traceability strategies based on delay time and foster improvement in food safety and quality.

Statistical iterative spectral CT imaging method based on blind separation of polychromatic projections.

Spectral computed tomography (CT) can provide narrow-energy-width reconstructed images, thereby suppressing beam hardening artifacts and providing rich attenuation information for component characterization. We propose a statistical iterative spectral CT imaging method based on blind separation of polychromatic projections to improve the accuracy of narrow-energy-width image decomposition. For direct inversion in blind scenarios, we introduce the system matrix into the X-ray multispectral forward model to reduce indirect errors. A constrained optimization problem with edge-preserving regularization is established and decomposed into two sub-problems to be alternately solved. Experiments indicate that the novel algorithm obtains more accurate narrow-energy-width images than the state-of-the-art method.

Adaptive Margin Aware Complement-Cross Entropy Loss for Improving Class Imbalance in Multi-View Sleep Staging Based on EEG Signals.

Sleep data are typically characterized by class imbalance, which can cause the model to be overly biased toward frequent classes, resulting in low accuracy of minority class classification. However, the minority class of sleep staging has important value in diagnosing certain disorders, such as an N1 Stage that is too short indicating possible hypersomnia or narcolepsy. To address this problem, we propose a multi-view CNN model based on adaptive margin-aware loss. A novel margin-aware factor that considers the relative sample sizes of both frequent and minority classes can improve the overfitting of minority classes by increasing the regularization strength of minority classes without changing the sample size to maximize the decision margins of minority classes. On this basis, we propose margin-aware cross-entropy and margin-aware complement entropy loss, respectively. Margin-aware complement entropy can be achieved to increase the regularization for minority classes while neutralizing errors for minority classes, thus improving the classification accuracy for minority classes. Finally, the synergy of margin-aware complement entropy and cross-entropy is realized in an adaptive way to improve the sleep staging classification accuracy. We tested on three sleep datasets and compared them with the state-of-the-art, and the results demonstrate that our proposed algorithm not only improves the accuracy of sleep staging in general, but also improves the minority classes to a greater extent.