Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Background: Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas. Methods: We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance. Results: Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively. Conclusions: The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Renal interstitial fibrotic assessment using non-Gaussian diffusion kurtosis imaging in a rat model of hyperuricemia.

BACKGROUND: To investigate the feasibility of Diffusion Kurtosis Imaging (DKI) in assessing renal interstitial fibrosis induced by hyperuricemia. METHODS: A hyperuricemia rat model was established, and the rats were randomly split into the hyperuricemia (HUA), allopurinol (AP), and AP + empagliflozin (AP + EM) groups (n = 19 per group). Also, the normal rats were selected as controls (CON, n = 19). DKI was performed before treatment (baseline) and on days 1, 3, 5, 7, and 9 days after treatment. The DKI indicators, including mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) of the cortex (CO), outer stripe of the outer medulla (OS), and inner stripe of the outer medulla (IS) were acquired. Additionally, hematoxylin and eosin (H&E) staining, Masson trichrome staining, and nuclear factor kappa B (NF-κB) immunostaining were used to reveal renal histopathological changes at baseline, 1, 5, and 9 days after treatment. RESULTS: The HUA, AP, and AP + EM group MKOS and MKIS values gradually increased during this study. The HUA group exhibited the highest MK value in outer medulla. Except for the CON group, all the groups showed a decreasing trend in the FA and MD values of outer medulla. The HUA group exhibited the lowest FA and MD values. The MKOS and MKIS values were positively correlated with Masson's trichrome staining results (r = 0.687, P < 0.001 and r = 0.604, P = 0.001, respectively). The MDOS and FAIS were negatively correlated with Masson's trichrome staining (r = -626, P < 0.0014 and r = -0.468, P = 0.01, respectively). CONCLUSION: DKI may be a non-invasive method for monitoring renal interstitial fibrosis induced by hyperuricemia.

Crocus genome reveals the evolutionary origin of crocin biosynthesis.

Crocus sativus (saffron) is a globally autumn-flowering plant, and its stigmas are the most expensive spice and valuable herb medicine. Crocus specialized metabolites, crocins, are biosynthesized in distant species, Gardenia (eudicot) and Crocus (monocot), and the evolution of crocin biosynthesis remains poorly understood. With the chromosome-level Crocus genome assembly, we revealed that two rounds of lineage-specific whole genome triplication occurred, contributing important roles in the production of carotenoids and apocarotenoids. According to the kingdom-wide identification, phylogenetic analysis, and functional assays of carotenoid cleavage dioxygenases (CCDs), we deduced that the duplication, site positive selection, and neofunctionalization of Crocus-specific CCD2 from CCD1 members are responsible for the crocin biosynthesis. In addition, site mutation of CsCCD2 revealed the key amino acids, including I143, L146, R161, E181, T259, and S292 related to the catalytic activity of zeaxanthin cleavage. Our study provides important insights into the origin and evolution of plant specialized metabolites, which are derived by duplication events of biosynthetic genes.

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques.

Background: Amyloid-ß (Aß) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aß plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aß plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aß42/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aß42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, Aß+ rate 49.5%) and BAI (mean age 66.2, Aß+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aß42/40 (0.73 and 0.81) predictors. CONCLUSIONS: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aß plaques and structural MRI.

Dual-signal amplified electrochemical aptasensor based on Au/MrGO and DNA nanospheres for ultra-sensitive detection of BPA without directly modified working electrode.

Rapid and sensitive analysis of bisphenol A (BPA) is essential for preventing health risks to humans and animals. Hence, a signal-amplified electrochemical aptasensor without repetitive polishing and modification of working electrode was developed for BPA using Au-decorated magnetic reduced graphene oxide (Au/MrGO)-based recognition probe (RP) and DNA nanospheres (DNS)-based signal probe (SP) cooperative signal amplification. The DNS served as a signal molecule carrier and signal amplifier, while Au/MrGO acted as a signal amplifier and excellent medium for magnetic adsorption and separation. Moreover, utilizing the excellent magnetic properties of Au/MrGO eliminates the need for repetitive polishing and multi-step direct modification of the working electrode while ensuring that all detection processes take place in solution and that used Au/MrGO can be easily recycled. The proposed aptasensor exhibited not only good stability and selectivity, but also excellent sensitivity with a limit of detection (LOD) of 8.13 fg/mL (S/N = 3). The aptasensor's practicality was proven by spiking recovery tests on actual water samples and comparing the results with those detected by HPLC. The excellent sensitivity and selectivity make this aptasensor an alternative and promising avenue for rapid detection of BPA in environmental monitoring.

Hierarchical Self-Attention Network for Industrial Data Series Modeling With Different Sampling Rates Between the Input and Output Sequences.

For industrial processes, it is significant to carry out the dynamic modeling of data series for quality prediction. However, there are often different sampling rates between the input and output sequences. For the most traditional data series models, they have to carefully select the labeled sample sequence to build the dynamic prediction model, while the massive unlabeled input sequences between labeled samples are directly discarded. Moreover, the interactions of the variables and samples are usually not fully considered for quality prediction at each labeled step. To handle these problems, a hierarchical self-attention network (HSAN) is designed for adaptive dynamic modeling. In HSAN, a dynamic data augmentation is first designed for each labeled step to include the unlabeled input sequences. Then, a self-attention layer of variable level is proposed to learn the variable interactions and short-interval temporal dependencies. After that, a self-attention layer of sample level is further developed to model the long-interval temporal dependencies. Finally, a long short-term memory network (LSTM) network is constructed to model the new sequence that contains abundant interactions for quality prediction. The experiment on an industrial hydrocracking process shows the effectiveness of HSAN.

Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study.

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann-Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.

Dapagliflozin ameliorates hepatic steatosis via suppressing LXRα-mediated synthesis of lipids and bile acids.

Nonalcoholic fatty liver disease (NAFLD) prevalence is rising globally with no pharmacotherapies approved. Hepatic steatosis is closely associated with progression and prognosis of NAFLD. Dapagliflozin, kind of sodium-glucose cotransporter 2 (SGLT2) inhibitor, was found to improve NAFLD in clinical trials, while the underlying mechanism remains poorly elucidated. Here, we reported that dapagliflozin effectively mitigated liver injury and relieved lipid metabolism disorders in vivo. Further investigation showed that dapagliflozin markedly suppressed Liver X Receptor α (LXRα)-mediated synthesis of de novo lipids and bile acids (BAs). In AML12 cells, our results proved dapagliflozin decreased lipid contents via inhibiting the expression of LXRα and downstream liposynthesis genes. Proteosome inhibitor MG132 eliminated the effect of dapagliflozin on LXRα-mediated signaling pathway, which suggested that dapagliflozin downregulated LXRα expression through increasing LXRα degradation. Knockdown of LXRα with siRNA abolished the reduction of lipogenesis from dapagliflozin treatment, indicating that LXRα might be the pivotal target for dapagliflozin to exhibit the aforementioned benefits. Furthermore, the data showed that dapagliflozin reversed gut dysbiosis induced by BAs disruption and altered gut microbiota profile to reduce intestinal lipids absorption. Together, our study deciphered a novel mechanism by which dapagliflozin relieved hepatic steatosis and highlighted the potential benefit of dapagliflozin in treating NAFLD.

The receptor binding domain of SARS-CoV-2 Omicron subvariants targets Siglec-9 to decrease its immunogenicity by preventing macrophage phagocytosis.

The development of a vaccine specific to severe acute respiratory syndrome coronavirus 2 Omicron has been hampered due to its low immunogenicity. Here, using reverse mutagenesis, we found that a phenylalanine-to-serine mutation at position 375 (F375S) in the spike protein of Omicron to revert it to the sequence found in Delta and other ancestral strains significantly enhanced the immunogenicity of Omicron vaccines. Sequence FAPFFAF at position 371-377 in Omicron spike had a potent inhibitory effect on macrophage uptake of receptor-binding domain (RBD) nanoparticles or spike-pseudovirus particles containing this sequence. Omicron RBD enhanced binding to Siglec-9 on macrophages to impair phagocytosis and antigen presentation and promote immune evasion, which could be abrogated by the F375S mutation. A bivalent F375S Omicron RBD and Delta-RBD nanoparticle vaccine elicited potent and broad nAbs in mice, rabbits and rhesus macaques. Our research suggested that manipulation of the Siglec-9 pathway could be a promising approach to enhance vaccine response.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

Targeted and untargeted metabolomics reveals meat quality in grazing yak during different phenology periods on the Qinghai-Tibetan Plateau.

Yak meat is more popular among consumers because of its high nutritional value, but little attention has been paid to its meat quality, which is affected by different phenology periods grass. We hypothesized that seasonal variations in grass composition influenced the ruminal bacteria community, and eventually affected the meat quality of yaks. This study aims to investigate the relationship of meat quality in grazing yak as well as the key rumen bacteria using targeted and untargeted metabolomics and 16S rRNA during different phenology periods. The main three altered metabolic pathways in grazing yak, including amino acids biosynthesis, glutathione metabolism, and fatty acids biosynthesis, were found in the grass period (GP) group compared to the regreen period (RP) and hay period (HP) groups. The GP group had higher concentrations of flavor amino acids (FAA), polyunsaturated fatty acids (PUFA), and a lower ratio of n-6/n-3 compared with the RP group. Correlation analysis results showed that Rikenellaceae_RC9_gut_group was positively correlated with fatty acids and lipid metabolites, which might be involved in lipid metabolism. Pediococcus had a positive correlation with biological peptides, which could be involved in the metabolism of bioactive compounds. In conclusion, grass in different phenology periods was associated with modified amino acids and fatty acids composition of yak meat as well as altered regulation of biological pathways, which was correlated with changes in rumen bacterial communities.

A Robust Causal Brain Network Measure and Its Application on Ictal Electrocorticogram Analysis of Drug-resistant Epilepsy.

Measuring causal brain network is a significant topic for exploring complex brain functions. While various data-driven algorithms have been proposed, they still have some drawbacks such as ignoring time non-separability, cumbersome parameter settings, and poor robustness. To solve these deficiencies, we developed a novel framework: "time-shift permutation cross-mapping, TPCM," integrating steps of (1) delayed improved phase-space reconstruction (DIPSR), (2) rank transformation of embedding vectors' distances, (3) cross-mapping with a fitting estimation, and (4) causality quantification using multi-delays. Based on synthetic models and comparison with baseline methods, numerical validation results demonstrate that TPCM significantly improves the robustness for data length with or without noise interference, and achieves the best quantification accuracy in detecting time delay and coupling strength, with the highest determination coefficient ( R2 = 0. 96 ) of fitting verse coupling parameters. The developed TPCM was finally applied to ictal electrocorticogram (ECoG) analysis of patients with drug-resistant epilepsy (DRE). A total of 17 patients with DRE were included into the retrospective study. For 8 patients undergoing successful surgeries, the causal coupling strength (0.58 ± 0.20) within epileptogenic zone network is significantly higher than those suffering failed surgeries (0.38 ± 0.16) with P < 0. 001 through Mann-Whitney-U-test. Therefore, the epileptic brain network measured by TPCM is a credible biomarker for predicting surgical outcomes. These findings additionally confirm TPCM's superior performance and promising potential to advance precision medicine for neurological disorders.

Dictyophora indusiata polysaccharide mediates priming of the NLRP3 inflammasome activation via TLR4/ NF-κB signaling pathway to exert immunostimulatory effects.

Dictyophora indusiata, commonly known as bamboo fungus, is a type of edible mushroom that is highly popular worldwide for its rich flavor and nutritional value. It is also recognized for its pharmaceutical efficacy, with medicinal benefits attributed to its consumption. One of the most important components of Dictyophora indusiata is polysaccharide, which has been acknowledged as a promising regulator of biological response due to its immunostimulatory and anti-inflammatory properties. However, the specific roles of polysaccharide in modulating the NOD-like receptor protein 3 (NLRP3) inflammasome activation within macrophages remain relatively under-researched. To investigate this further, the mechanism by which Dictyophora indusiata polysaccharide (DIP) exerts its immunostimulatory activity in RAW 264.7 macrophages was analyzed. Results indicated that DIP has the potential to facilitate the priming of NLRP3 inflammasome activation by enhancing TLR4 expression, phosphorylation of IκB-α, and nuclear translocation of NF-κB p65 subunit. It was noted that DIP was unable to mediate the second step of NLRP3 inflammasome activation. The findings of this study provide compelling evidence that DIP has immunomodulatory effects by modulating the NLRP3 inflammasome in RAW264.7 macrophages.

A Dual-domain Engineered Antibody for Efficient HBV Suppression and Immune Responses Restoration.

Chronic hepatitis B (CHB) remains a major public health concern because of the inefficiency of currently approved therapies in clearing the hepatitis B surface antigen (HBsAg). Antibody-based regimens have demonstrated potency regarding virus neutralization and HBsAg clearance. However, high dosages or frequent dosing are required for virologic control. In this study, a dual-domain-engineered anti-hepatitis B virus (HBV) therapeutic antibody 73-DY is developed that exhibits significantly improved efficacy regarding both serum and intrahepatic viral clearance. In HBV-tolerant mice, administration of a single dose of 73-DY at 2 mg kg-1 is sufficient to reduce serum HBsAg by over 3 log10 IU mL-1 and suppress HBsAg to < 100 IU mL-1 for two weeks, demonstrating a dose-lowering advantage of at least tenfold. Furthermore, 10 mg kg-1 of 73-DY sustainably suppressed serum viral levels to undetectable levels for ≈ 2 weeks. Molecular analyses indicate that the improved efficacy exhibited by 73-DY is attributable to the synergy between fragment antigen binding (Fab) and fragment crystallizable (Fc) engineering, which conferred sustained viral suppression and robust viral eradication, respectively. Long-term immunotherapy with reverse chimeric 73-DY facilitated the restoration of anti-HBV immune responses. This study provides a foundation for the development of next-generation antibody-based CHB therapies.

Effects of the alpine meadow in different phenological periods on rumen fermentation and gastrointestinal tract bacteria community in grazing yak on the Qinghai-Tibetan Plateau.

BACKGROUND: In this study, we investigated the effects of alpine meadow in different phenological periods on ruminal fermentation, serum biochemical indices, and gastrointestinal tract microbes in grazing yak on the Qinghai-Tibetan Plateau. A total of eighteen female freely grazing yaks with an average age of 3 years old and a body weight of 130 ± 19 kg were selected. According to the plant phenological periods, yaks were randomly allocated to one of three treatments: (1) regreen periods group (RP, n = 6); (2) grassy periods group (GP, n = 6); and (3) hay periods group (HP, n = 6). At the end of the experiment, the blood, rumen fluids, and rectal contents were collected to perform further analysis. RESULTS: The concentrations of total volatile fatty acid (TVFA), acetate, glucose (GLU), triglyceride (TG), cholesterol (CHO), high density lipoprotein (HDL), and low density lipoprotein (LDL) were higher in the GP group than in the HP group (P < 0.05). However, compared with the RP and GP groups, the HP group had higher concentrations of isobutyrate, isovalerate, valerate, and creatinine (CREA) (P < 0.05). The abundance of Prevotella in the rumen, and the abundances of Rikenellaceae_RC9_gut_group, Eubacterium_coprostanoligenes_group, and Prevotellaceae_UCG-004 in the gut were higher in the GP group compared with the HP group (P < 0.05). The HP had higher abundance of Eubacterium_coprostanoligenes_group in the rumen as well as the abundances of Romboutsia and Arthrobacter in the gut compared with the RP and GP groups (P < 0.05). CONCLUSIONS: Based on the results of rumen fermentation, serum biochemical, differential biomarkers, and function prediction, the carbohydrate digestion of grazing yak would be higher with the alpine meadow regreen and grassy due to the gastrointestinal tract microbes. However, the risk of microbe disorders and host inflammation in grazing yak were higher with the alpine meadow wither.

Cepharanthine analogs mining and genomes of Stephania accelerate anti-coronavirus drug discovery.

Cepharanthine is a secondary metabolite isolated from Stephania. It has been reported that it has anti-conronaviruses activities including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Here, we assemble three Stephania genomes (S. japonica, S. yunnanensis, and S. cepharantha), propose the cepharanthine biosynthetic pathway, and assess the antiviral potential of compounds involved in the pathway. Among the three genomes, S. japonica has a near telomere-to-telomere assembly with one remaining gap, and S. cepharantha and S. yunnanensis have chromosome-level assemblies. Following by biosynthetic gene mining and metabolomics analysis, we identify seven cepharanthine analogs that have broad-spectrum anti-coronavirus activities, including SARS-CoV-2, Guangxi pangolin-CoV (GX_P2V), swine acute diarrhoea syndrome coronavirus (SADS-CoV), and porcine epidemic diarrhea virus (PEDV). We also show that two other genera, Nelumbo and Thalictrum, can produce cepharanthine analogs, and thus have the potential for antiviral compound discovery. Results generated from this study could accelerate broad-spectrum anti-coronavirus drug discovery.

sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin.

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.

Astaxanthin Inhibits H2O2-Induced Excessive Mitophagy and Apoptosis in SH-SY5Y Cells by Regulation of Akt/mTOR Activation.

Oxidative stress, which damages cellular components and causes mitochondrial dysfunction, occurs in a variety of human diseases, including neurological disorders. The clearance of damaged mitochondria via mitophagy maintains the normal function of mitochondria and facilitates cell survival. Astaxanthin is an antioxidant known to have neuroprotective effects, but the underlying mechanisms remain unclear. This study demonstrated that astaxanthin inhibited H2O2-induced apoptosis in SH-SY5Y cells by ameliorating mitochondrial damage and enhancing cell survival. H2O2 treatment significantly reduced the levels of activated Akt and mTOR and induced mitophagy, while pretreatment with astaxanthin prevented H2O2-induced inhibition of Akt and mTOR and attenuated H2O2-induced mitophagy. Moreover, the inhibition of Akt attenuated the protective effect of astaxanthin against H2O2-induced cytotoxicity. Taken together, astaxanthin might inhibit H2O2-induced apoptosis by protecting mitochondrial function and reducing mitophagy. The results also indicate that the Akt/mTOR signaling pathway was critical for the protection of astaxanthin against H2O2-induced cytotoxicity. The results from the present study suggest that astaxanthin can reduce neuronal oxidative injury and may have the potential to be used for preventing neurotoxicity associated with neurodegenerative diseases.

The facial microbiome and metabolome across different geographic regions.

IMPORTANCE: Characterization of the skin microbiome and metabolome across geography will help uncover the climate factors behind the prevalence of skin disorders and provide suggestions for skincare products for people living in different geographic regions.

Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process.

This article studies the performance monitoring problem for the potassium chloride flotation process, which is a critical component of potassium fertilizer processing. To address its froth image segmentation problem, this article proposes a multiscale feature extraction and fusion network (MsFEFNet) to overcome the multiscale and weak edge characteristics of potassium chloride flotation froth images. MsFEFNet performs simultaneous feature extraction at multiple image scales and automatically learns spatial information of interest at each scale to achieve efficient multiscale information fusion. In addition, the potassium chloride flotation process is a multistage dynamic process with massive unlabeled data. To overcome its dynamic time-varying and working condition spatial similarity characteristics, a semi-supervised froth-grade prediction model based on a temporal-spatial neighborhood learning network combined with Mean Teacher (MT-TSNLNet) is proposed. MT-TSNLNet designs a new objective function for learning the temporal-spatial neighborhood structure of data. The introduction of Mean Teacher can further utilize unlabeled data to promote the proposed prediction model to better track the concentrate grade. To verify the effectiveness of the proposed MsFEFNet and MT-TSNLNet, froth image segmentation and grade prediction experiments are performed on a real-world potassium chloride flotation process dataset.