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Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis.

The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

In this study, we have, for the first time, demonstrated the beneficial effects of transcranial magneto-acoustic stimulation (TMAS), a technique based on focused ultrasound stimulation within static magnetic field, on the learning and memory abilities and neuroplasticity of the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of Parkinson's disease (PD). Our results showed that chronic TMAS treatment (2 weeks) improved the outcome of Morris water maze, long-term potentiation (LTP), and dendritic spine densities in the dentate gyrus (DG) region of the hippocampus of PD model mice. To further investigate into the underlying mechanisms of these beneficial effects by TMAS, we quantified the proteins in the hippocampus that regulated neuroplasticity. Results showed that the level of postsynaptic density protein 95 was elevated in the brain of TMAS-treated PD model mice while the level of synaptophysin (SYP) did not show any change. We further quantified proteins that mediated neuroplasticity mechanisms, such as brain-derived neurotrophic factor (BDNF) and other important proteins that mediated neuroplasticity. Results showed that TMAS treatment elevated the levels of BDNF, cAMP response element–binding protein (CREB), and protein kinase B (p-Akt) in the PD model mouse hippocampus, but not in the non-PD mouse hippocampus. These results suggest that the beneficial effects on the neuroplasticity of PD model mice treated with TMAS could possibly be conducted through postsynaptic regulations and mediated by BDNF.

Background Increased acid sphingomyelinase (ASMase) activity is associated with insulin resistance and cardiac dysfunction. However, the effects of ASMase on diabetic cardiomyopathy (DCM) and the molecular mechanism(s) underlying remain to be elucidated. We here investigated whether ASMase caused DCM through NADPH oxidase 4-mediated apoptosis. Methods and results We used pharmacological and genetic approaches coupled with study of murine and cell line samples to reveal the mechanisms initiated by ASMase in diabetic hearts. The protein expression and activity of ASMase were upregulated, meanwhile ceramide accumulation was increased in the myocardium of HFD mice. Inhibition of ASMase with imipramine (20 mg Kg −1  d −1 ) or siRNA reduced cardiomyocyte apoptosis, fibrosis, and mitigated cardiac hypertrophy and cardiac dysfunction in HFD mice. The similar effects were observed in cardiomyocytes treated with high glucose (HG, 30 mmol L −1 ) + palmitic acid (PA, 100 μmol L −1 ) or C16 ceramide (CER, 20 μmol L −1 ). Interestingly, the cardioprotective effect of ASMase inhibition was not accompanied by reduced ceramide accumulation, indicating a ceramide-independent manner. The mechanism may involve activated NADPH oxidase 4 (NOX4), increased ROS generation and triggered apoptosis. Suppression of NOX4 with apocynin prevented HG + PA and CER incubation induced Nppb and Myh7 pro-hypertrophic gene expression, ROS production and apoptosis in H9c2 cells. Furthermore, cardiomyocyte-specific ASMase knockout (ASMase Myh6KO ) restored HFD-induced cardiac dysfunction, remodeling, and apoptosis, whereas NOX4 protein expression was downregulated. Conclusions These results demonstrated that HFD-mediated activation of cardiomyocyte ASMase could increase NOX4 expression, which may stimulate oxidative stress, apoptosis, and then cause metabolic cardiomyopathy.

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks. Extensive experiments on two public benchmarks show that our HED-Net performs favorably against state-of-the-art methods.

In tumor therapeutics, the transition from conventional cytotoxic drugs to targeted molecular therapies, such as those targeting receptor tyrosine kinases, has been pivotal. Despite this progress, the clinical outcomes have remained modest, with glioblastoma patients' median survival stagnating at less than 15 months. This underscores the urgent need for more specialized treatment strategies. Our review delves into the progression toward immunomodulation in glioma treatment. We dissect critical discoveries in immunotherapy, such as spotlighting the instrumental role of tumor-associated macrophages, which account for approximately half of the immune cells in the glioma microenvironment, and myeloid-derived suppressor cells. The complex interplay between tumor cells and the immune microenvironment has been explored, revealing novel therapeutic targets. The uniqueness of our review is its exhaustive approach, synthesizing current research to elucidate the intricate roles of various molecules and receptors within the glioma microenvironment. This comprehensive synthesis not only maps the current landscape but also provides a blueprint for refining immunotherapy for glioma, signifying a paradigm shift toward leveraging immune mechanisms for improved patient prognosis.

Due to the low efficiency and high pollution of conventional internal combustion engine vehicles, the fuel cell hybrid electric vehicles are expected to play a key role in the future of clean energy transportation attributed to the long driving range, short hydrogen refueling time and environmental advantages. The development of energy management strategies has an important impact on the economy and durability, but most strategies ignore the aging of fuel cells and the corresponding impact on hydrogen consumption. In this paper, a rule-based fuzzy control strategy is proposed based on the constructed data-driven online estimation model of fuel cell health. Then, a genetic algorithm is used to optimize this fuzzy controller, where the objective function is designed to consider both the economy and durability by combining the hydrogen consumption cost and the degradation cost characterized by the fuel cell health status. Considering that the rule-based strategy is more sensitive to operating conditions, this paper uses an artificial neural network for predictive control. The results are compared with those obtained from the genetic algorithm optimized fuzzy controller and are found to be very similar, where the prediction accuracy is assessed using MAPE, RMSE and 10-fold cross-validation. Experiments show that the developed strategy has a good generalization capability for variable driving cycles.

Chordomas are rare, aggressive tumors of the axial skeleton with limited treatment options. Genetic alterations in SMARCB1 , a tumor suppressor gene, have been implicated in poorly differentiated chordomas, but their role in typical and chondroid subtypes remains unclear. This study examined 42 chordoma samples (26 typical, 16 chondroid) for SMARCB1 genetic alterations, expression patterns, and associated pathways. SMARCB1 knockdown experiments were conducted in chordoma cell lines, followed by comprehensive transcriptome analysis. No exonic SMARCB1 mutations were identified, but heterozygous loss was observed in 3/26 typical chordomas. SMARCB1 expression positively correlated with patient survival and epithelial-mesenchymal markers. Functional studies revealed that SMARCB1 knockdown significantly enhanced cell proliferation, migration, and invasion. Transcriptome analysis demonstrated enrichment of MYC targets, E2F targets, and cell cycle pathways in SMARCB1 -low samples, while cellular adhesion pathways were downregulated. Notably, SLPI, LBH, and LOXL2 were significantly downregulated in SMARCB1 -low samples. SMARCB1 plays an important role in chordoma progression, influencing prognosis and cellular behavior, despite infrequent genetic alterations. Its effects on key oncogenic pathways and cellular plasticity suggest potential for targeted therapies. These findings provide new insights into chordoma biology and lay the groundwork for developing SMARCB1 -based prognostic tools and personalized treatment strategies.

Background: Mesalazine is the first-line inflammatory bowel disease (IBD) treatment. However, it can cause fatal cardiotoxicity. We aimed to analyze the clinical characteristics of mesalazine-induced cardiotoxicity and provide evidence for clinical diagnosis, treatment, and prevention. Methods: We collected Chinese and English literature on mesalazine-induced cardiotoxicity from 1970 to 2021 for retrospective analysis. Results: A total of 52 patients (40 males and 12 females) were included, with a median age of 24.5 years (range 9–62) and a median onset time of 14 days (range 2–2880). Cardiotoxicity manifested as myocarditis, pericarditis, and cardiac pericarditis. The main clinical manifestations are chest pain (82.7%), fever (46.2%), and respiratory symptoms such as dyspnea and cough (40.4%). The levels of troponin T, creatine kinase, C-reactive protein, leukocyte count, erythrocyte sedimentation rate, and other biochemical markers were significantly increased. Cardiac imaging often suggests myocardial infarction, pericardial effusion, myocardial necrosis, and other symptoms of cardiac injury. It is essential to discontinue mesalamine immediately in patients with cardiotoxicity. Although corticosteroids are a standard treatment option, the benefits remain to be determined. Re-challenge of mesalamine should be carefully considered as cardiotoxic symptoms may reoccur. Conclusion: Mesalazine may cause cardiotoxicity in patients with inflammatory bowel disease, which should be comprehensively diagnosed based on clinical manifestations, biochemical indicators, and cardiac function imaging examinations. Mesalazine should be immediately discontinued, and corticosteroids may be an effective treatment for cardiotoxicity.