qPRF: A system to accelerate population receptive field decoding.

Patterns of BOLD response can be decoded using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). The time cost of evaluating the PRF model is high, often requiring days to decode BOLD signals for a small cohort of subjects. We introduce the qPRF, an efficient method for decoding that reduced the computation time by a factor of 1436 when compared to another widely available PRF decoder (Kay, Winawer, Mezer and Wandell, 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen, Smith, Barch, Behrens, Yacoub and Ugurbil, 2013). With a specially designed data structure and an efficient search algorithm, the qPRF optimizes the five PRF model parameters according to a least-squares criterion. To verify the accuracy of the qPRF solutions, we compared them to those provided by Benson, Jamison, Arcaro, Vu, Glasser, Coalson, Van Essen, Yacoub, Ugurbil, Winawer and Kay (2018). Both hemispheres of the 181 subjects in the HCP data set (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were decoded by qPRF in 15.2 hours on an ordinary CPU. The absolute difference in R 2 reported by Benson et al. and achieved by the qPRF was negligible, with a median of 0.39% ( R 2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R 2 on 99.7% of vertices. The qPRF may facilitate the development and computation of more elaborate models based on the PRF framework, as well as the exploration of novel clinical applications. Highlights: We describe a novel software system, qPRF, which can perform population receptive field (PRF) decoding of BOLD fMRI at speeds about 1400 times faster than the conventional systems designed for PRF decoding.We show that qPRF yields estimates of PRF model parameters that, in terms of goodness-of-fit, are equivalent to estimates derived using the conventional systems.An efficient similarity-based search strategy, underlies the accelerated computations of qPRF, supported by a specially designed data structure wherein tens of millions of pre-computed prediction curves are stored.

Integrated Piezoelectric/Pyroelectric Sensing from Organic-Inorganic Perovskite Nanocomposites.

Flexible ferroelectric materials are in high demand in emerging energy harvesting and self-powered sensing electronics. However, current flexible ferroelectric polymers, such as poly(vinylidene fluoride) (PVDF) and P(VDF-co-trifluoroethylene) [P(VDF-TrFE)], cannot fulfill the requirement of emerging applications because of their low piezoelectric/pyroelectric performance. In this work, using organic-inorganic hybrid perovskite [(4-aminotetrahydropyran)2PbBr2Cl2] ferroelectric nanorods as reinforcement and P(VDF-TrFE) as the matrix, we prepared flexible core-sheath piezoelectric nanofibers and pyroelectric nanocomposite films. The core-sheath nanofibers possess a record-high piezoelectric coefficient of 78.1 pC·N-1, and the output voltage reaches to 192 V, with the maximum power density of 1.04 W·m-2. On the other hand, the nanocomposite film exhibits a high pyroelectric coefficient of 58.2 µC·m-2·K-1 at 333 K, which yields a voltage of 6.1 V under 6.6 K temperature fluctuation. An integrated flexible sensing device was prepared by combining piezoelectric nanofibers and pyroelectric films, which can wirelessly detect vibration and temperature fluctuation simultaneously. The integrated device is suitable for pipelines, power equipment, and other scenarios, where vibration and temperature need to be monitored at the same time.

Bacteria-Like Gaiella Accelerate Soil Carbon Loss by Decomposing Organic Matter of Grazing Soils in Alpine Meadows on the Qinghai-Tibet Plateau.

The alpine meadows of the Qinghai-Tibet Plateau have significant potential for storing soil carbon, which is important to global carbon sequestration. Grazing is a major threat to its potential for carbon sequestration. However, grazing poses a major threat to this potential by speeding up the breakdown of organic matter in the soil and releasing carbon, which may further lead to positive carbon-climate change feedback and threaten ecological security. Therefore, in order to accurately explore the driving mechanism and regulatory factors of soil organic matter decomposition in grazing alpine meadows on the Qinghai-Tibet Plateau, we took the grazing sample plots of typical alpine meadows as the research object and set up grazing intensities of different life cycles, aiming to explore the relationship and main regulatory factors of grazing on soil organic matter decomposition and soil microorganisms. The results show the following: (1) soil microorganisms, especially Acidobacteria and Acidobacteria, drove the decomposition of organic matter in the soil, thereby accelerating the release of soil carbon, which was not conducive to soil carbon sequestration in grassland; (2) the grazing triggering effect formed a positive feedback with soil microbial carbon release, accelerating the decomposition of organic matter and soil carbon loss; and (3) the grazing ban and light grazing were more conducive to slowing down soil organic matter decomposition and increasing soil carbon sequestration.

Effects of HOX family regulator-mediated modification patterns and immunity characteristics on tumor-associated cell type in endometrial cancer.

Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

Alternating electric fields transform the intricate network of tumour vasculature into orderly parallel capillaries and enhance the anti-angiogenesis effect of bevacizumab.

The search for effective strategies to target tumour angiogenesis remains a critical goal of cancer research. We present a pioneering approach using alternating electric fields to inhibit tumour angiogenesis and enhance the therapeutic efficacy of bevacizumab. Chicken chorioallantoic membrane, cell viability and in vitro endothelial tube formation assays revealed that electric fields with a frequency of 1000 kHz and an electric intensity of 0.6 V/cm inhibited the growth of vascular endothelial cells and suppressed tumour-induced angiogenesis. In an animal U87MG glioma model, 1000 kHz electric fields inhibited tumour angiogenesis and suppressed tumour growth. As demonstrated by 3D vessel analysis, tumour vasculature in the control group was a stout, interwoven network. However, electric fields transformed it into slim, parallel capillaries that were strictly perpendicular to the electric field direction. This architectural transformation was accompanied by apoptosis of vascular endothelial cells and a notable reduction in tumour vessel number. Additionally, we found that the anti-angiogenesis and tumour-suppression effects of electric fields synergised with bevacizumab. The anti-angiogenic mechanisms of electric fields include disrupting spindle formation during endothelial cell division and downregulating environmental angiogenesis-related cytokines, such as interleukin-6, CXCL-1, 2, 3, 5 and 8, and matrix metalloproteinases. In summary, our findings demonstrate the potential of alternating electric fields (AEFs) as a therapeutic modality to impede angiogenesis and restrain cancer growth.

Analysis of the Therapeutic Efficacy of Hysteroscopic Electric Resection in the Treatment of Atypical Endometrial Hyperplasia and Factors Influencing Prognosis.

Objective: The objective of this study was to evaluate the efficacy of hysteroscopic electroresection in the treatment of atypical endometrial hyperplasia and to determine the prognostic factors. Methods: 226 patients with endometrial dysplasia treated in hospital from January 2021 to August 2022 were selected and divided into control group (113 cases) and study group (113 cases) according to different treatment methods selected by the patients themselves. The control group received curettage plus conventional progesterone treatment, while the study group received hysteroscopic electroresection plus conventional progesterone treatment. After 6 months of treatment, the clinical efficacy (complete response, partial response and progress) of the two groups were evaluated, complications and adverse drug reactions of the two groups were analyzed, and estrogen levels before and after treatment were compared between the two groups. After 1 year follow-up, patients were divided into relapse group and non-recurrence group according to whether they had relapse or not. Clinical data of the two groups were compared to analyze the related factors affecting the prognosis of patients. Results: (1) Chi-square test results showed that the total effective rate of the study group was higher (96.46% VS 77.88%) than that of the control group (P < .05). The complication rate and recurrence rate of the study group were lower than those of the control group (1.77% VS 7.96%, 4.42% VS 21.24%) (P < .05). (2) t test results of independent samples showed that after 6 months of treatment, the levels of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in the study group were lower than those in the control group (P < .05); (3) The t test results of independent samples indicated that the age and body mass index of the relapsed group were higher than those of the non-relapsed group (P < .05); Chi-square test results showed that the proportion of diabetes was higher than that of the group without recurrence, and the proportion of hysteroscopic electroresection was lower than that of the group without recurrence (P < .05). (4) Logistic regression model was established, and the results showed that age (OR=1.159), body mass index (OR=1.529) and diabetes (OR=3.861) were the risk factors for prognosis of patients with endometrial dysplasia (P < .05), and hysteroscopic electroresection was the protective factor (OR < 1, P < .05). Conclusion: Hysteroscopic electroresection shows significant potential in the treatment of atypical hyperplasia of endometrial, and can improve clinical efficacy and reduce complications by effectively regulating estrogen secretion. In addition, studies have shown that the prognosis of endometrial dysplasia may be related to the age of patients, body mass index and diabetes mellitus. Therefore, for patients with the above risk factors, early consideration of hysteroscopic electrotomy therapy is recommended to reduce recurrence rates and provide important informational support for treatment protocols and clinical guidelines.

Causal Brain Network Predicts Surgical Outcomes in Patients With Drug-Resistant Epilepsy: A Retrospective Comparative Study.

Network neuroscience, especially causal brain network, has facilitated drug-resistant epilepsy (DRE) studies, while surgical success rate in patients with DRE is still limited, varying from 30%  âˆ¼  70 %. Predicting surgical outcomes can provide additional guidance to adjust treatment plans in time for poorly predicted curative effects. In this retrospective study, we aim to systematically explore biomarkers for surgical outcomes by causal brain network methods and multicenter datasets. Electrocorticogram (ECoG) recordings from 17 DRE patients with 58 seizures were included. Ictal ECoG within clinically annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgeries. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α -frequency band ( 8  âˆ¼  13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen's d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with [Formula: see text]. Based on the brain network features, machine learning models were developed to predict the surgical outcomes. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest average accuracy of 84.48% by 5-fold cross-validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE surgical outcomes.

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement.

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,∗) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets.

Causal Brain Network in Clinically-Annotated Epileptogenic Zone Predicts Surgical Outcomes of Drug-Resistant Epilepsy.

OBJECTIVE: Patients with drug-resistant epilepsy (DRE) are commonly treated using neurosurgery, while its success rate is limited with approximately 50%. Predicting surgical outcomes is currently a prominent topic. The DRE is recognized as a network disorder involving a seizure triggering mechanism within epileptogenic zone (EZ); however, a systematic exploration of the EZ causal network remains lacking. METHODS: This paper will advance DRE study by: (1) developing a novel causal coupling algorithm, "full convergent cross mapping (FCCM)" to improve the quantization performance; (2) characterizing the DRE's multi-frequency epileptogenic network by FCCM calculation of ictal iEEG; (3) predicting surgical outcomes using network features and machine learning. Numerical validations demonstrate the FCCM's superior quantization in terms of nonlinearity, accuracy, and stability. A multicenter cohort containing 22 DRE patients with 81 seizures is included. RESULT: Based on the Mann-Whitney-U-test, coupling strength of the epileptogenic network in successful surgeries is significantly higher than that of the failed group, with the most significant difference observed in α -iEEG network (p = 1.52e - 07 ) Other clinical covariates are also considered and all th α -iEEG networks demonstrate consistent differences comparing successful and failed groups, with p = 0.014 and 9.23e - 06 for lesional and non-lesional DRE, p = 2.32e - 05, 0.0074 and 0.0030 for three clinical centers CHFU, JHU and NIH. Using FCCM features and 10-fold cross validation, the SVM achieves the highest accuracy of 87.65% in predicting surgical outcomes. CONCLUSION: The epileptogenic causal network is a reliable biomarker for estimating DRE's surgical outcomes. SIGNIFICANCE: The proposed approach is promising to facilitate DRE precision medicine.

Corrigendum: How can the collaborative participation of regulators, whistleblowers, and parties effectively promote rumor management in public health emergencies?

[This corrects the article DOI: 10.3389/fpubh.2023.1290841.].

Surface-based morphometry of the corpus callosum in young children of ages 1-5.

The corpus callosum (CC) is a large white matter fiber bundle in the brain and is involved in various cognitive, sensory, and motor processes. While implicated in various developmental and psychiatric disorders, much is yet to be uncovered about the normal development of this structure, especially in young children. Additionally, while sexual dimorphism has been reported in prior literature, observations have not necessarily been consistent. In this study, we use morphometric measures including surface tensor-based morphometry (TBM) to investigate local changes in the shape of the CC in children between the ages of 12 and 60 months, in intervals of 12 months. We also analyze sex differences in each of these age groups. We observed larger significant clusters in the earlier ages between 12 v 24 m and between 48 v 60 m and localized differences in the anterior region of the body of the CC. Sex differences were most pronounced in the 12 m group. This study adds to the growing literature of work aiming to understand the developing brain and emphasizes the utility of surface TBM as a useful tool for analyzing regional differences in neuroanatomical morphometry.

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.

Phosphorylation of ZmAL14 by ZmSnRK2.2 regulates drought resistance through derepressing ZmROP8 expression.

Drought stress has negative effects on crop growth and production. Characterization of transcription factors that regulate the expression of drought-responsive genes is critical for understanding the transcriptional regulatory networks in response to drought, which facilitates the improvement of crop drought tolerance. Here, we identified an Alfin-like (AL) family gene ZmAL14 that negatively regulates drought resistance. Overexpression of ZmAL14 exhibits susceptibility to drought while mutation of ZmAL14 enhances drought resistance. An abscisic acid (ABA)-activated protein kinase ZmSnRK2.2 interacts and phosphorylates ZmAL14 at T38 residue. Knockout of ZmSnRK2.2 gene decreases drought resistance of maize. A dehydration-induced Rho-like small guanosine triphosphatase gene ZmROP8 is directly targeted and repressed by ZmAL14. Phosphorylation of ZmAL14 by ZmSnRK2.2 prevents its binding to the ZmROP8 promoter, thereby releasing the repression of ZmROP8 transcription. Overexpression of ZmROP8 stimulates peroxidase activity and reduces hydrogen peroxide accumulation after drought treatment. Collectively, our study indicates that ZmAL14 is a negative regulator of drought resistance, which can be phosphorylated by ZmSnRK2.2 through the ABA signaling pathway, thus preventing its suppression on ZmROP8 transcription during drought stress response.

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.

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.

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.

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.