Probiotic-derived amphiphilic exopolysaccharide self-assembling adjuvant delivery platform for enhancing immune responses.

Enhancing immune response activation through the synergy of effective antigen delivery and immune enhancement using natural, biodegradable materials with immune-adjuvant capabilities is challenging. Here, we present NAPSL.p that can activate the Toll-like receptor 4 (TLR4) pathway, an amphiphilic exopolysaccharide, as a potential self-assembly adjuvant delivery platform. Its molecular structure and unique properties exhibited remarkable self-assembly, forming a homogeneous nanovaccine with ovalbumin (OVA) as the model antigen. When used as an adjuvant, NAPSL.p significantly increased OVA uptake by dendritic cells. In vivo imaging revealed prolonged pharmacokinetics of NAPSL. p-delivered OVA compared to OVA alone. Notably, NAPSL.p induced elevated levels of specific serum IgG and isotype titers, enhancing rejection of B16-OVA melanoma xenografts in vaccinated mice. Additionally, NAPSL.p formulation improved therapeutic effects, inhibiting tumor growth, and increasing animal survival rates. The nanovaccine elicited CD4+ and CD8+ T cell-based immune responses, demonstrating the potential for melanoma prevention. Furthermore, NAPSL.p-based vaccination showed stronger protective effects against influenza compared to Al (OH)3 adjuvant. Our findings suggest NAPSL.p as a promising, natural self-adjuvanting delivery platform to enhance vaccine design across applications.

Revealing neural dynamical structure of C. elegans with deep learning.

Caenorhabditis elegans serves as a common model for investigating neural dynamics and functions of biological neural networks. Data-driven approaches have been employed in reconstructing neural dynamics. However, challenges remain regarding the curse of high-dimensionality and stochasticity in realistic systems. In this study, we develop a deep neural network (DNN) approach to reconstruct the neural dynamics of C. elegans and study neural mechanisms for locomotion. Our model identifies two limit cycles in the neural activity space: one underpins basic pirouette behavior, essential for navigation, and the other introduces extra Ω turns. The combination of two limit cycles elucidates predominant locomotion patterns in neural imaging data. The corresponding energy landscape explains the switching strategies between two limit cycles, quantitatively, and provides testable predictions on neural functions and circuit roles. Our work provides a general approach to study neural dynamics by combining imaging data and stochastic modeling.

Data-driven energy landscape reveals critical genes in cancer progression.

The evolution of cancer is a complex process characterized by stable states and transitions among them. Studying the dynamic evolution of cancer and revealing the mechanisms of cancer progression based on experimental data is an important topic. In this study, we aim to employ a data-driven energy landscape approach to analyze the dynamic evolution of cancer. We take Kidney renal clear cell carcinoma (KIRC) as an example. From the energy landscape, we introduce two quantitative indicators (transition probability and barrier height) to study critical shifts in KIRC cancer evolution, including cancer onset and progression, and identify critical genes involved in these transitions. Our results successfully identify crucial genes that either promote or inhibit these transition processes in KIRC. We also conduct a comprehensive biological function analysis on these genes, validating the accuracy and reliability of our predictions. This work has implications for discovering new biomarkers, drug targets, and cancer treatment strategies in KIRC.

Gender differences in the incidence of chronic thromboembolic pulmonary hypertension after acute pulmonary embolism: A meta-analysis.

BACKGROUND: To explore the role of gender in the incidence of chronic thromboembolic pulmonary hypertension after acute pulmonary embolism. METHODS: Two researchers search the PubMed Database, Embase Database and Cochrane Library Database from their establishment to October 2022, using Endnote software for document management and RevMan5.3 software for the meta-analysis of the included literature. A total of 11 studies are selected, including 5788 acute pulmonary embolism events and 391 patients (179 males and 212 females) with chronic thromboembolic pulmonary hypertension (CTEPH) under the stated conditions. The results show that there is no statistically significant difference in the incidence of CTEPH between males and females after PE (P = .28), with combined OR of 0.89 and 95% CI 0.72-1.10. RESULTS AND CONCLUSIONS: Gender is found to be absent as a factor in the incidence of CTEPH after acute pulmonary embolism. This may indicate that gender is not a risk factor for CTEPH and that female patients are not necessarily more likely to have a higher incidence than male patients. As such, accurate judgments should be made on the possible complications of all patients after acute pulmonary embolism, which will be conducive to early detection and intervention in the treatment of CTEPH.

Landscape quantifies the intermediate state and transition dynamics in ecological networks.

Understanding the ecological mechanisms associated with the collapse and restoration is especially critical in promoting harmonious coexistence between humans and nature. So far, it remains challenging to elucidate the mechanisms of stochastic dynamical transitions for ecological systems. Using an example of plant-pollinator network, we quantified the energy landscape of ecological system. The landscape displays multiple attractors characterizing the high, low and intermediate abundance stable states. Interestingly, we detected the intermediate states under pollinator decline, and demonstrated the indispensable role of the intermediate state in state transitions. From the landscape, we define the barrier height (BH) as a global quantity to evaluate the transition feasibility. We propose that the BH can serve as a new early-warning signal (EWS) for upcoming catastrophic breakdown, which provides an earlier and more accurate warning signal than traditional metrics based on time series. Our results promote developing better management strategies to achieve environmental sustainability.

Early-initiated childhood reading for pleasure: associations with better cognitive performance, mental well-being and brain structure in young adolescence.

BACKGROUND: Childhood is a crucial neurodevelopmental period. We investigated whether childhood reading for pleasure (RfP) was related to young adolescent assessments of cognition, mental health, and brain structure. METHODS: We conducted a cross-sectional and longitudinal study in a large-scale US national cohort (10 000 + young adolescents), using the well-established linear mixed model and structural equation methods for twin study, longitudinal and mediation analyses. A 2-sample Mendelian randomization (MR) analysis for potential causal inference was also performed. Important factors including socio-economic status were controlled. RESULTS: Early-initiated long-standing childhood RfP (early RfP) was highly positively correlated with performance on cognitive tests and significantly negatively correlated with mental health problem scores of young adolescents. These participants with higher early RfP scores exhibited moderately larger total brain cortical areas and volumes, with increased regions including the temporal, frontal, insula, supramarginal; left angular, para-hippocampal; right middle-occipital, anterior-cingulate, orbital areas; and subcortical ventral-diencephalon and thalamus. These brain structures were significantly related to their cognitive and mental health scores, and displayed significant mediation effects. Early RfP was longitudinally associated with higher crystallized cognition and lower attention symptoms at follow-up. Approximately 12 h/week of youth regular RfP was cognitively optimal. We further observed a moderately significant heritability of early RfP, with considerable contribution from environments. MR analysis revealed beneficial causal associations of early RfP with adult cognitive performance and left superior temporal structure. CONCLUSIONS: These findings, for the first time, revealed the important relationships of early RfP with subsequent brain and cognitive development and mental well-being.

Multistep optimization for the electrodeposited mixed perovskite FA1-yCsyPbBrxI3-xsolar cells.

The electrodeposition method has recently been developed for the fabrication of perovskite solar cells due to its potential advantages in commercial preparation. However, there is few studies on the preparation of perovskite solar cells by the electrodeposition method, especially on the perovskite FAPbI3-based solar cells. Herein, we fabricated the mixed perovskite FA1-yCsyPbBrxI3-xsolar cells by an optimized electrodeposition method, in which the electrodeposited PbO2reacts directly with FAI and an appropriate amount of CsBr dopants. The corresponding solar cells display the best PCE of 4.97%. By regulating the growth temperature in the reaction between PbO2and FAI/CsBr, the efficiency of the mixed perovskite solar cells can be promoted to 10.18%. These results illustrate that the element doping and growth environment regulation can optimize the quality of the perovskite films, thus promoting the efficiency of the perovskite solar cells. With further optimizing the growth process in the electrodeposition method, it is expected to open up a new commercial preparation route for the perovskite solar cells in the near future.

Controlling brain dynamics: Landscape and transition path for working memory.

Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.

Configured quantum reservoir computing for multi-task machine learning.

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence.

Xuebijing Injection Attenuates Heat Stroke-Induced Brain Injury through Oxidative Stress Blockage and Parthanatos Modulation via PARP-1/AIF Signaling.

Heat stroke (HS) is a potentially fatal acute condition caused by an interplay of complex events including inflammation, endothelial injury, and coagulation abnormalities that make its pharmacological treatment a challenging problem. The traditional Chinese medicine Xuebijing injection (XBJ) has been shown to reduce inflammatory responses and prevent organ injuries in HS-induced mice. However, the underlying mechanism of XBJ in HS-induced brain injury remains unclear. In this study, HS-induced rat models and cell models were established to elucidate the effects and underlying mechanisms of XBJ injection on HS-induced brain injury in vivo and in vitro. The results revealed that XBJ injection improved the survival outcome of HS rats and attenuated HS-induced brain injury in a concentration-dependent manner. Subsequently, the reduction in viability and proliferation of neurons induced by HS were reversed by XBJ treatment, while the HS-induced increased ROS levels and neuron death were also inhibited by XBJ injection. Mechanistically, HS activated PARP-1/AIF signaling in vitro and in vivo, inducing the translocation of AIF from the cytoplasm to the nucleus, leading to PARP-1-dependent cell death of neurons. Additionally, we compared XBJ injection effects in young and old age rats. Results showed that XBJ also provided protective effects in HS-induced brain injury in aging rats; however, the treatment efficacy of XBJ injection at the same concentration was more significant in the young age rats. In conclusion, XBJ injection attenuates HS-induced brain injury by inhibiting oxidative stress and Parthanatos via the PARP-1/AIF signaling, which might provide a novel therapeutic strategy for HS treatment.

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network.

Waddington's epigenetic landscape is a framework depicting the processes of cell differentiation and reprogramming under the control of a gene regulatory network (GRN). Traditional model-driven methods for landscape quantification focus on the Boolean network or differential equation-based models of GRN, which need sophisticated prior knowledge and hence hamper their practical applications. To resolve this problem, we combine data-driven methods for inferring GRNs from gene expression data with model-driven approach to the landscape mapping. Specifically, we build an end-to-end pipeline to link data-driven and model-driven methods and develop a software tool named TMELand for GRN inference, visualizing Waddington's epigenetic landscape, and calculating state transition paths between attractors to uncover the intrinsic mechanism of cellular transition dynamics. By integrating GRN inference from real transcriptomic data with landscape modeling, TMELand can facilitate studies of computational systems biology, such as predicting cellular states and visualizing the dynamical trends of cell fate determination and transition dynamics from single-cell transcriptomic data. The source code of TMELand, a user manual, and model files of case studies can be downloaded freely from https://github.com/JieZheng-ShanghaiTech/TMELand.

Structure and function in artificial, zebrafish and human neural networks.

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

Estimation of non-equilibrium transition rate from gene expression data.

The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.

An improved approach for calculating energy landscape of gene networks from moment equations.

The energy landscape theory has widely been applied to study the stochastic dynamics of biological systems. Different methods have been developed to quantify the energy landscape for gene networks, e.g., using Gaussian approximation (GA) approach to calculate the landscape by solving the diffusion equation approximately from the first two moments. However, how high-order moments influence the landscape construction remains to be elucidated. Also, multistability exists extensively in biological networks. So, how to quantify the landscape for a multistable dynamical system accurately, is a paramount problem. In this work, we prove that the weighted summation from GA (WSGA), provides an effective way to calculate the landscape for multistable systems and limit cycle systems. Meanwhile, we proposed an extended Gaussian approximation (EGA) approach by considering the effects of the third moments, which provides a more accurate way to obtain probability distribution and corresponding landscape. By applying our generalized EGA approach to two specific biological systems: multistable genetic circuit and synthetic oscillatory network, we compared EGA with WSGA by calculating the KL divergence of the probability distribution between these two approaches and simulations, which demonstrated that the EGA provides a more accurate approach to calculate the energy landscape.

Constructing a Surface Multi-cationic Heterojunction for CsPbI1.5Br1.5 Perovskite Solar Cells with Efficiency beyond 14.

All-inorganic CsPbI1.5Br1.5 perovskite solar cells are considered as top cell candidates for tandem cells as a result of their excellent thermal stability and photoelectric performance. However, their power conversion efficiencies (PCEs) are still low and far below the theoretical limit mainly as a result of the severe non-radiative recombination and optical loss. Herein, we introduce an versatile method to construct a surface multi-cationic heterojunction to achieve an efficient and stable CsPbI1.5Br1.5 perovskite solar cell. By precisely controlling the content of FA+ and MA+ on PbBr2-rich perovskite films, a high-quality heterojunction layer is formed to help effectively passivate the surface defects and reduce the optical loss of the CsPbI1.5Br1.5 perovskite. In addition, the incorporation of a heterojunction layer can also improve energy-level alignment and reduce interfacial charge recombination loss. As a result, the champion device with the incorporation of SMH exhibits a PCE of 14.11%, which presents the highest reported efficiency for inorganic CsPbI1.5Br1.5 solar cells thus far while retaining 85% of the initial efficiency after 1000 h of storage without encapsulation.

Relationship between obstructive sleep apnea-hypopnea syndrome and osteoporosis adults: A systematic review and meta-analysis.

Objective: This study is undertaken to explore the relationship between obstructive sleep apnea-hypopnea syndrome (OSAHS) and osteoporosis, including the relationship between OSAHS and osteoporosis incidence, lumbar spine bone mineral density (BMD), and lumbar spine T-score. Method: Cochrane Library, PubMed, Embase, Web of Science, and other databases are searched from their establishment to April 2022. Literature published in 4 databases on the correlation between OSAHS and osteoporosis,lumbar spine BMD,lumbar spine T-score is collected. Review Manager 5.4 software is used for meta-analysis. Results: A total of 15 articles are selected, including 113082 subjects. Compared with the control group, the OSAHS group has a higher incidence of osteoporosis (OR = 2.03, 95% CI: 1.26~3.27, Z = 2.90, P = 0.004), the lumbar spine BMD is significantly lower (MD = -0.05, 95% CI: -0.08~-0.02, Z = 3.07, P = 0.002), and the lumbar spine T-score is significantly decreased (MD = -0.47, 95% CI: -0.79~-0.14, Z = 2.83, P = 0. 005). Conclusion: Compared with the control group, the OSAHS group has a higher incidence of osteoporosis and decreased lumbar spine BMD and T-score. In order to reduce the risk of osteoporosis, attention should be paid to the treatment and management of adult OSAHS, and active sleep intervention should be carried out.

Association of Obstructive Sleep Apnea-Hypopnea Syndrome with hearing loss: A systematic review and meta-analysis.

Objective: This study seeks to investigate the relationship between Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) and hearing impairment by meta-analysis. Methods: Cochrane Library, PubMed, Embase, Web of Science and other databases are searched from their establishment to July 1st, 2022. Literature on the relationship between OSAHS and hearing loss is collected, and two researchers independently perform screening, data extraction and quality evaluation on the included literature. Meta-analysis is performed using RevMan 5.4.1 software. According to the heterogeneity between studies, a random-effects model or fixed-effects model is used for meta-analysis. Results: A total of 10 articles are included, with 7,867 subjects, 1,832 in the OSAHS group and 6,035 in the control group. The meta-analysis shows that the incidence of hearing impairment in the OSAHS group is higher than in the control group (OR = 1.38; 95% CI 1.18-1.62, Z = 4.09, P < 0.001), and the average hearing threshold of OSAHS patients is higher than that of the control group (MD = 5.89; 95% CI 1.87-9.91, Z = 2.87, P = 0.004). After stratifying the included studies according to hearing frequency, the meta-analysis shows that the OSAHS group has a higher threshold of 0.25, and the response amplitudes at frequencies 2, 4, 6, and 8 kHz are all higher than those of the control group. Conclusion: Compared with the control group, the OSAHS group has a higher incidence of hearing loss, mainly high-frequency hearing loss. Thus, OSAHS is closely associated with and a risk factor for hearing loss.

Validation of GOAL questionnaire as screening tool for clinical obstructive sleep apnea: A large sample study in China.

Background: Obstructive sleep apnea (OSA) is a serious disease with a high prevalence in the general population. The purpose of this study is to explore the effectiveness of the GOAL questionnaire in the clinical screening of OSA and compare it with other existing screening tools. Materials and methods: Outpatients and inpatients who underwent polysomnography (PSG) examination at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from January 2013 to November 2016 were analyzed retrospectively. The basic data such as demographic, medical history, etc., and PSG data of the patients were collected, and the sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC) of GOAL and five other screening scales (the NoSAS score, Epworth Sleepiness Scale, the Berlin questionnaire, STOP, and STOP-Bang questionnaire) were calculated. Results: Data from 2,171 participants (1,644 male; 78%) were analyzed there were 1,507 OSA patients [Apnea Hypopnea Index (AHI) ≥ 5 events/h] among them, accounting for about 69.415%. No matter which cut-off point (AHI ≥ 5, 15 and 30 events/h), the AUC score reveals that GOAL questionnaire had comparable screening ability to the NoSAS and STOP-BANG, and performed better than the ESS, and the AUC scores of the STOP questionnaire and Epworth Sleepiness Scale (ESS) were both lower than 0.7. When the cut-off point of the AHI was 5 events/h, the AUC of GOAL was the highest at 0.799 (0.781-0.816), and its sensitivity was the highest at 89.1%. The sensitivity levels of the NoSAS score and STOP-Bang questionnaire were 67.4 and 78.8% respectively, while ESS and the Berlin questionnaire have higher specificity (70.2 and 72.3% respectively) but lower sensitivity (49.3 and 60.0% respectively). Conclusion: GOAL is a free, efficient and easy to manage tool with a screening ability comparable to NoSAS and STOP-Bang, and better than that of ESS.

Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma.

Drug resistance and tumor relapse in patients with melanoma is attributed to a combination of genetic and non-genetic mechanisms. Dedifferentiation, a common mechanism of non-genetic resistance in melanoma is characterized by the loss of melanocytic markers. While various molecular attributes of de-differentiation have been identified, the transition dynamics remain poorly understood. Here, we construct cell-state transition landscapes, to quantify the stochastic dynamics driving phenotypic switching in melanoma based on its underlying regulatory network. These landscapes reveal the existence of multiple alternative paths to resistance-de-differentiation and transition to a hyper-pigmented phenotype. Finally, by visualizing the changes in the landscape during in silico molecular perturbations, we identify combinatorial strategies that can lead to the most optimal outcome-a landscape with the minimum occupancy of the two drug-resistant states. Therefore, we present these landscapes as platforms to screen possible therapeutic interventions in terms of their ability to lead to the most favorable patient outcomes.

Application value of joint STOP-Bang questionnaire and Epworth Sleepiness Scale in screening for obstructive sleep apnea.

Objective: This paper evaluates the application value of the STOP-Bang questionnaire combined with the Epworth Sleepiness Scale (ESS) in screening for obstructive sleep apnea (OSA) in the population. Method: Thousand-six hundred seventy-one patients with suspected OSA who visited the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from August 2017 to August 2020 were monitored by overnight polysomnography (PSG) after completing the ESS scale and STOP-Bang questionnaire. The sensitivity, specificity, positive predictive value, negative predictive value and receiver operating characteristic (ROC) curves of the two scales were calculated, and the accuracy in predicting OSA of the STOP-Bang questionnaire combined with ESS was analyzed. Results: With Apnea Hypopnea Index (AHI) cutoffs of ≥5, ≥15 and ≥30 events/h, the areas under the ROC curve scored by STOP-Bang were 0.724, 0.703 and 0.712, and those of ESS were 0.632, 0.634 and 0.695; the diagnostic odds ratio (DOR) values of STOP-Bang for OSA, moderate to severe OSA, and severe OSA were 3.349, 2.651 and 3.189, and those of ESS were 2.665, 2.279 and 3.289. The STOP-Bang score of three was used as the cut-off point for OSA diagnosis with higher sensitivity and lower specificity, while ESS had higher specificity. STOP-Bang (≥3) combined with ESS significantly improved its specificity for predicting OSA. Conclusion: The STOP-Bang questionnaire is a simple and effective new tool for screening patients for OSA, while a STOP-Bang score of ≥3 combined with ESS can further improve its specificity. Thus, we suggest further screening with ESS after a STOP-Bang score of ≥3 in suspected patients.