Infinite-dimensional reservoir computing.

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state-space systems. It is shown that this class is very rich and possesses useful features and universal approximation properties. The reservoir architectures used for the approximation and estimation of elements in the new class are randomly generated echo state networks with either linear or ReLU activation functions. Their readouts are built using randomly generated neural networks in which only the output layer is trained (extreme learning machines or random feature neural networks). The results in the paper yield a recurrent neural network-based learning algorithm with provable convergence guarantees that do not suffer from the curse of dimensionality when learning input/output systems in the class of generalized Barron functionals and measuring the error in a mean-squared sense.

Metabolomics analysis of rice fermented by medicinal fungi providing insights into the preparation of functional food.

Rice, a primary staple food, may be improved in value via fermentation. Here, ten medicinal basidiomycetous fungi were separately applied for rice fermentation. After preliminary screening, Ganoderma boninense, Phylloporia pulla, Sanghuangporus sanghuang and Sanghuangporus weigelae were selected for further LC-MS based determination of the changes in metabolic profile after their fermentation with rice, and a total of 261, 296, 312, and 355 differential compounds were identified, respectively. Most of these compounds were up-regulated and involved in the metabolic pathways of amino acid metabolism, lipid metabolism, carbohydrate metabolism and the biosynthesis of other secondary metabolites. Sanghuangporus weigelae endowed the rice with the highest nutritional and bioactive values. The metabolic network of the identified differential compounds in rice fermented by S. weigelae illustrated their close relationships. In summary, this study provides insights into the preparation and application of potential functional food via the fermentation of rice with medicinal fungi.

Temporal shifts in the phytoplankton network in a large eutrophic shallow freshwater lake subjected to major environmental changes due to human interventions.

Phytoplankton communities are crucial components of aquatic ecosystems, and since they are highly interactive, they always form complex networks. Yet, our understanding of how interactive phytoplankton networks vary through time under changing environmental conditions is limited. Using a 29-year (339 months) long-term dataset on Lake Taihu, China, we constructed a temporal network comprising monthly sub-networks using "extended Local Similarity Analysis" and assessed how eutrophication, climate change, and restoration efforts influenced the temporal dynamics of network complexity and stability. The network architecture of phytoplankton showed strong dynamic changes with varying environments. Our results revealed cascading effects of eutrophication and climate change on phytoplankton network stability via changes in network complexity. The network stability of phytoplankton increased with average degree, modularity, and nestedness and decreased with connectance. Eutrophication (increasing nitrogen) stabilized the phytoplankton network, mainly by increasing its average degree, while climate change, i.e., warming and decreasing wind speed enhanced its stability by increasing the cohesion of phytoplankton communities directly and by decreasing the connectance of network indirectly. A remarkable shift and a major decrease in the temporal dynamics of phytoplankton network complexity (average degree, nestedness) and stability (robustness, persistence) were detected after 2007 when numerous eutrophication mitigation efforts (not all successful) were implemented, leading to simplified phytoplankton networks and reduced stability. Our findings provide new insights into the organization of phytoplankton networks under eutrophication (or re-oligotrophication) and climate change in subtropical shallow lakes.

Does climate policy uncertainty drive the extreme spillovers of carbon-energy-shipping markets?

In addressing the ramifications of climate change, the shipping industry, reliant on energy, has been integrated into the Emissions Trading System (ETS). This study utilizes the quantile connectedness model to investigate the information spillover mechanisms and extreme time-varying interconnections among carbon, energy, and shipping markets. Whether climate policy uncertainty drives the extreme interconnections is also discussed during both pre- and post-Paris Agreement periods, by using GARCH-MIDAS model. The empirical findings underscore the following key points: (i) the systemic connectedness is highly sensitive to market conditions and major events, increasing significantly under extreme market conditions; (ii) following the implementation of the Paris Agreement, an elevated level of informational interdependence has manifested between the carbon market and the energy and shipping sectors; (iii) the information transfer mechanism between carbon and shipping sectors creates direct and indirect spillover paths, with crude oil market mediating the indirect path; (iv) climate policy uncertainty greatly affects the extreme time-varying interconnections, and this impact has decreased after the Paris Agreement came into effect. These results offer valuable insights for market policymakers and shipping companies in achieving a balance between carbon emission reduction and shipping business, particularly amidst heightened climate policy uncertainty.

Association between the oral microbiome and brain resting state connectivity in schizophrenia.

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10-3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.

Fabrication of sea urchin shaped polyaniline-modified magnetic microporous organic network for efficient extraction of non-steroidal anti-inflammatory drugs from animal-derived food samples.

In this work, a novel polyaniline-modified magnetic microporous organic network (MMON-PANI) composite was fabricated for effective magnetic solid phase extraction (MSPE) of five typical nonsteroidal anti-inflammatory drugs (NSAIDs) from animal-derived food samples before high performance liquid chromatography (HPLC) detection. The core-shell sea urchin shaped MMON-PANI integrates the merits of Fe3O4, MON, and PANI, exhibiting large specific surface area, rapid magnetic responsiveness, good stability, and multiple binding sites to NSAIDs. Convenient and effective extraction of trace NSAIDs from chicken, beef and pork samples is realized on MMON-PANI via the synergetic π-π, hydrogen bonding, hydrophobic, and electrostatic interactions. Under optimal conditions, the MMON-PANI-MSPE-HPLC-UV method exhibits wide linear ranges (0.2-1000 µg L-1), low limits of detection (0.07-1.7 µg L-1), good precisions (intraday and inter-day RSDs < 5.4 %, n = 3), large enrichment factors (98.6-99.9), and less adsorbent consumption (3 mg). The extraction mechanism and selectivity of MMON-PANI are also evaluated in detail. This work proves the incorporation of PANI onto MMON is an efficient way to promote NSAIDs enrichment and provides a new strategy to synthesize multifunctional MON-based composites in sample pretreatment.

Unveiling potential roles of earthworms in mitigating the presence of virulence factor genes in terrestrial ecosystems.

Earthworms can redistribute soil microbiota, and thus might affect the profile of virulence factor genes (VFGs) which are carried by pathogens in soils. Nevertheless, the knowledge of VFG profile in the earthworm guts and its interaction with earthworm gut microbiome is still lacking. Herein, we characterized earthworm gut and soil microbiome and VFG profiles in natural and agricultural ecosystems at a national scale using metagenomics. VFG profiles in the earthworm guts significantly differed from those in the surrounding soils, which was mainly driven by variations of bacterial communities. Furthermore, the total abundance of different types of VFGs in the earthworm guts was about 20-fold lower than that in the soils due to the dramatic decline (also by approximately 20-fold) of VFG-carrying bacterial pathogens in the earthworm guts. Additionally, five VFGs related to nutritional/metabolic factors and stress survival were identified as keystones merely in the microbe-VFG network in the earthworm guts, implying their pivotal roles in facilitating pathogen colonization in earthworm gut microhabitats. These findings suggest the potential roles of earthworms in reducing risks related to the presence of VFGs in soils, providing novel insights into earthworm-based bioremediation of VFG contamination in terrestrial ecosystems.

Efficacy and safety of androgen receptor inhibitors for treatment of advanced prostate cancer: A systematic review and network meta-analysis.

AIMS: Androgen receptor inhibitors (ARIs) have become an effective treatment for advanced prostate cancer (PC). However, it is unknown which ARI is the most helpful and safe for men with advanced PC. Our aim is to help physicians make clinical decisions and provide medication guidelines for patients with advanced PC to avoid potential risks when using ARIs for treatment. METHODS: We systematically searched the following databases: PubMed, Embase and Cochrane Library, with a literature publication deadline of February 2023. The primary efficacy outcomes were 18-month overall survival (OS), treatment-emergent adverse events (TEAEs), hypertension and fatigue. The network meta-analysis (NMA) was performed by Stata 15.1, and Revman 5.3 was used to assess the included studies' risk of bias. RESULTS: The analysis included 26 trials with 26 263 people. The surface under the cumulative ranking curve (SUCRA) concluded that enzalutamide (86.8%) showed the best effect in prolonging the OS of patients. Flutamide led to the highest risk of TEAEs (29.9%) and AEs leading to discontinuation (12.8%). Apalutamide (13.4%) led to the highest risk of grade ≥3 TEAEs. Enzalutamide had the highest risk of hypertension (0.2%), grade ≥3 hypertension (4.5%) and fatigue (5.2%). CONCLUSIONS: This NMA indicates there is no one ARI to reach both the most effective and safe therapy aims for treating advanced PC and that there is a compromise between the efficacy and safety of ARIs in the treatment of advanced PC. Physicians should weigh the risks to safety against the anticipated benefits when prescribing these drugs to patients with PC.

Goats on the Move: Evaluating Machine Learning Models for Goat Activity Analysis Using Accelerometer Data.

Putting sensors on the bodies of animals to automate animal activity recognition and gain insight into their behaviors can help improve their living conditions. Although previous hard-coded algorithms failed to classify complex time series obtained from accelerometer data, recent advances in deep learning have improved the task of animal activity recognition for the better. However, a comparative analysis of the generalizing capabilities of various models in combination with different input types has yet to be addressed. This study experimented with two techniques for transforming the segmented accelerometer data to make them more orientation-independent. The methods included calculating the magnitude of the three-axis accelerometer vector and calculating the Discrete Fourier Transform for both sets of three-axis data as the vector magnitude. Three different deep learning models were trained on this data: a Multilayer Perceptron, a Convolutional Neural Network, and an ensemble merging both called a hybrid Convolutional Neural Network. Besides mixed cross-validation, every model and input type combination was assessed on a goat-wise leave-one-out cross-validation set to evaluate its generalizing capability. Using orientation-independent data transformations gave promising results. A hybrid Convolutional Neural Network with L2-norm as the input combined the higher classification accuracy of a Convolutional Neural Network with the lower standard deviation of a Multilayer Perceptron. Most of the misclassifications occurred for behaviors that display similar accelerometer traces and minority classes, which could be improved in future work by assembling larger and more balanced datasets.

The Integration of ANN and FEA and Its Application to Property Prediction of Dual-Performance Turbine Disks.

Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present substantial challenges. The prediction of yield strength is typically derived from the analysis of microstructures under various heat treatment regimes. This method is time-consuming, expensive, and the accuracy often depends on the precision of microstructural characterization. This study successfully employed a coupled method of Artificial Neural Network (ANN) and finite element analysis (FEA) to reveal the relationship between the heat treatment process and yield strength. The coupled method accurately predicted the location specified and temperature-dependent yield strength based on the heat treatment parameters such as holding temperatures and cooling rates. The root mean square error (RMSE) and mean absolute percentage deviation (MAPD) for the training set are 50.37 and 3.77, respectively, while, for the testing set, they are 50.13 and 3.71, respectively. Furthermore, an integrated model of FEA and ANN is established using a Abaqus user subroutine. The integrated model can predict the yield strength based on temperature calculation results and automatically update material properties of the FEA model during the loading process simulation. This allows for an accurate calculation of the stress-strain state of the turbine disk during actual working conditions, aiding in locating areas of stress concentration, plastic deformation, and other critical regions, and provides a novel reliable reference for the rapid design of the turbine disk.

Model of Shape Memory Alloy Actuator with the Usage of LSTM Neural Network.

Shape Memory Alloys (SMAs) are used to design actuators, which are one of the most fascinating applications of SMA. Usually, they are on-off actuators because, in the case of continuous actuators, the nonlinearity of their characteristics is the problem. The main problem, especially in control systems in these actuators, is a hysteretic loop. There are many models of hysteresis, but from a control theory point of view, they are not helpful. This study used an artificial neural network (ANN) to model the SMA actuator hysteresis. The ANN structure and training method are presented in the paper. Data were generated from the Preisach model for training. This approach allowed for quick and controllable data generation, making experiments thoroughly planned and repeatable. The advantage and disadvantage of this approach is the lack of disturbances. The paper's main goal is to model an SMA actuator. Additionally, it explores whether and how an ANN can describe and model the hysteresis loop. A literature review shows that ANNs are used to model hysteresis, but to a limited extent; this means that the hysteresis loop was modelled with a hysteretic element.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

The mechanics of correlated variability in segregated cortical excitatory subnetworks.

Understanding the genesis of shared trial-to-trial variability in neuronal population activity within the sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since it likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in the mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells. Furthermore, our findings provide theoretical support for recent experimental observations showing that cortical inhibition forms structural and functional subnetworks with excitatory cells, in contrast to the classical view that inhibition is a nonspecific blanket suppression of local excitation.

Identifying Core Genes Related to Low-Temperature Stress Resistance in Quinoa Seedlings Based on WGCNA.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.

SignalP: The Evolution of a Web Server.

SignalP ( https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) is a very popular prediction method for signal peptides, the intrinsic signals that make proteins secretory. The SignalP web server has existed since 1995 and is now in its sixth major version. In this historical account, we (three authors who have taken part in the entire journey plus the first author of the latest version) describe the differences between the versions and discuss the various decisions taken along the way.

Protective effect and mechanism of Xiaoyu Xiezhuo decoction on ischemia-reperfusion induced acute kidney injury based on gut-kidney crosstalk.

This study aimed to explore the mechanism of Xiaoyu Xiezhuo decoction (XXD) on ischemia-reperfusion-induced acute kidney injury (IRI-AKI) using network pharmacology methods and gut microbiota analysis. A total of 1778 AKI-related targets were obtained, including 140 targets possibly regulated by AKI in XXD, indicating that the core targets were mainly enriched in inflammatory-related pathways, such as the IL-17 signaling pathway and TNF signaling pathway. The unilateral IRI-AKI animal model was established and randomly divided into four groups: the sham group, the AKI group, the sham + XXD group, and the AKI + XXD group. Compared with the rats in the AKI group, XXD improved not only renal function, urinary enzymes, and biomarkers of renal damage such as Kim-1, cystatin C, and serum inflammatory factors such as IL-17, TNF-α, IL-6, and IL 1-ß, but also intestinal metabolites including lipopolysaccharides, d-lactic acid, indoxyl sulfate, p-cresyl sulfate, and short-chain fatty acids. XXD ameliorated renal and colonic pathological injury as well as inflammation and chemokine gene abundance, such as IL-17, TNF-α, IL-6, IL-1ß, ICAM-1, and MCP-1, in AKI rats via the TLR4/NF-κB/NLRP3 pathway, reducing the AKI score, renal pathological damage, and improving the intestinal mucosa's inflammatory infiltration. It also repaired markers of the mucosal barrier, including claudin-1, occludin, and ZO-1. Compared with the rats in the AKI group, the α diversity was significantly increased, and the Chao1 index was significantly enhanced after XXD treatment in both the sham group and the AKI group. The treatment group significantly reversed this change in microbiota.

Get out of my head: social evaluative brain states carry over into post-feedback rest and influence remembering how others view us.

Learning how others perceive us helps us tune our behavior to form adaptive relationships. But which perceptions stick with us? And when in the learning process are they codified in memory? We leveraged a popular television series-The Office-to answer these questions. Prior to their functional magnetic resonance imaging (fMRI) session, viewers of The Office reported which characters they identified with, as well as which characters they perceived another person (i.e. counterpart) was similar to. During their fMRI scan, participants found out which characters other people thought they and the counterpart were like, and also completed rest scans. Participants remembered more feedback inconsistent with their self-views (vs. views of the counterpart). Although neural activity while encoding self-inconsistent feedback did not meaningfully predict memory, returning to the inconsistent self feedback during subsequent rest did. During rest, participants reinstated neural patterns engaged while receiving self-inconsistent feedback in the dorsomedial prefrontal cortex (DMPFC). DMPFC reinstatement also quadratically predicted self-inconsistent memory, with too few or too many reinstatements compromising memory performance. Processing social feedback during rest may impact how we remember and integrate the feedback, especially when it contradicts our self-views.

EEGGAN-Net: enhancing EEG signal classification through data augmentation.

Background: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications. Methods: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks. Results: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models. Conclusions: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

An integrated network pharmacology approach reveals that Ampelopsis grossedentata improves alcoholic liver disease via TLR4/NF-κB/MLKL pathway.

BACKGROUND: Alcohol-related liver damage is the most prevalent chronic liver disease, which creates a heavy public health burden worldwide. The leaves of Ampelopsis grossedentata have been considered a popular tea and traditional herbal medicine in China for more than one thousand years, and possess anti-inflammatory, antioxidative, hepatoprotective, and antiviral activities. PURPOSE: We explored the protective effects of Ampelopsis grossedentata extract (AGE) against chronic alcohol-induced hepatic injury (alcoholic liver disease, ALD), aiming to elucidate its underlying mechanisms. METHODS: Firstly, UPLC-Q/TOF-MS analysis and network pharmacology were used to identify the constituents and elucidate the potential mechanisms of AGE against ALD. Secondly, C57BL/6 mice were pair-fed the Lieber-DeCarli diet containing either isocaloric maltodextrin or ethanol, AGE (150 and 300 mg/kg/d) and silymarin (200 mg/kg) were administered to chronic ethanol-fed mice for 7 weeks to evaluate the hepatoprotective effects. Serum biochemical parameters were determined, hepatic and ileum sections were used for histologic examination, and levels of inflammatory cytokines and oxidative stress in the liver were examined. The potential molecular mechanisms of AGE in improving ALD were demonstrated by RNA-seq, Western blotting analysis, and immunofluorescence staining. RESULTS: Ten main constituents of AGE were identified using UPLC-Q/TOF-MS and 274 potential ALD-related targets were identified. The enriched KEGG pathways included Toll-like receptor signaling pathway, NF-κB signaling pathway, and necroptosis. Moreover, in vivo experimental studies demonstrated that AGE significantly reduced serum aminotransferase levels and improved pathological abnormalities after chronic ethanol intake. Meanwhile, AGE improved ALD in mice by down-regulating oxidative stress and inflammatory cytokines. Furthermore, AGE notably repaired damaged intestinal epithelial barrier and suppressed the production of gut-derived lipopolysaccharide by elevating intestinal tight junction protein expression. Subsequent RNA-seq and experimental validation indicated that AGE inhibited NF-κB nuclear translocation, suppressed IκB-α, RIPK3 and MLKL phosphorylation and alleviated hepatic necroptosis in mice. CONCLUSION: In this study, we have demonstrated for the first time that AGE protects against alcoholic liver disease by regulating the gut-liver axis and inhibiting the TLR4/NF-κB/MLKL-mediated necroptosis pathway. Therefore, our present work provides important experimental evidence for AGE as a promising candidate for protection against ALD.