Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time series, and then provide a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative-based, contrastive-based, and adversarial-based. These methods are further divided into ten subcategories with detailed reviews and discussions about their key intuitions, main frameworks, advantages and disadvantages. To facilitate the experiments and validation of time series SSL methods, we also summarize datasets commonly used in time series forecasting, classification, anomaly detection, and clustering tasks. Finally, we present the future directions of SSL for time series analysis.

Irreversible Electroporation-Induced Inflammation Facilitates Neutrophil-Mediated Drug Delivery to Enhance Pancreatic Cancer Therapy.

Pancreatic cancer is a deadly disease with a five-year overall survival rate of around 11%. Chemotherapy is a cornerstone in the treatment of this malignancy, but the intratumoral delivery of chemotherapy drugs is impaired by the highly fibrotic tumor-associated stroma. Irreversible electroporation (IRE) is an ablative technique for treating locally advanced pancreatic cancer. During a typical IRE procedure, high-intensity electric pulses are released to kill tumor cells through the irreversible disruption of the cytoplasm membranes. IRE also induces rapid tumor infiltration by neutrophils and offers an opportunity for neutrophil-mediated drug delivery. We herein showed that the IRE-induced neutrophil trafficking was facilitated by the upregulation of neutrophil chemotaxis and migration as well as the release of several chemoattractants. Doxorubicin-loaded bovine serum albumin nanoparticles were prepared and loaded into neutrophils at a ratio of 9.9 ± 1.2 to 11.7 ± 2.0 pg of doxorubicin per cell. The resultant formulation (NP@NEs) efficiently accumulated in the IRE-treated KPC-A377 murine pancreatic tumors with an uptake value of 10.7 ± 1.5 (percent of injected dose per gram of tissue, abbreviated as %ID/g) at 48 h after intravenous injection. In both Panc02 and KPC-A377 murine pancreatic tumor models, the combination of IRE + NP@NEs inhibited tumor growth more effectively than either monotherapy. The tumors treated with the combination also exhibited the lowest frequency of Ki67+ proliferating cells and the highest abundance of terminal deoxynucleotidyl transferase dUTP nick end labeling+ (TUNEL+) apoptotic cells among the experiment groups. Minimal treatment-associated toxicity was observed. Our findings suggest that neutrophil-mediated delivery of chemotherapy drugs is a useful tool to enhance the response of pancreatic cancer to IRE.

Flipover outperforms dropout in deep learning.

Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.

Janus interface enables reversible Zn-ion battery by regulating interfacial water structure and crystal-orientation.

To tackle the shortcomings of traditional battery systems, there has been much focus on aqueous Zn-ion batteries due to various advantages. However, they still suffer from poor stability of Zn anodes. Here, a methionine additive with unique Janus properties is proposed to regulate the behavior of the interface between Zn anodes and the electrolyte environment. Systematic characterizations as well as calculations elucidate that the Janus additive is adsorbed on the Zn anode via zincophilic -NH2, changing the structure of the electric double layer and breaking the hydrogen bonding network among H2O molecules through hydrophobic S-CH3. At the same time, it can induce preferential formation of Zn(101) with high reversibility. The above two functions contribute to the dendrite inhibiting ability of Zn anodes. As validated, fabricated Zn//Zn symmetric cells achieve stable cycles of 4500 h, 1165 h, and 318 h at 1, 5 and 10 mA cm-2/mA h cm-2, respectively. Furthermore, Zn//Cu asymmetric cells with an average coulombic efficiency of 98.9% for 2200 stable cycles can be realized. Finally, Zn//MnO2 full cells exhibit 79.9% capacity retention with an ultra-high coulombic efficiency of 99.9% for 1000 cycles, much better than that of the pure Zn(ClO4)2 system, indicating the great potential of this useful strategy in aqueous batteries.

Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets.

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate GNN inference on large-scale graphs by jointly pruning the graph structure and the model weights. Though promising, GLT encounters robustness and generalization issues when deployed in real-world scenarios, which are also long-standing and critical problems in deep learning ideology. In real-world scenarios, the distribution of unseen test data is typically diverse. We attribute the failures on out-of-distribution (OOD) data to the incapability of discerning causal patterns, which remain stable amidst distribution shifts. In traditional spase graph learning, the model performance deteriorates dramatically as the graph/network sparsity exceeds a certain high level. Worse still, the pruned GNNs are hard to generalize to unseen graph data due to limited training set at hand. To tackle these issues, we propose the Resilient Graph Lottery Ticket (RGLT) to find more robust and generalizable GLT in GNNs. Concretely, we reactivate a fraction of weights/edges by instantaneous gradient information at each pruning point. After sufficient pruning, we conduct environmental interventions to extrapolate potential test distribution. Finally, we perform last several rounds of model averages to further improve generalization. We provide multiple examples and theoretical analyses that underpin the universality and reliability of our proposal. Further, RGLT has been experimentally verified across various independent identically distributed (IID) and out-of-distribution (OOD) graph benchmarks.

Study on the material basis and mechanism of Hemerocallis citrina Baroni on sleep-improvement using Drosophila activity monitoring, metabolomic, targeted screening and transcriptomic.

Daylily (Hemerocallis citrina Baroni, HC) is an edible plant and is traditionally considered with potential to improve sleep. Herein, based on the Drosophila activity monitoring, metabolome, targeted screening and transcriptome, the material basis and mechanism of HC on sleep-improvement was investigated. The results showed that the aqueous extracts of HC (HAE) as well as the ethanol extracts (HEE) all prolonged the total sleep time of insomnia fruit flies, especially HEE-60 and HEE-95 exhibited more significant effects. In addition, 539 of 728 found metabolites were screened as potential sleep-improved metabolites, and quercetin, linoleic acid, phenethyl caffeate, L-methionine and γ-aminobutyric acid were considered as core active metabolites. Meanwhile, 368 differentially expressed genes (DEGs) were revealed by transcriptomics analysis, and the neuroactive ligand-receptor interaction was deduced as the main pathway by KEGG pathway enrichment. Furthermore, nine DEGs located in this pathway, namely betaTry, deltaTry, gammaTry, epsilonTry, etaTry, iotaTry, lambdaTry, kappaTry and CG30031 were proven being up-regulated. All these results contribute to the development of HC-related functional foods.

Glutamine alleviates Lipopolysaccharide-induced corneal epithelial inflammation and oxidative stress in dogs.

Pseudomonas aeruginosa is a common pathogenic bacteria in canine ophthalmology. Lipopolysaccharide (LPS), a component in the cell wall of gram-negative bacteria, is released following bacterial lysis and causes pathology and inflammation of the cornea. Antibiotics are used to treat bacterial keratitis, and the reuse of antibiotics can easily cause bacterial resistance. Research has shown that glutamine (GLN) has anti-inflammatory and antioxidant biological functions. Herein, we explored the effects and underlying mechanisms of GLN and established an LPS-induced cornea inflammation model. Treatment groups comprised: control check (CK), LPS, LPS + GLN, and Sham groups. Topical GLN treatment alleviated corneal opacity, reduced corneal injury, and accelerated corneal wound healing. Furthermore, GLN treatment altered the uniform distribution of corneal epithelial cells and transformed the healing approach of these cells in the corneal wound from crawling to filling. The expression of Toll-like receptor 4 (TLR4), IL-6, TNF-α, and p-p65 and the activity of myeloperoxidase and superoxide dismutase decreased while the content of malondialdehyde increased in the LPS + GLN group compared with those in the LPS group. Thus, our study suggests that LPS-induced inflammation and oxidative stress may be suppressed via the TLR4/NF-κB signaling pathway by GLN and that GLN could be used as an adjunct therapy to reduce antibiotic use.

Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference.

Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this article, we aim to infer values at nonsensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. First, data exhibit distinct patterns at both long-and short-term temporal scales, which should be analyzed separately. Second, short-term patterns contain more delicate relations, including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short-and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long-and short-term relations, achieving state-of-the-art performance against existing methods.

Proteomic Study on the Mechanism of Arsenic Neurotoxicity in the Rat Cerebral Cortex and the Protective Mechanism of Dictyophora Polysaccharides against Arsenic Neurotoxicity.

Arsenic (As) is a toxic element, and long-term exposure to As can cause neurotoxicity. The bioactive natural compound Dictyophora polysaccharide (DIP) from edible plants has been reported to reduce the toxicity of As. In this study, As poisoning was simulated by feeding As-containing feed, followed by proteomic analysis after one month of DIP treatment. The proteomic analysis showed that 145, 276, and 97 proteins were differentially expressed between the As-treated rats and control rats (As/Ctrl group), DIP-treated + As-treated and As-treated rats (DIP + As/As group), and DIP + As and control rats (DIP + As/Ctrl group), respectively. The differentially expressed proteins (DEPs) in the As/Ctrl and DIP + As/Ctrl groups were mainly related to apoptosis, synapses, energy metabolism, nervous system development, and mitochondria. After DIP treatment, the expression of the dysregulated proteins in the As/Ctrl group was restored or reversed, and 12 of them were reversed proteins. These results suggest that energy metabolism disorder, apoptosis, mitochondrial dysfunction, nervous system development injury, synaptic dysfunction, and oxidative stress may be the key pathological mechanisms of As-induced nerve injury in rats. DIP can restore or reverse the expression of related proteins, which may be the main mechanism of its intervention in As poisoning.

Integrating omics and network pharmacology reveals the anti-constipation role of chitosan with different molecular weights in constipated mice.

This study aimed to reveal the constipation-relieving role of chitosan (COS) with different molecular weights (1 kDa, 3 kDa and 244 kDa). Compared with COS3K (3 kDa) and COS240K (244 kDa), COS1K (1 kDa) more significantly accelerated gastrointestinal transit and defecation frequency. These differential effects were reflected in the regulation of specific gut microbiota (Desulfovibrio, Bacteroides, Parabacteroides and Anaerovorax) and short-chain fatty acids (propionic acid, butyric acid and valeric acid). RNA-sequencing found that the differential expressed genes (DEGs) caused by different molecular weights of COS were mainly enriched in intestinal immune-related pathways, especially cell adhesion molecules. Furthermore, network pharmacology revealed two candidate genes (Clu and Igf2), which can be regarded as the key molecules for the differential anti-constipation effects of COS with different molecular weights. These results were further verified by qPCR. In conclusion, our results provide a novel research strategy to help understand the differences in the anti-constipation effects of chitosan with different molecular weights.

Study on Anti-Constipation Effects of Hemerocallis citrina Baroni through a Novel Strategy of Network Pharmacology Screening.

Daylily (Hemerocallis citrina Baroni) is an edible plant widely distributed worldwide, especially in Asia. It has traditionally been considered a potential anti-constipation vegetable. This study aimed to investigate the anti-constipation effects of daylily from the perspective of gastro-intestinal transit, defecation parameters, short-chain organic acids, gut microbiome, transcriptomes and network pharmacology. The results show that dried daylily (DHC) intake accelerated the defecation frequency of mice, while it did not significantly alter the levels of short-chain organic acids in the cecum. The 16S rRNA sequencing showed that DHC elevated the abundance of Akkermansia, Bifidobacterium and Flavonifractor, while it reduced the level of pathogens (such as Helicobacter and Vibrio). Furthermore, a transcriptomics analysis revealed 736 differentially expressed genes (DEGs) after DHC treatment, which are mainly enriched in the olfactory transduction pathway. The integration of transcriptomes and network pharmacology revealed seven overlapping targets (Alb, Drd2, Igf2, Pon1, Tshr, Mc2r and Nalcn). A qPCR analysis further showed that DHC reduced the expression of Alb, Pon1 and Cnr1 in the colon of constipated mice. Our findings provide a novel insight into the anti-constipation effects of DHC.

One-Pot Synthesis of 2D-2D WO3 /g-C3 N4 Photocatalyst in Reverse Microemulsion System via Supercritical CO2 for Enhanced Hydrogen Generation.

Construction of Z-scheme photocatalyst is an effective approach for using solar energy to produce hydrogen during water splitting. Herein, 2D/2D WO3 /g-C3 N4 heterojunction photocatalyst was synthesized by a convenient and green method including exfoliation and heterojunction procedures, in the reverse microemulsion system via supercritical carbon dioxide (scCO2 ). The resultant W/CN-10.3 composite exhibited enhanced photocatalytic activities towards the hydrogen evolution during water splitting with a hydrogen evolution rate of 688.51 µmol g-1 h-1 , which was more than 16 times higher than bulk g-C3 N4 with the same loading amount of Pt as cocatalyst. Due to its effective separation of photogenerated carriers and prolonged lifetime, more photoexcited electrons with high reduction ability could contribute to the production of H2 . Possible formation mechanism of 2D-2D WO3 /g-C3 N4 nanosheets via scCO2 in the reverse microemulsion system by the one-pot method has been proposed. This work provides an efficient and green strategy to synthesize 2D-2D heterojunction for the utilization in solar-to-fuel conversion.

Both LTA and LTB Subunits Are Equally Important to Heat-Labile Enterotoxin (LT)-Enhanced Bacterial Adherence.

There is increasing evidence indicating that the production of heat-labile enterotoxin (LT) enhances bacterial adherence within in vitro and in vivo models. However, which subunit plays the main role, and the precise regulatory mechanisms remain unclear. To further elucidate the contribution of the A subunit of LT (LTA) and the B subunit of LT (LTB) in LT-enhanced bacterial adherence, we generated several LT mutants where their ADP-ribosylation activity or GM1 binding ability was impaired and evaluated their abilities to enhance the two LT-deficient E. coli strains (1836-2 and EcNc) adherence. Our results showed that the two LT-deficient strains, expressing either the native LT or LT derivatives, had a significantly greater number of adhesions to host cells than the parent strains. The adherence abilities of strains expressing the LT mutants were significantly reduced compared with the strains expressing the native LT. Moreover, E. coli 1836-2 and EcNc strains when exogenously supplied with cyclic AMP (cAMP) highly up-regulated the adhesion molecules expression and improved their adherence abilities. Ganglioside GM1, the receptor for LTB subunit, is enriched in lipid rafts. The results showed that deletion of cholesterol from cells also significantly decreased the ability of LT to enhance bacterial adherence. Overall, our data indicated that both subunits are equally responsible for LT-enhanced bacterial adherence, the LTA subunit contributes to this process mainly by increasing bacterial adhesion molecules expression, while LTB subunit mainly by mediating the initial interaction with the GM1 receptors of host cells.

Hypoglycemic effects of different molecular weight konjac glucomannans via intestinal microbiota and SCFAs mediated mechanism.

The hypoglycemic effects of konjac glucomannans (KGMs) are well recognized, and our previous study showed KGMs with different molecular weight have different hypoglycemic effects on diabetes rats, but the detailed mechanisms still remain unclear. In this study, KGMs with medium molecular weight (KGM-M, 757.1 kDa) and low molecular weight (KGM-L, 87.3 kDa) were utilized to investigate the possible mechanism on hypoglycemic effects of type 2 diabetic (T2DM) rats. The results revealed that KGM-M had better effects than KGM-L on decreasing fasting blood glucose, mitigating insulin resistance and improving inflammation. Further mechanism analysis showed that KGM-M better enriched gut flora diversity and the abundance of Ruminococcus and Lachnoclostridium, which was accompanied by increased short chain fatty acids (SCFAs) production and expression of G protein-coupled receptors (GPCRs), and improved regulation on bile acid synthesis. Antibiotics treatment eliminated the beneficial effects of KGMs on gut flora, SCFAs, GPCRs and bile acid synthesis. By contrast, fecal microbiota transplantation (FMT) treatment restored the structure of intestinal microbiota. And after FMT treatment, KGM-M displayed higher hypoglycemic activity than KGM-L, probably due to the better effects on intestinal microbiota, SCFAs production, GPCRs expression and bile acid synthesis inhibition.

Heat-labile enterotoxin enhances F4-producing enterotoxigenic E. coli adhesion to porcine intestinal epithelial cells by upregulating bacterial adhesins and STb enterotoxin.

As one of the crucial enterotoxins secreted by enterotoxigenic Escherichia coli (ETEC), heat-labile enterotoxin (LT) enhances bacterial adherence both in vivo and in vitro; however, the underlying mechanism remains unclear. To address this, we evaluated the adherence of LT-producing and LT-deficient ETEC strains using the IPEC-J2 cell model. The expression levels of inflammatory cytokines and chemokines, and tight-junction proteins were evaluated in IPEC-J2 cells after infection with various ETEC strains. Further, the levels of adhesins and enterotoxins were also evaluated in F4ac-producing ETEC (F4 + ETEC) strains after treatment with cyclic AMP (cAMP). The adherence of the ΔeltAB mutant was decreased compared with the wild-type strain, whereas adherence of the 1836-2/pBR322-eltAB strain was markedly increased compared with the 1836-2 parental strain. Production of LT up-regulated the expression of TNF-α, IL-6, CXCL-8, and IL-10 genes. However, it did not appear to affect tight junction protein expression. Importantly, we found that cAMP leads to the upregulation of adhesin production and STb enterotoxin. Moreover, the F4 + ETEC strains treated with cAMP also had greater adhesion to IPEC-J2 cells, and the adherence of ΔfaeG, ΔfliC, and ΔestB mutants was decreased. These results indicate that LT enhances the adherence of F4 + ETEC due primarily to the upregulation of F4 fimbriae, flagellin, and STb enterotoxin expression and provide insights into the pathogenic mechanism of LT and ETEC.

Comparative Proteomic Analysis Reveals the Ascorbate Peroxidase-Mediated Plant Resistance to Verticillium dahliae in Gossypium barbadense.

In previous research on the resistance of cotton to Verticillium wilt (VW), Gossypium hirsutum and G. barbadense were usually used as the susceptible and resistant cotton species, despite their different genetic backgrounds. Herein, we present data independent acquisition (DIA)-based comparative proteomic analysis of two G. barbadense cultivars differing in VW tolerance, susceptible XH7 and resistant XH21. A total of 4,118 proteins were identified, and 885 of them were differentially abundant proteins (DAPs). Eight co-expressed modules were identified through weighted gene co-expression network analysis. GO enrichment analysis of the module that significantly correlated with V. dahliae infection time revealed that oxidoreductase and peroxidase were the most significantly enriched GO terms. The last-step rate-limiting enzyme for ascorbate acid (AsA) biosynthesis was further uncovered in the significantly enriched GO terms of the 184 XH21-specific DAPs. Additionally, the expression of ascorbate peroxidase (APX) members showed quick accumulation after inoculation. Compared to XH7, XH21 contained consistently higher AsA contents and rapidly increased levels of APX expression, suggesting their potential importance for the resistance to V. dahliae. Silencing GbAPX1/12 in both XH7 and XH 21 resulted in a dramatic reduction in VW resistance. Our data indicate that APX-mediated oxidoreductive metabolism is important for VW resistance in cotton.

The Anti-Constipation Effects of Raffino-Oligosaccharide on Gut Function in Mice Using Neurotransmitter Analyses, 16S rRNA Sequencing and Targeted Screening.

Raffino-oligosaccharide (ROS), the smallest oligosaccharide of the raffinose family, is a novel food ingredient. However, the anti-constipation effects of ROS remain obscure. This study investigates the anti-constipation effects of ROS based on the loperamide-induced mice model and reveals the underlying mechanism using constipation parameters, neurotransmitter level, 16S rRNA sequencing, and the targeted screening strategy. The prevention effects were firstly investigated by the gastro-intestinal transit rate experiment (50 mice) and defecation status experiment (50 mice), which were divided into five groups (n = 10/group): blank, model, and low-, medium- and high-dose ROS. Furthermore, the slow-transit constipation experiment (blank, model, and high-dose ROS, n = 10/group) was conducted to illustrate the underlying mechanism. The results showed that ROS aided in preventing the occurrence of constipation by improving the gastro-intestinal transit rate and the defecation frequency in mice, and ROS significantly reduced the serum levels of vasoactive intestinal peptide (VIP). In addition, ROS regulated the diversity and structure of intestinal flora. Among them, one specific family and six specific genera were significantly regulated in constipated mice. The targeted screening revealed that 29 targets related to the anti-constipation effects of ROS, indicating ROS may play a role by regulating multiple targets. Furthermore, the network pharmacology analysis showed that Akt1, Stat3, Mapk8, Hsp90aa1, Cat, Alb, Icam1, Sod2, and Gsk3b can be regarded as the core anti-constipation targets. In conclusion, ROS could effectively relieve constipation, possibly by inhibiting the level of neurotransmitters and regulating the gut flora in mice. This study also provides a novel network pharmacology-based targeted screening strategy to reveal the anti-constipation effects of ROS.

Phase function estimation from a diffuse optical image via deep learning.

Objective.The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey-Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function.Approach.Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters.Main Results.Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey-Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%.Significance.We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.

Visual Cascade Analytics of Large-Scale Spatiotemporal Data.

Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges 1) generalized pattern inference; 2) implicit influence visualization; and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.

Study and Experimental Validation of the Functional Components and Mechanisms of Hemerocallis citrina Baroni in the Treatment of Lactation Deficiency.

The function of Hemerocallis citrina Baroni (daylily) on promoting lactation is reported in several ancient Chinese medicine books. However, nowadays, there is no conclusive data to support this statement. In this study, we investigated the effect of Hemerocallis citrina Baroni extract (HCE) on lactation insufficiency in chronic unpredictable mild stress (CUMS) dams and further explored the mechanism and functional components through network pharmacology. The results showed that HCE could increase the offspring's weight, serum prolactin (PRL), and oxytocin (OT) level of CUMS dams. Network pharmacology analysis revealed that the facilitation of HCE on lactation is the result of the comprehensive action of 62 components on 209 targets and 260 pathways, among this network, quercetin, kaempferol, thymidine, etc., were the vital material basis, signal transducer and activator of transcription 3 (STAT3), mitogen activity protein kinase 1 (MAPK1), tumor protein P53 (TP53), etc., were the core targets, and the prolactin signaling pathway was the core pathway. In addition, verification test results showed that HCE regulated the abnormal expression of the prolactin signaling pathway, including STAT3, cyclin D1 (CCND1), MAPK1, MAPK8, nuclear factor NF-kappa-B p105 subunit (NFKB1), and tyrosine-protein kinase (JAK2). In conclusion, HCE exhibited a facilitation of lactation insufficiency, in which quercetin, kaempferol, thymidine, etc., were the most important material basis. The mechanism of this promotional effect is mediated by the prolactin signaling pathway in mammary gland.