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"Han, Lu"
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Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences.
•Variational auto-encoder implements 1 an unsupervised model of “Bayesian brain”.•Variational auto-encoder explains and predicts fMRI responses to natural videos.•Variational auto-encoder decodes fMRI responses to directly reconstruct visual input.•Convolutional neural networks trained for image classification better predict fMRI responses than variational auto-encoder trained for image reconstruction.
Journal Article
مختارات من المسرح الصيني الحديث
تكمن أهمية كتاب \"مختارات من المسرح الصيني الحديث\" (الصادر ضمن سلسلة \"آفاق عالمية\"، الهيئة المصرية العامة لقصور الثقافة في القاهرة)، في أنه يسلط الضوء على جانب مهم في الثقافة الصينية التي لا يعرف القارئ العربي عنها كثيرا، يضم الكتاب ثمانية نصوص مسرحية وهي المسافر، ليلة اصطياد النمر، زهرة التانج دي، اخفض سوطك، بعد الثمالة، الظلم، الأعود، موقف الباص، لستة كتاب صينيين، تولى فريق من المترجمات المصريات نقلها إلى العربية، تحت إشراف المترجم والأكاديمي محسن فرجاني، وقد لاحظ فرجاني أن أي محاولة لترجمة نص درامي صيني حديث، ستجد نفسها مطالبة، تقريبا، بالرجوع المستمر إلى الأوبرا الكلاسيكية، تاريخا وفنا وتقنيات وأدوات ووسائل فنية، وحضورا جماهيريا، وطاقات تواصل، ورموزا ودلالات، مثلما ستكون مصغية بتقدير بالغ إلى أهمية عملية التنوع الخصب في المواد الدرامية وطرق التعبير عنها، كما لاحظ فرجاني أن الحركة المسرحية الصينية الحديثة وصفت بغلبة عنصر الأيديولوجيا السياسية عليها لدرجة أنها أصبحت بديلا من الشخصية الفنية الأصيلة للدراما بوصفها شكلا تمثيليا ذا صبغة فنية جمالية بحتة وأن وظيفتها كوسيلة للإمتاع قد تراجعت وراء مهام التوجيه الأيديولوجي، وهو وصف يرى فرجاني أنه يبدو إلى حد ما صادق التقدير معقول النظر بيد أنه يغفل درجة التعقيد التي تصبغ العلاقة بين السياسة والفن المسرحي الصيني على صعيد تطوره ويتجاهل المنظور المناسب لتقدير خصوصية هذه العلاقة.
BOOK
Heavy metal exposure and its impact on inflammatory ratios in minors: The mediating role of BMI
Despite existing evidence that endocrine-disrupting chemicals like heavy metals exposure impairs health of minors, the association between the exposures and inflammatory ratios remains uncertain. This study aims to investigate the relationship between heavy metal exposure and inflammatory ratios, focusing on BMI as a potential mediator in this association.
We conducted a retrospective cross-sectional analysis from the NHANES 2007-2018. 14,007 minors were categorized into different age groups, and analyses were performed based on demographic characteristics. Multiple linear regression and mediation analysis were applied to asses associations between heavy metal concentrations and inflammatory ratios, with BMI included as a mediating variable.
The participants were divided into four age groups: toddlers (2487), preschool children (2297), school-age children (5019), and teenagers (4204). Blood Pb was positively correlated with LMR (β = 0.70, 95% CI: 0.60-0.81) and PNR (β = 14.88, 95% CI: 12.29-17.47), with 25.89% and 27.02% of these associations mediated by BMI. Negative correlations were observed between Pb and inflammation ratios, including NLR (β = -0.29, 95% CI: -0.34 - -0.24), PLR (β = -10.35, 95% CI: -12.61- -8.08), and NMR (β = -0.63, 95% CI: -0.78 - -0.48), with BMI accounting for 37.64%, 22.40%, and 39.59% of these effects, respectively. Blood Cd and Hg were also correlated with these ratios, with BMI consistently mediating these associations.
BMI serves as a significant mediator between blood heavy metals and inflammatory ratios among minors.
Journal Article
Association of Dietary Live Microbe Intake with Cardiovascular Disease in US Adults: A Cross-Sectional Study of NHANES 2007–2018
Objective: To detect the potential association between dietary live microbe and cardiovascular diseases (CVD). Methods: Data of 10,875 participants aged 18 years or older in this study were collected from the National Health and Nutrition Examination Survey (NHANES). Participants in this study were divided into three groups according to the Sanders dietary live microbe classification system: low, medium, and high dietary live microbe groups. CVD was defined by a combination of self-reported physician diagnoses and standardized medical status questionnaires. The analyses utilized weighted logistic regression models. Results: After the full adjustment for confounders, patients in the medium dietary live microbe group had a low prevalence of CVD in contrast to those in the low dietary live microbe group (OR: 0.78, 95% CI: 0.52–0.99, and p < 0.05), but no significant association with CVD was detected between the high and low dietary live microbe groups. Higher dietary live microbe groups were negatively associated with the prevalence of stroke (p for trend = 0.01) and heart attack (p for trend = 0.01). People who were male were more likely to suffer stroke due to low dietary live microbe (p for interaction = 0.03). Conclusion: A high dietary live microbe intake was associated with a low prevalence of CVD, and the significant association was detected when the analysis was limited to stroke and heart attack.
Journal Article
A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification
This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.
Journal Article
The diagnostic value of decreased levels of inflammatory markers for the state of hepatitis C virus (HCV) infection
Blood-cell-based inflammatory biomarkers are increasingly recognized for their diagnostic value in infections due to their clinical accessibility. With Hepatitis C virus (HCV) incidence rising and its often asymptomatic onset, this study aims to improve diagnostic evidence for HCV by analyzing changes in these biomarkers.
Utilizing NHANES database, we employed binary logistic regression and generalized additive models to explore the relationship between systemic inflammatory index and HCV infection. Three adjusted models controlled for confounders, and subgroup analyses were stratified by age, gender, race, and BMI.
Significant differences were observed in PLR (103.24 ± 44.59), SII (455.23 ± 339.56), PNR (58.22 ± 32.20), PMR (366.85 ± 191.76), and NMR (7.03 ± 3.78) between infected and uninfected groups (P < 0.05). Adjusted analyses revealed associations between anti-HCV and Log2-PLR (OR = 0.58), Log2-SII (OR = 0.64), Log2-PMR (OR = 0.77), and Log2-NMR (OR = 0.79). Individuals under 30 showed no significant differences. A unit increase below 9.30 in Log2-PMR reduced HCV risk by 0.60-fold. PMR demonstrated an AUC of 0.648, specificity 0.7632, and sensitivity 0.4709.
In individuals aged 30 and above, inflammatory markers PLR, SII, PMR, and NMR decrease in HCV cases. Variability across races, genders, and BMI groups highlights their diagnostic utility in diverse populations.
Journal Article
Mapping white-matter functional organization at rest and during naturalistic visual perception
Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans both in the resting state and while watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were de-noised and decomposed into spatially independent components, which were further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about the activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts.
•ICA applied to white-matter fMRI signals reveals reproducible and hierarchical patterns.•White-matter ICA components are mostly preserved, but are in part distinct between the resting state and the task state.•The distinction is specific to the axonal fibers involved in the task execution.•White-matter fMRI data are not noise or artifacts, but instead are signals of likely neuronal origin.
Journal Article
Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging
A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.
Journal Article
IL-22 initiates an IL-18-dependent epithelial response circuit to enforce intestinal host defence
IL-18 is emerging as an IL-22-induced and epithelium-derived cytokine which contributes to host defence against intestinal infection and inflammation. In contrast to its known role in Goblet cells, regulation of barrier function at the molecular level by IL-18 is much less explored. Here we show that IL-18 is a bona fide IL-22-regulated gate keeper for intestinal epithelial barrier. IL-22 promotes crypt immunity both via induction of phospho-Stat3 binding to the
Il-18
gene promoter and via
Il-18
independent mechanisms. In organoid culture, while IL-22 primarily increases organoid size and inhibits expression of stem cell genes, IL-18 preferentially promotes organoid budding and induces signature genes of Lgr5
+
stem cells via Akt-Tcf4 signalling. During adherent-invasive
E. coli
(AIEC) infection, systemic administration of IL-18 corrects compromised T-cell IFNγ production and restores Lysozyme
+
Paneth cells in
Il-22
−/−
mice, but IL-22 administration fails to restore these parameters in
Il-18
−/−
mice, thereby placing IL-22-Stat3 signalling upstream of the IL-18-mediated barrier defence function. IL-18 in return regulates Stat3-mediated anti-microbial response in Paneth cells, Akt-Tcf4-triggered expansion of Lgr5
+
stem cells to facilitate tissue repair, and AIEC clearance by promoting IFNγ
+
T cells.
IL-22 induces IL-18 expression by intestinal epithelial cells. Authors show here that IL-18 is a key barrier maintenance factor during adherent-invasive
E. coli
invasion, inducing expression of anti-microbial genes in Paneth cells via Stat3, prompting IFNγ expression in T cells and triggering intestinal Lgr5
+
stem cell expansion via Tcf4.
Journal Article