Nonrandom behavior in the projection of random bipartite networks.

There are two main categories of networks studied in the complexity physics community: Monopartite and bipartite networks. In this paper, we present a general framework that provides insights into the connection between these two classes. When a random bipartite network is projected into a monopartite network, under quite general conditions, the result is a nonrandom monopartite network, the features of which can be studied analytically. Unlike previous studies in the physics literature on complex networks, which rely on sparse-network approximations, we provide a complete analysis, focusing on the degree distribution and the clustering coefficient. Our findings primarily offer a technical contribution, adding to the current body of literature by enhancing the understanding of bipartite networks within the community of physicists. In addition, our model emphasizes the substantial difference between the information that can be extracted from a network measuring its degree distribution, or using higher-order metrics such as the clustering coefficient. We believe that our results are general and have broad real-world implications.

The outcome and predictive model of allogeneic hematopoietic stem cell transplantation among elderly patients with severe aplastic anemia from the Chinese Blood and Marrow Transplant Registry Group.

Not available.

Targeting cytohesin-1 suppresses acute myeloid leukemia progression and overcomes resistance to ABT-199.

Adhesion molecules play essential roles in the homeostatic regulation and malignant transformation of hematopoietic cells. The dysregulated expression of adhesion molecules in leukemic cells accelerates disease progression and the development of drug resistance. Thus, targeting adhesion molecules represents an attractive anti-leukemic therapeutic strategy. In this study, we investigated the prognostic role and functional significance of cytohesin-1 (CYTH1) in acute myeloid leukemia (AML). Analysis of AML patient data from the GEPIA and BloodSpot databases revealed that CYTH1 was significantly overexpressed in AML and independently correlated with prognosis. Functional assays using AML cell lines and an AML xenograft mouse model confirmed that CYTH1 depletion significantly inhibited the adhesion, migration, homing, and engraftment of leukemic cells, delaying disease progression and prolonging animal survival. The CYTH1 inhibitor SecinH3 exerted in vitro and in vivo anti-leukemic effects by disrupting leukemic adhesion and survival programs. In line with the CYTH1 knockdown results, targeting CYTH1 by SecinH3 suppressed integrin-associated adhesion signaling by reducing ITGB2 expression. SecinH3 treatment efficiently induced the apoptosis and inhibited the growth of a panel of AML cell lines (MOLM-13, MV4-11 and THP-1) with mixed-lineage leukemia gene rearrangement, partly by reducing the expression of the anti-apoptotic protein MCL1. Moreover, we showed that SecinH3 synergized with the BCL2-selective inhibitor ABT-199 (venetoclax) to inhibit the proliferation and promote the apoptosis of ABT-199-resistant leukemic cells. Taken together, our results not only shed light on the role of CYTH1 in cell-adhesion-mediated leukemogenesis but also propose a novel combination treatment strategy for AML.

Impact of a novel prognostic model on allogeneic hematopoietic stem cell transplantation outcomes in patients with CMML.

Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic stem cell malignancy, and allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curable treatment. The outcomes after transplant are influenced by both disease characteristics and patient comorbidities. To develop a novel prognostic model to predict the post-transplant survival of CMML patients, we identified risk factors by applying univariable and multivariable Cox proportional hazards regression to a derivation cohort. In multivariable analysis, advanced age (hazard ratio [HR] 3.583), leukocyte count (HR 3.499), anemia (HR 3.439), bone marrow blast cell count (HR 2.095), and no chronic graft versus host disease (cGVHD; HR 4.799) were independently associated with worse survival. A novel prognostic model termed ABLAG (Age, Blast, Leukocyte, Anemia, cGVHD) was developed and the points were assigned according to the regression equation. The patients were categorized into low risk (0-1), intermediate risk (2, 3), and high risk (4-6) three groups and the 3-year overall survival (OS) were 93.3% (95%CI, 61%-99%), 78.9% (95%CI, 60%-90%), and 51.6% (95%CI, 32%-68%; p < .001), respectively. In internal and external validation cohort, the area under the receiver operating characteristic (ROC) curves of the ABLAG model were 0.829 (95% CI, 0.776-0.902) and 0.749 (95% CI, 0.684-0.854). Compared with existing models designed for the nontransplant setting, calibration plots, and decision curve analysis showed that the ABLAG model revealed a high consistency between predicted and observed outcomes and patients could benefit from this model. In conclusion, combining disease and patient characteristic, the ABLAG model provides better survival stratification for CMML patients receiving allo-HSCT.

Hematopoietic Stem Cell Transplantation Activity in China 2020-2021 During the SARS-CoV-2 Pandemic: A Report From the Chinese Blood and Marrow Transplantation Registry Group.

Between 2020 and 2021, 31,525 hematopoietic stem cell transplantations (HSCTs) were reported to the Chinese Blood and Marrow Transplantation Registry Group throughout mainland China. In this report, we describe the activity and current trends for HSCT in China during the SARS-CoV-2 pandemic. In 2020, a total of 13,415 cases of HSCT were reported from 166 transplantation teams, and 75% (10,042 cases) were allogeneic HSCTs. In 2021, a total of 18,110 cases of HSCT were reported from 174 transplantation teams, and 70% (12,744 cases) were allogeneic HSCTs. Haploidentical donor (HID) transplantation accounted for 63% (7977 cases) of allogeneic HSCTs in 2021. The most common indications for allogeneic HSCT for malignant disease were acute myeloid leukemia (37%) and acute lymphoblastic leukemia (23%), and the largest proportion of nonmalignant disease comprised aplastic anemia (13%). The peripheral blood stem cell source accounted for 41% of HIDs and 75% of matched sibling donors. The BuCy-based regimen (57%) was the most popular conditioning regimen for allogeneic HSCT, followed by the BuFlu-based regimen (28%) and total body irradiation-based regimen (11%). This survey provides comprehensive information about the current activities and might benefit clinical physicians' decision planning for HSCT.

An empirical study of college students' reading engagement on academic achievement.

With the popularity of Internet technology, reading has developed in the direction of digitalization and mobileization. And entering the metaverse era, both the subject and object of reading may be redefined, presenting a new developmental pattern. This process brings a crisis to reading, such as the fragmentation of reading, the obstruction of reading needs, and the replacement of classical reading. However, reading is still an important way for college students to acquire new knowledge, broaden their horizons and improve their skills. The existence of reading crises inevitably affects the academic achievement of college students. Therefore, from the perspective of university management, this paper conducts regression analysis on 1,155 effective samples of colleges and universities in Anhui Province, extracts the factors that affect college students' reading engagement, and further explores the relationship between college students' reading engagement and academic achievement. The study concluded that: (1) in terms of family reading culture, students who grow up in families with good family reading culture perform better in reading engagement. The amount of family books, family reading education and family reading atmosphere all have significant positive effects on reading time and reflective reading strategies of college students. (2) In the cultivation of reading habits in colleges and universities, the course-driven mechanism and the atmosphere stimulating mechanism have a significant positive effect on students' reading time. The course-driven mechanism, resource supporting mechanism and atmosphere stimulating mechanism have a significant positive effect on the critical reading strategy of college students. (3) In terms of reading time, it is only found that the reading time spent on paper books has a significant positive effect on college students' academic achievement and professional quality. (4) In terms of reading strategies, the replicative reading strategy only has a significant positive effect on the improvement of college students' academic achievement and professional quality. The critical reading strategy has a significant positive effect on the professional quality, general ability and career planning ability of college students.

Genomic and Molecular Signatures of Successful Patient-Derived Xenografts for Oral Cavity Squamous Cell Carcinoma.

Background: Oral cavity squamous cell carcinoma (OSCC) is an aggressive malignant tumor with high recurrence and poor prognosis in the advanced stage. Patient-derived xenografts (PDXs) serve as powerful preclinical platforms for drug testing and precision medicine for cancer therapy. We assess which molecular signatures affect tumor engraftment ability and tumor growth rate in OSCC PDXs. Methods: Treatment-naïve OSCC primary tumors were collected for PDX models establishment. Comprehensive genomic analysis, including whole-exome sequencing and RNA-seq, was performed on case-matched tumors and PDXs. Regulatory genes/pathways were analyzed to clarify which molecular signatures affect tumor engraftment ability and the tumor growth rate in OSCC PDXs. Results: Perineural invasion was found as an important pathological feature related to engraftment ability. Tumor microenvironment with enriched hypoxia, PI3K-Akt, and epithelial-mesenchymal transition pathways and decreased inflammatory responses had high engraftment ability and tumor growth rates in OSCC PDXs. High matrix metalloproteinase-1 (MMP1) expression was found that have a great graft advantage in xenografts and is associated with pooled disease-free survival in cancer patients. Conclusion: This study provides a panel with detailed genomic characteristics of OSCC PDXs, enabling preclinical studies on personalized therapy options for oral cancer. MMP1 could serve as a biomarker for predicting successful xenografts in OSCC patients.

An Empirical Study on the Improvement of College Students' Employability Based on University Factors.

With the popularization of higher education and the promotion of college enrollment expansion, the number of college graduates increases sharply. At the same time, the continuous transformation and upgrading of the industrial structure put forward higher requirements on the employability of college students, which leads to the imbalance between supply and demand in the labor market. The key to dealing with employment difficulties lie in the improvement of college students' employability. Therefore, we make a regression analysis of 263 valid samples from universities in Anhui Province and extract the factors that influence the improvement of college students' employability in the process of talent cultivation in university. The result shows that there is a positive correlation between course setting, course teaching, club activities, and college students' employability, among which the course teaching and club activities are the most critical factors which may influence college students' employability. In addition, from the viewpoint of individual college students, the overall grades of college students and the time of participating in the internship are also closely related to their employability, i.e., college students with good overall grades and long internship time should also have stronger employability.

[Allogeneic Hematopoietic Stem Cell Transplantation for Paroxysmal Nocturnal Hemoglobinuria].

OBJECTIVE: To analyze the clinical efficacy and safety of allogeneic hematopoietic stem cell transplantation (allo-HSCT) for paroxysmal nocturnal hemoglobinuria (PNH), and preliminarily explore the role of an improved post-transplantation cyclophosphamide (PTCy) based conditioning regimen in PNH patients receiving transplantation. METHODS: Clinical related data of PNH sufferers receiving allo-HSCT in Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology were collected, and hematopoietic reconstitution, chimerism, PNH cloning, graft-versus-host disease (GVHD), infection, and survival were analyzed. RESULTS: Totally five PNH patients receiving allo-HSCT were enrolled, including 1 case with classic PNH, 3 cases with aplastic anemia-PNH syndrome, 1 case with myelodysplastic syndrome, three of them (case 1-3) received the improved PTCy based conditioning regimen before HSCT. All sufferers engrafted successfully within 28 days, the median time of neutrophil and platelet engraftment was 11 days and 12 days, respectively, no patient occurred acute or chronic GVHD, after a median follow-up of 16 months, all recipients survived and completely eliminated PNH cloning. CONCLUSION: Allo-HSCT can completely clear PNH cloning and restore hematopoietic function with controllable complications, and the improved PTCy based conditioning regimen is proved to be effective in PNH transplantation.

Industry Upgrading: Recommendations of New Products Based on World Trade Network.

GDP is a classic indicator of the extent of national economic development. Research based on the World Trade Network has found that a country's GDP depends largely on the products it exports. In order to increase the competitiveness of a country and further increase its GDP, a crucial issue is finding the right direction to upgrade the industry so that the country can enhance its competitiveness. The proximity indicator measures the similarity between products and can be used to predict the probability that a country will develop a new industry. On the other hand, the Fitness-Complexity algorithm can help to find the important products and developing countries. In this paper, we find that the maximum of the proximity between a certain product and a country's existing products is highly correlated with the probability that the country exports this new product in the next year. In addition, we find that the more products that are related to a certain product, the higher probability of the emergence of the new product. Finally, we combine the proximity indicator and the Fitness-Complexity algorithm and then attempt to provide a recommendation list of new products that can help developing countries to upgrade their industry. A few examples are given in the end.

[Effect of Autologous Hematopoietic Stem Cell Transplantation on Recurrent Refractory B Cell NHL and Its Factors Influencing Prognosis].

OBJECTIVE: To analyze the effect of autologous hematopoietic stem cell transplantation in the treatment of patients with recurrent refractory B cell non-Hodgkin's lymphoma (NHL) and the related factors affecting the prognosis. METHODS: The clinical data of 47 cases of recurrent refractory B cell NHL treated in our hospital were retrospectively analyzed. Survival curves were drawn by Kaplan-Meier, and survival analyses were performed. Univariate and multivariate analyses were used to analyze the prognostic factors. RESULTS: The complete remission rate was 51.06% before autologous hematopoietic stem cell transplantation, but it increased to 65.96% after transplantation. The median survival time was 21 months, the 3 years progression-free survival rate was 40.43%, and the 3 years overall survival rate was 48.94%. The results of unvariate analysis showed that no using the rituximab in the first treatment and incomplete remission shown by PET/CT before transplantation all were the risk factors (P<0.05) affecting the prognosis. By multifactor analysis, it was found that the incomplete remission shown by PET/CT before transplantation was a risk factor for the prognosis(P<0.05). CONCLUSION: The application of autologous hematopoietic stem cell transplantation for patients with relapsed and refractory B cell NHL can improve the clinical efficacy, and the incomplete remission shown by PET/CT before transplantation is more adverse to the patients' prognosis.

Randomizing growing networks with a time-respecting null model.

Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology-a time-respecting null model-that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

Economic Complexity Based Recommendation Enhance the Efficiency of the Belt and Road Initiative.

The Belt and Road initiative (BRI) was announced in 2013 by the Chinese government. Its goal is to promote the cooperation between European and Asian countries, as well as enhancing the trust between members and unifying the market. Since its creation, more and more developing countries are joining the initiative. Based on the geographical location characteristics of the countries in this initiative, we propose an improvement of a popular recommendation algorithm that includes geographic location information. This recommendation algorithm is able to make suitable recommendations of products for countries in the BRI. Then, Fitness and Complexity metrics are used to evaluate the impact of the recommendation results and measure the country's competitiveness. The aim of this work is to provide countries' insights on the ideal development direction. By following the recommendations, the countries can quickly increase their international competitiveness.

Identification and impact of discoverers in online social systems.

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems.

Recovery rate affects the effective epidemic threshold with synchronous updating.

Accurate identification of effective epidemic threshold is essential for understanding epidemic dynamics on complex networks. In this paper, we systematically study how the recovery rate affects the susceptible-infected-removed spreading dynamics on complex networks, where synchronous and asynchronous updating processes are taken into account. We derive the theoretical effective epidemic threshold and final outbreak size based on the edge-based compartmental theory. To validate the proposed theoretical predictions, extensive numerical experiments are implemented by using asynchronous and synchronous updating methods. When asynchronous updating method is used in simulations, recovery rate does not affect the final state of spreading dynamics. But with synchronous updating, we find that the effective epidemic threshold decreases with recovery rate, and final outbreak size increases with recovery rate. A good agreement between the theoretical predictions and the numerical results are observed on both synthetic and real-world networks. Our results extend the existing theoretical studies and help us to understand the phase transition with arbitrary recovery rate.

Ranking nodes in growing networks: When PageRank fails.

PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm's efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank's performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

Dynamics of social contagions with limited contact capacity.

Individuals are always limited by some inelastic resources, such as time and energy, which restrict them to dedicate to social interaction and limit their contact capacities. Contact capacity plays an important role in dynamics of social contagions, which so far has eluded theoretical analysis. In this paper, we first propose a non-Markovian model to understand the effects of contact capacity on social contagions, in which each adopted individual can only contact and transmit the information to a finite number of neighbors. We then develop a heterogeneous edge-based compartmental theory for this model, and a remarkable agreement with simulations is obtained. Through theory and simulations, we find that enlarging the contact capacity makes the network more fragile to behavior spreading. Interestingly, we find that both the continuous and discontinuous dependence of the final adoption size on the information transmission probability can arise. There is a crossover phenomenon between the two types of dependence. More specifically, the crossover phenomenon can be induced by enlarging the contact capacity only when the degree exponent is above a critical degree exponent, while the final behavior adoption size always grows continuously for any contact capacity when degree exponent is below the critical degree exponent.

Toward link predictability of complex networks.

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Ranking reputation and quality in online rating systems.

How to design an accurate and robust ranking algorithm is a fundamental problem with wide applications in many real systems. It is especially significant in online rating systems due to the existence of some spammers. In the literature, many well-performed iterative ranking methods have been proposed. These methods can effectively recognize the unreliable users and reduce their weight in judging the quality of objects, and finally lead to a more accurate evaluation of the online products. In this paper, we design an iterative ranking method with high performance in both accuracy and robustness. More specifically, a reputation redistribution process is introduced to enhance the influence of highly reputed users and two penalty factors enable the algorithm resistance to malicious behaviors. Validation of our method is performed in both artificial and real user-object bipartite networks.