Prospectively Isolated Tetraspanin+ Neoblasts Are Adult Pluripotent Stem Cells Underlying Planaria Regeneration.

Proliferating cells known as neoblasts include pluripotent stem cells (PSCs) that sustain tissue homeostasis and regeneration of lost body parts in planarians. However, the lack of markers to prospectively identify and isolate these adult PSCs has significantly hampered their characterization. We used single-cell RNA sequencing (scRNA-seq) and single-cell transplantation to address this long-standing issue. Large-scale scRNA-seq of sorted neoblasts unveiled a novel subtype of neoblast (Nb2) characterized by high levels of PIWI-1 mRNA and protein and marked by a conserved cell-surface protein-coding gene, tetraspanin 1 (tspan-1). tspan-1-positive cells survived sub-lethal irradiation, underwent clonal expansion to repopulate whole animals, and when purified with an anti-TSPAN-1 antibody, rescued the viability of lethally irradiated animals after single-cell transplantation. The first prospective isolation of an adult PSC bridges a conceptual dichotomy between functionally and molecularly defined neoblasts, shedding light on mechanisms governing in vivo pluripotency and a source of regeneration in animals. VIDEO ABSTRACT.

Productive scientists are associated with lower disruption in scientific publishing.

While scientific researchers often aim for high productivity, prioritizing the quantity of publications may come at the cost of time and effort dedicated to individual research. It is thus important to examine the relationship between productivity and disruption for individual researchers. Here, we show that with the increase in the number of published papers, the average citation per paper will be higher yet the mean disruption of papers will be lower. In addition, we find that the disruption of scientists' papers may decrease when they are highly productive in a given year. The disruption of papers in each year is not determined by the total number of papers published in the author's career, but rather by the productivity of that particular year. Besides, more productive authors also tend to give references to recent and high-impact research. Our findings highlight the potential risks of pursuing productivity and aim to encourage more thoughtful career planning among scientists.

Impactful scientists have higher tendency to involve collaborators in new topics.

In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, and resources and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from a controlled surrogate. In particular, we find that highly productive scientists tend to have a higher fraction of single-topic collaborators, while highly cited-i.e., impactful-scientists have a higher fraction of multitopic collaborators. We also suggest a plausible mechanism for this distinction. Moreover, we investigate the cases where scientists involve existing collaborators in a new topic. We find that, compared to productive scientists, impactful scientists show strong preference of collaboration with high-impact scientists on a new topic. Finally, we validate our findings by investigating active scientists in different years and across different disciplines.

Natural variation in the Tn1a promoter regulates tillering in rice.

Rice tillering is an important agronomic trait that influences plant architecture and ultimately affects yield. This can be genetically improved by mining favourable variations in genes associated with tillering. Based on a previous study on dynamic tiller number, we cloned the gene Tiller number 1a (Tn1a), which encodes a membrane-localised protein containing the C2 domain that negatively regulates tillering in rice. A 272 bp insertion/deletion at 387 bp upstream of the start codon in the Tn1a promoter confers a differential transcriptional response and results in a change in tiller number. Moreover, the TCP family transcription factors Tb2 and TCP21 repress the Tn1a promoter activity by binding to the TCP recognition site within the 272 bp indel. In addition, we identified that Tn1a may affect the intracellular K+ content by interacting with a cation-chloride cotransporter (OsCCC1), thereby affecting the expression of downstream tillering-related genes. The Tn1a+272 bp allele, associated with high tillering, might have been preferably preserved in rice varieties in potassium-poor regions during domestication. The discovery of Tn1a is of great significance for further elucidating the genetic basis of tillering characteristics in rice and provides a new and favourable allele for promoting the geographic adaptation of rice to soil potassium.

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.

[Betulinic acid inhibits non-small cell lung cancer by repolarizing tumor-associated macrophages via mTOR signaling pathway].

The abnormal activation of the mammalian target of rapamycin(mTOR) signaling pathway in non-small cell lung cancer(NSCLC) is closely associated with distant metastasis, drug resistance, tumor immune escape, and low overall survival. The present study reported that betulinic acid(BA), a potent inhibitor of mTOR signaling pathway, exhibited an inhibitory activity against NSCLC in vitro and in vivo. CCK-8 and colony formation results demonstrated that BA significantly inhibited the viability and clonogenic ability of H1299, A549, and LLC cells. Additionally, the treatment with BA induced mitochondrion-mediated apoptosis of H1299 and LLC cells. Furthermore, BA inhibited the mobility and invasion of H1299 and LLC cells by down-regulating the expression level of matrix metalloproteinase 2(MMP2) and impairing epithelial-mesenchymal transition. The results demonstrated that the inhibition of mTOR signaling pathway by BA decreased the proportion of M2 phenotype(CD206 positive) cells in total macrophages. Furthermore, a mouse model of subcutaneous tumor was established with LLC cells to evaluate the anti-tumor efficiency of BA in vivo. The results revealed that the administration of BA dramatically retarded the tumor growth and inhibited the proliferation of tumor cells. More importantly, BA increased the ratio of M1/M2 macrophages in the tumor tissue, which implied the enhancement of anti-tumor immunity. In conclusion, BA demonstrated the inhibitory effect on NSCLC by repolarizing tumor-associated macrophages via the mTOR signaling pathway.

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Magnetic resonance spectroscopy and liquid chromatography-mass spectrometry metabolomics study may differentiate pre-eclampsia from gestational hypertension.

OBJECTIVE: To investigate the findings of magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and serum metabolomics for differentiating pre-eclampsia (PE) from gestational hypertension (GH). METHODS: This prospective study enrolled 176 subjects including a primary cohort with healthy non-pregnant women (HN, n = 35), healthy pregnant women (HP, n = 20), GH (n = 27), and PE (n = 39) and a validation cohort with HP (n = 22), GH (n = 22), and PE (n = 11). T1 signal intensity index (T1SI), apparent diffusion coefficient (ADC) value, and the metabolites on MRS were compared. The differentiating performances of single and combined MRI and MRS parameters for PE were evaluated. Serum liquid chromatography-mass spectrometry (LC-MS) metabolomics was investigated by sparse projection to latent structures discriminant analysis. RESULTS: Increased T1SI, lactate/creatine (Lac/Cr), and glutamine and glutamate (Glx)/Cr and decreased ADC value and myo-inositol (mI)/Cr in basal ganglia were found in PE patients. T1SI, ADC, Lac/Cr, Glx/Cr, and mI/Cr yielded an area under the curves (AUC) of 0.90, 0.80, 0.94, 0.96, and 0.94 in the primary cohort, and of 0.87, 0.81, 0.91, 0.84, and 0.83 in the validation cohort, respectively. A combination of Lac/Cr, Glx/Cr, and mI/Cr yielded the highest AUC of 0.98 in the primary cohort and 0.97 in the validation cohort. Serum metabolomics analysis showed 12 differential metabolites, which are involved in pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism. CONCLUSIONS: MRS is expected to be a noninvasive and effective tool for monitoring GH patients to avoid the development of PE. KEY POINTS: • Increased T1SI and decreased ADC value in the basal ganglia were found in PE patients than in GH patients. • Increased Lac/Cr and Glx/Cr, and decreased mI/Cr in the basal ganglia were found in PE patients than in GH patients. • LC-MS metabolomics showed that the major differential metabolic pathways between PE and GH were pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism.

Predicting the cascading dynamics in complex networks via the bimodal failure size distribution.

Cascading failure as a systematic risk occurs in a wide range of real-world networks. Cascade size distribution is a basic and crucial characteristic of systemic cascade behaviors. Recent research works have revealed that the distribution of cascade sizes is a bimodal form indicating the existence of either very small cascades or large ones. In this paper, we aim to understand the properties and formation characteristics of such bimodal distribution in complex networks and further predict the final cascade size. We first find that the bimodal distribution is ubiquitous under certain conditions in both synthetic and real networks. Moreover, the large cascades distributed in the right peak of bimodal distribution are resulted from either the failure of nodes with high load at the first step of the cascade or multiple rounds of cascades triggered by the initial failure. Accordingly, we propose a hybrid load metric (HLM), which combines the load of the initial broken node and the load of failed nodes triggered by the initial failure, to predict the final size of cascading failures. We validate the effectiveness of HLM by computing the accuracy of identifying the cascades belonging to the right and left peaks of the bimodal distribution. The results show that HLM is a better predictor than commonly used network centrality metrics in both synthetic and real-world networks. Finally, the influence of network structure on the optimal HLM is discussed.

Electrochemically mediated bioconversion and integrated purification greatly enhanced co-production of 1,3-propanediol and organic acids from glycerol in an industrial bioprocess.

In this study, we show how electrochemically mediated bioconversion can greatly increase the co-production of 1,3-propanediol and organic acids from glycerol in an industrial bioprocess using a Clostridum pasteurianum mutant. Remarkably, an enhanced butyrate formation was observed due to a weakened butanol pathway of the mutant. This allowed the strain to have a higher ATP generation for an enhanced growth, higher glycerol consumption and PDO production. The PDO titer reached as high as 120.67 g/L at a cathodic current of -400 mA, which is 33% higher than that without electricity, with a concurrent increase of butyric acid by 80%. To fully recover the increased PDO and organic acids, a novel downstream process combining thin film evaporation of PDO and esterification of organic acids with ethanol was developed. This enables the efficient co-production of PDO, ethyl acetate and ethyl butyrate with a high overall carbon use of 87%.

Percutaneous transluminal angioplasty and stenting of post-irradiation stenosis of the vertebral artery.

BACKGROUND: The outcomes of percutaneous transluminal angioplasty and stenting (PTAS) in patients with medically refractory post-irradiation stenosis of the vertebral artery (PISVA) have not been clarified. AIM: This retrospective study evaluated the safety and outcomes of PTAS in patients with severe PISVA compared with their radiation-naïve counterparts (non-RT group). METHODS: Patients with medically refractory severe symptomatic vertebral artery stenosis and undergoing PTAS between 2000 and 2021 were classified as the PISVA group or the non-RT group. The periprocedural neurological complications, periprocedural brain magnetic resonance imaging, the extent of symptom relief, and long-term stent patency were compared. RESULTS: As compared with the non-RT group (22 cases, 24 lesions), the PISVA group (10 cases, 10 lesions) was younger (62.0 ± 8.6 vs 72.4 ± 9.7 years, P = 0.006) and less frequently had hypertension (40.0% vs 86.4%, P = 0.013) and diabetes mellitus (10.0% vs 54.6%, P = 0.024). Periprocedural embolic infarction was not significantly different between the non-RT group and the PISVA group (37.5% vs 35.7%, P = 1.000). At a mean follow-up of 72.1 ± 58.7 (3-244) months, there was no significant between-group differences in the symptom recurrence rate (0.00% vs 4.55%, P = 1.000) and in-stent restenosis rate (10.0% vs 12.5%, P = 1.000). CONCLUSION: PTAS of severe medically refractory PISVA is effective in the management of vertebrobasilar ischemic symptoms in head and neck cancer patients. Technical safety and outcome of the procedure were like those features in radiation-naïve patients.

[Alzheimer's disease classification based on nonlinear high-order features and hypergraph convolutional neural network].

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.

Food web and metabolic interactions of the lung inhabitants Streptococcus pneumoniae and Pseudomonas aeruginosa.

Bacteria that successfully adapt to different substrates and environmental niches within the lung and overcome the immune defence can cause serious lung infections. Such infections are generally complex, and recognized as polymicrobial in nature. Both Pseudomonas aeruginosa and Streptococcus pneumoniae can cause chronic lung infections and were both detected in cystic fibrosis (CF) lung at different stages. In this study, single and dual species cultures of Pseudomonas aeruginosa and Streptococcus pneumoniae were studied under well-controlled planktonic growth conditions. Under pH-controlled conditions, both species apparently benefited from the presence of the other. In co-culture with P. aeruginosa, S. pneumoniae grew efficiently under aerobic conditions, whereas in pure S. pneumoniae culture, growth inhibition occurred in bioreactors with dissolved oxygen concentrations above the microaerobic range. Lactic acid and acetoin that are produced by S. pneumoniae were efficiently utilized by P. aeruginosa. In pH-uncontrolled co-cultures, the low pH triggered by S. pneumoniae assimilation of glucose and lactic acid production negatively affected the growth of both strains. Nevertheless, ammonia production improved significantly, and P. aeruginosa growth dominated at later growth stages. This study revealed unreported metabolic interactions of two important pathogenic microorganisms and shed new lights into pathophysiology of bacterial lung infection.

In vivo detection of dysregulated choline metabolism in paclitaxel-resistant ovarian cancers with proton magnetic resonance spectroscopy.

BACKGROUND: Chemoresistance gradually develops during treatment of epithelial ovarian cancer (EOC). Metabolic alterations, especially in vivo easily detectable metabolites in paclitaxel (PTX)-resistant EOC remain unclear. METHODS: Xenograft models of the PTX-sensitive and PTX-resistant EOCs were built. Using a combination of in vivo proton-magnetic resonance spectroscopy (1H-MRS), metabolomics and proteomics, we investigated the in vivo metabolites and dysregulated metabolic pathways in the PTX-resistant EOC. Furthermore, we analyzed the RNA expression to validate the key enzymes in the dysregulated metabolic pathway. RESULTS: On in vivo 1H-MRS, the ratio of (glycerophosphocholine + phosphocholine) to (creatine + phosphocreatine) ((GPC + PC) to (Cr + PCr))(i.e. Cho/Cr) in the PTX-resistant tumors (1.64 [0.69, 4.18]) was significantly higher than that in the PTX-sensitive tumors (0.33 [0.10, 1.13]) (P = 0.04). Forty-five ex vivo metabolites were identified to be significantly different between the PTX-sensitive and PTX-resistant tumors, with the majority involved of lipids and lipid-like molecules. Spearman's correlation coefficient analysis indicated in vivo and ex vivo metabolic characteristics were highly consistent, exhibiting the highest positive correlation between in vivo GPC + PC and ex vivo GPC (r = 0.885, P < 0.001). These metabolic data suggested that abnormal choline concentrations were the results from the dysregulated glycerophospholipid metabolism, especially choline metabolism. The proteomics data indicated that the expressions of key enzymes glycerophosphocholine phosphodiesterase 1 (GPCPD1) and glycerophosphodiester phosphodiesterase 1 (GDE1) were significantly lower in the PTX-resistant tumors compared to the PTX-sensitive tumors (both P < 0.01). Decreased expressions of GPCPD1 and GDE1 in choline metabolism led to an increased GPC levels in the PTX-resistant EOCs, which was observed as an elevated total choline (tCho) on in vivo 1H-MRS. CONCLUSIONS: These findings suggested that dysregulated choline metabolism was associated with PTX-resistance in EOCs and the elevated tCho on in vivo 1H-MRS could be as an indicator for the PTX-resistance in EOCs.

Strain evolution and novel downstream processing with integrated catalysis enable highly efficient coproduction of 1,3-propanediol and organic acid esters from crude glycerol.

Bioconversion of natural microorganisms generally results in a mixture of various compounds. Downstream processing (DSP) which only targets a single product often lacks economic competitiveness due to incomplete use of raw material and high cost of waste treatment for by-products. Here, we show with the efficient microbial conversion of crude glycerol by an artificially evolved strain and how a catalytic conversion strategy can improve the total products yield and process economy of the DSP. Specifically, Clostridium pasteurianum was first adapted to increased concentration of crude glycerol in a novel automatic laboratory evolution system. At m3 scale bioreactor the strain achieved a simultaneous production of 1,3-propanediol (PDO), acetic and butyric acids at 81.21, 18.72, and 11.09 g/L within only 19 h, respectively, representing the most efficient fermentation of crude glycerol to targeted products. A heterogeneous catalytic step was developed and integrated into the DSP process to obtain high-value methyl esters from acetic and butyric acids at high yields. The coproduction of the esters also greatly simplified the recovery of PDO. For example, a cosmetic grade PDO (96% PDO) was easily obtained by a simple single-stage distillation process (with an overall yield more than 77%). This integrated approach provides an industrially attractive route for the simultaneous production of three appealing products from the crude glycerol fermentation broth, which greatly improve the process economy and ecology.

Control of redox potential in a novel continuous bioelectrochemical system led to remarkable metabolic and energetic responses of Clostridium pasteurianum grown on glycerol.

BACKGROUND: Electro-fermentation (EF) is an emerging tool for bioprocess intensification. Benefits are especially expected for bioprocesses in which the cells are enabled to exchange electrons with electrode surfaces directly. It has also been demonstrated that the use of electrical energy in BES can increase bioprocess performance by indirect secondary effects. In this case, the electricity is used to alter process parameters and indirectly activate desired pathways. In many bioprocesses, oxidation-reduction potential (ORP) is a crucial process parameter. While C. pasteurianum fermentation of glycerol has been shown to be significantly influenced electrochemically, the underlying mechanisms are not clear. To this end, we developed a system for the electrochemical control of ORP in continuous culture to quantitatively study the effects of ORP alteration on C. pasteurianum by metabolic flux analysis (MFA), targeted metabolomics, sensitivity and regulation analysis. RESULTS: In the ORP range of -462 mV to -250 mV, the developed algorithm enabled a stable anodic electrochemical control of ORP at desired set-points and a fixed dilution rate of 0.1 h-1. An overall increase of 57% in the molar yield for 1,3-propanediol was observed by an ORP increase from -462 to -250 mV. MFA suggests that C. pasteurianum possesses and uses cellular energy generation mechanisms in addition to substrate-level phosphorylation. The sensitivity analysis showed that ORP exerted its strongest impact on the reaction of pyruvate-ferredoxin-oxidoreductase. The regulation analysis revealed that this influence is mainly of a direct nature. Hence, the observed metabolic shifts are primarily caused by direct inhibition of the enzyme upon electrochemical production of oxygen. A similar effect was observed for the enzyme pyruvate-formate-lyase at elevated ORP levels. CONCLUSIONS: The results show that electrochemical ORP alteration is a suitable tool to steer the metabolism of C. pasteurianum and increase product yield for 1,3-propanediol in continuous culture. The approach might also be useful for application with further anaerobic or anoxic bioprocesses. However, to maximize the technique's efficiency, it is essential to understand the chemistry behind the ORP change and how the microbial system responds to it by transmitted or direct effects.

Radiologic Imaging Modalities for Colorectal Cancer.

BACKGROUND: Studies reported various diagnostic value of radiologic imaging modalities for diagnosis and management of colorectal cancer (CRC). AIMS: To summary the diagnosis and management of CRC using computed tomography colonography (CTC), magnetic resonance colonography (MRC), and positron emission tomography (PET)/computed tomography (CT). METHODS: Comprehensive literature searches were conducted in PubMed, EmBase, and the Cochrane library for studies published before April 2021. The diagnostic performance of CTC, MRC, and PET/CT for CRC was summarized. RESULTS: A total of 54 studies (17 studies for CTC, 8 studies for MRC, and 29 studies for PET/CT) were selected for final analysis. The sensitivity and specificity for CTC ranged from 27 to 100%, 88 to 100%, respectively, and the pooled sensitivity and specificity for CTC were 0.97 (95% CI 0.88-0.99) and 0.99 (95% CI 0.99-1.00). The sensitivity and specificity for MRC ranged from 48 to 100%, 60 to 100%, respectively, and the pooled sensitivity and specificity for MRC were 0.98 (95% C: 0.77-1.00) and 0.94 (95% CI 0.84-0.98). The sensitivity and specificity for PET/CT ranged from 84 to 100%, 33 to 100%, respectively, and the pooled sensitivity and specificity for PET/CT were 0.94 (95% CI 0.92-0.96) and 0.94 (95% CI 0.90-0.97). The area under the receiver operating characteristic curve for CTC, MRC, and PET/CT was 1.00 (95% CI 0.99-1.00), 0.99 (95% CI 0.98-1.00), and 0.97 (0.95% CI 0.95-0.98), respectively. CONCLUSIONS: This study suggested both CTC and MRC with relative higher diagnostic value for diagnosing CRC, while PET/CT with higher diagnostic value in detecting local recurrence for patients with CRC.

Preserving the topological properties of complex networks in network sampling.

Extremely large-scale networks have received increasing attention in recent years. The development of big data and network science provides an unprecedented opportunity for research on these networks. However, it is difficult to perform analysis directly on numerous real networks due to their large size. A solution is to sample a subnetwork instead for detailed research. Unfortunately, the properties of the subnetworks could be substantially different from those of the original networks. In this context, a comprehensive understanding of the sampling methods would be crucial for network-based big data analysis. In our work, we find that the sampling deviation is the collective effect of both the network heterogeneity and the biases caused by the sampling methods themselves. Here, we study the widely used random node sampling (RNS), breadth-first search, and a hybrid method that falls between these two. We empirically and analytically investigate the differences in topological properties between the sampled network and the original network under these sampling methods. Empirically, the hybrid method has the advantage of preserving structural properties in most cases, which suggests that this method performs better with no additional information needed. However, not all the biases caused by sampling methods follow the same pattern. For instance, properties, such as link density, are better preserved by RNS. Finally, models are constructed to explain the biases concerning the size of giant connected components and link density analytically.

Modeling multi-opinion propagation in complex systems with heterogeneous relationships via Potts model on signed networks.

This paper investigates how the heterogenous relationships around us affect the spread of diverse opinions in the population. We apply the Potts model, derived from condensed matter physics on signed networks, to multi-opinion propagation in complex systems with logically contradictory interactions. Signed networks have received increasing attention due to their ability to portray both positive and negative associations simultaneously, while the Potts model depicts the coevolution of multiple states affected by interactions. Analyses and experiments on both synthetic and real signed networks reveal the impact of the topology structure on the emergence of consensus and the evolution of balance in a system. We find that, regardless of the initial opinion distribution, the proportion and location of negative edges in the signed network determine whether a consensus can be formed. The effect of topology on the critical ratio of negative edges reflects two distinct phenomena: consensus and the multiparty situation. Surprisingly, adding a small number of negative edges leads to a sharp breakdown in consensus under certain circumstances. The community structure contributes to the common view within camps and the confrontation (or alliance) between camps. The importance of inter- or intra-community negative relationships varies depending on the diversity of opinions. The results also show that the dynamic process causes an increase in network structural balance and the emergence of dominant high-order structures. Our findings demonstrate the strong effects of logically contradictory interactions on collective behaviors, and could help control multi-opinion propagation and enhance the system balance.

[Mystery of Yiyin decoction theory: rule discovery and evaluation strategy of atypical pharmacological effects of Chinese medicinal prescription].

Yi Yin, a famous medical scientist and culinary master in the late Xia Dynasty and early Shang Dynasty, developed the Chinese medicinal liquids and Chinese medicinal prescriptions emerged after that. Chinese medicinal prescriptions have attracted much attention because of their unique advantages in the treatment of chronic multifactorial diseases, representing an important direction of drug discovery in the future. Yiyin decoction theory is the superior form of personalized combined medication with advanced consciousness. It is different from not only the magic bullet theory of single component action but also the connotation of modern multi-target drugs. The core of Yiyin decoction theory can be summarized as compound compatibility, multiple effects, and moderate regulation. Compound compatibility refers to that the formulation of Chinese medicinal prescriptions involves the complex synergy and interactions between sovereign, minister, assistant, and guide medicinal materials. Multiple effects mean that the prescriptions employ a variety of mechanisms to exert comprehensive pharmacological effects of nonlinear feedback. Moderate regulation reflects that the prescriptions can accurately regulate the multiple points of the disease biological network as a whole. To solve the mystery of Yiyin decoction theory, we should not only simply study the known active substances(components) and their independent target effects in the mixture, but also mine the "dark matter" and "dark effect" of Chinese medicinal prescriptions. That is, we should learn the neglected atypical pharmacological effects of Chinese medicinal prescriptions and the multi-point nesting mechanism that plays a precise regulatory function in the body. Yiyin decoction theory focuses on the overall pharmacological effect to reflect the comprehensive clinical value of Chinese medicinal prescriptions, which is of great significance for the development of a new model for the evaluation and application of new Chinese medicinal prescriptions in line with the theory of traditional Chinese medicine.