Unravelling the spatial directionality of urban mobility.

As it is central to sustainable urban development, urban mobility has primarily been scrutinised for its scaling and hierarchical properties. However, traditional analyses frequently overlook spatial directionality, a critical factor in city centre congestion and suburban development. Here, we apply vector computation to unravel the spatial directionality of urban mobility, introducing a two-dimensional anisotropy-centripetality metric. Utilising travel data from 90 million mobile users across 60 Chinese cities, we effectively quantify mobility patterns through this metric, distinguishing between strong monocentric, weak monocentric, and polycentric patterns. Our findings highlight a notable difference: residents in monocentric cities face increasing commuting distances as cities expand, in contrast to the consistent commuting patterns observed in polycentric cities. Notably, mobility anisotropy intensifies in the outskirts of monocentric cities, whereas it remains uniform in polycentric settings. Additionally, centripetality wanes as one moves from the urban core, with a steeper decline observed in polycentric cities. Finally, we reveal that employment attraction strength and commuting distance scaling are key to explaining these divergent urban mobility patterns. These insights are important for shaping effective policies aimed at alleviating congestion and guiding suburban housing development.

Pectolinarigenin alleviated septic acute kidney injury via inhibiting Jak2/Stat3 signaling and mitochondria dysfunction.

Sepsis is a systemic inflammatory response to infection, where sepsis-associated acute kidney injury (AKI) is a common morbid disease with a high morbidity and mortality, and however at present no effective therapy exists. Increasing evidence have shown that mitochondrial damage and inflammatory response are important initiating factors in pathogenesis of septic AKI. Natural flavonoid pectolinarigenin exerted anti-inflammatory properties in previous studies, while its role in septic AKI remains unknown. In the study, pectolinarigenin administration significantly ameliorated the dramatic rise of serum creatinine and blood urea nitrogen in lipopolysaccharide (LPS)- and cecal ligation/puncture (CLP)-induced septic mice, respectively. Consistently, LPS/CLP-induced renal damage as implied by histopathological score and the increased injury markers NGAL and KIM-1, which was attenuated by pectolinarigenin. Meanwhile, LPS/CLP triggered proinflammatory cytokine production and inflammation related proteins in the kidneys. However, pectolinarigenin inhibited renal expression of IL-6, IL-1ß, TNF-α, and MCP-1 to improve inflammatory response. Furthermore, pectolinarigenin upregulated Bcl-2 protein expression and suppressed apoptotic protein of BAX and cleaved caspase-3 in the kidneys of CLP-induced septic AKI. Mechanistically, LPS could induce the high expression of IL-6 and trigger the phosphorylation of Jak2 and Stat3, while pectolinarigenin remarkably reduced their corresponding levels. Notably, CLP-induced kidney injury of mice significantly reduced the expression of PGC-1α, OPA1 and increased the expression of Drp1, Cyt-C, where pectolinarigenin pretreatment significantly restored their corresponding expression in mice. In summary, pectolinarigenin improved septic AKI via inhibiting JAK2/STAT3 signaling and mitochondria dysfunction.

Research on the Correlation of Peripheral Blood Inflammatory Markers with PCT, CRP, and PCIS in Infants with Community-Acquired Pneumonia.

Aims: This study aimsto investigate the relationship between peripheral blood neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), and procalcitonin (PCT), C-reactive protein (CRP), and pediatric critical illness score (PCIS) in infants with community-acquired pneumonia (CAP). Methods: 100 infants with bacterial CAP admitted to our hospital between January 2021 and December 2021 were selected as the infected group, and another 100 healthy infants who underwent health check-ups at the same time were selected as the control group, and the NLR, PLR, and SII of peripheral blood of infants in both groups and the serum PCT, CRP, and PCIS scores of infants in the infected group were tested. The correlation between NLR, PLR, SII, and PCT, CRP, and PCIS was analyzed by Spearman's analysis. Results: The peripheral blood levels of NLR, PLR, and SII were higher in the infected group than in the control infants (P < 0.05). The ROC results showed that the AUCs of peripheral blood NLR, PLR, and SII for the diagnosis of infants with CAP were 0.934, 0.737, and 0.882, respectively. The ROC results showed that the AUCs of peripheral blood NLR, PLR, and SII for assessing the extent of disease in infants with CAP were 0.815, 0.710, and 0.813, respectively, with best cut-off values of 2.05, 98.57, and 823.41; the joint predicted AUC was 0.862. Conclusions: NLR, PLR, and SII were significantly elevated in the peripheral blood of infants with CAP, positively correlated with PCT and CRP, and negatively correlated with PSIC scores, and NLR and SII also have some guiding value in early diagnosis and assessment of the extent of the disease in infants and toddlers with CAP.

Case Report: Adolescent-Onset Isolated Nephronophthisis Caused by a Novel Homozygous Inversin Mutation.

Objective: Nephronophthisis (NPHP) is a rare autosomal recessive inherited kidney disease that can cause cystic enlargement of the kidneys, and lead to end-stage renal disease (ESRD) before the age of 30 years. Herein we describe a case of adolescent-onset NPHP with a novel homozygous mutation in the inversin gene (INVS). Methods: The patient was a 15-year-old Chinese boy who presented with ESRD. Genetic testing was performed via whole exome sequencing and validated via Sanger sequencing. A novel homozygous INVS mutation was identified (c. 1909C > T; p. Gln637Ter). Results: The results of laboratory examinations included urinary protein 1.05 g/24 h, urine erythrocyte count 5/high-power field, serum creatinine 1,026.2 µmol/L, and estimated glomerular filtration rate 5.8 ml/min/1.73 mm2. Extrarenal features included hypertension and moderate anemia, and his parents were consanguineous (first cousins). A homozygous 1-bp substitution resulting in a nonsense mutation (c. 1909C > T; p. Gln637Ter) in exon 15 of INVS was detected via whole exome sequencing, and validated via Sanger sequencing. According to the classification system of the American College of Medical Genetics and Genomics, the mutated gene in INVS is strongly pathogenic (PVS1+PM2+PP3+PP5). His parents and a younger brother were heterozygous carriers. Based on the above results he was diagnosed with juvenile type 2 NPHP. He underwent hemodialysis, and received a kidney transplant after 2 months. He is currently recovering well, with a serum creatinine level of 117 µmol/L and an estimated glomerular filtration rate of 79.6 ml/min/1.73 mm2. Conclusion: Here we have described an extremely rare case of adolescent-onset type 2 NPHP caused by a homozygous INVS mutation. The patient had progressed to ESRD by the age of 15 years. The current report will deepen our understanding of the clinical and genetic basis of this disease.

A Study on the Correlations of Anxiety and Depression With Self-Management Ability and Endogenous Creatinine Clearance Rate in Renal Transplant Recipients.

Objective: To explore the effects of anxiety and depression on the self-management ability and endogenous creatinine clearance rate of renal transplant patients. Method: Eighty-eight renal transplant recipients who were followed up in the outpatient clinic of the Affiliated Hospital of Zunyi Medical University were selected using convenient sampling. The self-made general data sheet, Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Self-Management Scale for Kidney Transplant Recipients were used. Correlation analysis was used to find factors related to endogenous creatinine clearance, while multiple linear regression was used to identify factors influencing endogenous creatinine clearance. Patients with or without anxiety and depression were divided into groups, and the indexes of the groups were compared using the independent samples t test, rank-sum test, or chi-squared test. Results: Anxiety was present in 12.5% of patients, depression in 25%, and a moderate level of self-management in 34.1%. Only 9.1% of renal transplant recipients had endogenous creatinine clearance within the normal range, and 34.1% had a body mass index not in the normal range (25% were overweight, and 9.1% were underweight). The endogenous creatinine clearance rate was negatively correlated with age and degree of depression, and positively correlated with body mass index, treatment management score, and psychosocial management score. The main influencing factors of endogenous creatinine clearance rate were age, sex, depression, body mass index, and treatment management score. The endogenous creatinine clearance rate and psychosocial management ability were significantly higher in patients without anxiety and depression than in patients with anxiety and depression (all P < 0.05). Conclusions: Anxiety and depression showed significant negative effects on the psychosocial self-management ability and endogenous creatinine clearance rate of renal transplant recipients and thus should be given more attention.

A universal opportunity model for human mobility.

Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility.

Destination choice game: A spatial interaction theory on human mobility.

With remarkable significance in migration prediction, global disease mitigation, urban planning and many others, an arresting challenge is to predict human mobility fluxes between any two locations. A number of methods have been proposed against the above challenge, including the gravity model, the intervening opportunity model, the radiation model, the population-weighted opportunity model, and so on. Despite their theoretical elegance, all models ignored an intuitive and important ingredient in individual decision about where to go, that is, the possible congestion on the way and the possible crowding in the destination. Here we propose a microscopic mechanism underlying mobility decisions, named destination choice game (DCG), which takes into account the crowding effects resulted from spatial interactions among individuals. In comparison with the state-of-the-art models, the present one shows more accurate prediction on mobility fluxes across wide scales from intracity trips to intercity travels, and further to internal migrations. The well-known gravity model is proved to be the equilibrium solution of a degenerated DCG neglecting the crowding effects in the destinations.

Angiopoietin-1 Promotes the Integrity of Neovascularization in the Subcutaneous Matrigel of Type 1 Diabetic Rats.

OBJECTIVE: This study aimed to investigate the effects of Ang-1 on neovascularization of diabetic organs by subcutaneous Matrigel angiogenesis model, established in type 1 diabetic rats. METHODS: Ang-1 adenoviral vector was constructed. The rat model was established by STZ and divided into four group. The Matrigel was inserted subcutaneously into the abdominal cavity of rats at 8 weeks, the treatment group was injected with Ang-1 adenovirus vector via tail vein, and the rats were sacrificed at 10 weeks. Neovascularization of Matrigel was observed with transmission electron microscopy. The marker of vascular endothelial cell and pericyte were detected by immunofluorescence. Immunohistochemical detection of the neovascular endothelial junction protein was performed. RT-PCR was used to determine protein expression of neovascular in Matrigel. RESULTS: Vascular cavity-like structure could be seen in subcutaneous Matrigel of diabetic rats, and the cavity was filled with a lot of red blood cells. Transmission electron microscopy showed that neovascular endothelial structure of the Matrigel was incomplete, while the Ang-1 treatment group had more vascular cavity-like structures, intact vascular endothelial structure, and reduced inflammatory cell infiltration in Matrigel. Additionally, the integrity of vascularization improved, and the marker of pericyte and the cell tight junctions protein was upregulated in Ang-1 treatment group. CONCLUSION: Hyperglycemia could induce pathological angiogenesis in subcutaneous Matrigel of diabetic rats, and Ang-1 could upregulate the expression of intercellular junction protein in subcutaneous Matrigel of diabetic rats and promote the integrity of neovascularization in the subcutaneous Matrigel of diabetic rats.

A Comprehensive Risk Analysis of Transportation Networks Affected by Rainfall-Induced Multihazards.

Climate change and its projected natural hazards have an adverse impact on the functionality and operation of transportation infrastructure systems. This study presents a comprehensive framework to analyze the risk to transportation infrastructure networks that are affected by natural hazards. The proposed risk analysis method considers both the failure probability of infrastructure components and the expected infrastructure network efficiency and capacity loss due to component failure. This comprehensive approach facilitates the identification of high-risk network links in terms of not only their susceptibility to natural hazards but also their overall impact on the network. The Chinese national rail system and its exposure to rainfall-related multihazards are used as a case study. The importance of various links is comprehensively assessed from the perspectives of topological, efficiency, and capacity criticality. Risk maps of the national railway system are generated, which can guide decisive action regarding investments in preventative and adaptive measures to reduce risk.

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

Universal model of individual and population mobility on diverse spatial scales.

Studies of human mobility in the past decade revealed a number of general scaling laws. However, to reproduce the scaling behaviors quantitatively at both the individual and population levels simultaneously remains to be an outstanding problem. Moreover, recent evidence suggests that spatial scales have a significant effect on human mobility, raising the need for formulating a universal model suited for human mobility at different levels and spatial scales. Here we develop a general model by combining memory effect and population-induced competition to enable accurate prediction of human mobility based on population distribution only. A variety of individual and collective mobility patterns such as scaling behaviors and trajectory motifs are accurately predicted for different countries and cities of diverse spatial scales. Our model establishes a universal underlying mechanism capable of explaining a variety of human mobility behaviors, and has significant applications for understanding many dynamical processes associated with human mobility.

Multiplex congruence network of natural numbers.

Congruence theory has many applications in physical, social, biological and technological systems. Congruence arithmetic has been a fundamental tool for data security and computer algebra. However, much less attention was devoted to the topological features of congruence relations among natural numbers. Here, we explore the congruence relations in the setting of a multiplex network and unveil some unique and outstanding properties of the multiplex congruence network. Analytical results show that every layer therein is a sparse and heterogeneous subnetwork with a scale-free topology. Counterintuitively, every layer has an extremely strong controllability in spite of its scale-free structure that is usually difficult to control. Another amazing feature is that the controllability is robust against targeted attacks to critical nodes but vulnerable to random failures, which also differs from ordinary scale-free networks. The multi-chain structure with a small number of chain roots arising from each layer accounts for the strong controllability and the abnormal feature. The multiplex congruence network offers a graphical solution to the simultaneous congruences problem, which may have implication in cryptography based on simultaneous congruences. Our work also gains insight into the design of networks integrating advantages of both heterogeneous and homogeneous networks without inheriting their limitations.

Maximum entropy, word-frequency, Chinese characters, and multiple meanings.

The word-frequency distribution of a text written by an author is well accounted for by a maximum entropy distribution, the RGF (random group formation)-prediction. The RGF-distribution is completely determined by the a priori values of the total number of words in the text (M), the number of distinct words (N) and the number of repetitions of the most common word (k(max)). It is here shown that this maximum entropy prediction also describes a text written in Chinese characters. In particular it is shown that although the same Chinese text written in words and Chinese characters have quite differently shaped distributions, they are nevertheless both well predicted by their respective three a priori characteristic values. It is pointed out that this is analogous to the change in the shape of the distribution when translating a given text to another language. Another consequence of the RGF-prediction is that taking a part of a long text will change the input parameters (M, N, k(max)) and consequently also the shape of the frequency distribution. This is explicitly confirmed for texts written in Chinese characters. Since the RGF-prediction has no system-specific information beyond the three a priori values (M, N, k(max)), any specific language characteristic has to be sought in systematic deviations from the RGF-prediction and the measured frequencies. One such systematic deviation is identified and, through a statistical information theoretical argument and an extended RGF-model, it is proposed that this deviation is caused by multiple meanings of Chinese characters. The effect is stronger for Chinese characters than for Chinese words. The relation between Zipf's law, the Simon-model for texts and the present results are discussed.

Cascading walks model for human mobility patterns.

BACKGROUND: Uncovering the mechanism behind the scaling laws and series of anomalies in human trajectories is of fundamental significance in understanding many spatio-temporal phenomena. Recently, several models, e.g. the explorations-returns model (Song et al., 2010) and the radiation model for intercity travels (Simini et al., 2012), have been proposed to study the origin of these anomalies and the prediction of human movements. However, an agent-based model that could reproduce most of empirical observations without priori is still lacking. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, considering the empirical findings on the correlations of move-lengths and staying time in human trips, we propose a simple model which is mainly based on the cascading processes to capture the human mobility patterns. In this model, each long-range movement activates series of shorter movements that are organized by the law of localized explorations and preferential returns in prescribed region. CONCLUSIONS/SIGNIFICANCE: Based on the numerical simulations and analytical studies, we show more than five statistical characters that are well consistent with the empirical observations, including several types of scaling anomalies and the ultraslow diffusion properties, implying the cascading processes associated with the localized exploration and preferential returns are indeed a key in the understanding of human mobility activities. Moreover, the model shows both of the diverse individual mobility and aggregated scaling displacements, bridging the micro and macro patterns in human mobility. In summary, our model successfully explains most of empirical findings and provides deeper understandings on the emergence of human mobility patterns.

Universal predictability of mobility patterns in cities.

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

Diversity of individual mobility patterns and emergence of aggregated scaling laws.

Uncovering human mobility patterns is of fundamental importance to the understanding of epidemic spreading, urban transportation and other socioeconomic dynamics embodying spatiality and human travel. According to the direct travel diaries of volunteers, we show the absence of scaling properties in the displacement distribution at the individual level,while the aggregated displacement distribution follows a power law with an exponential cutoff. Given the constraint on total travelling cost, this aggregated scaling law can be analytically predicted by the mixture nature of human travel under the principle of maximum entropy. A direct corollary of such theory is that the displacement distribution of a single mode of transportation should follow an exponential law, which also gets supportive evidences in known data. We thus conclude that the travelling cost shapes the displacement distribution at the aggregated level.

Efficient learning strategy of Chinese characters based on network approach.

We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

Do scientists trace hot topics?

Do scientists follow hot topics in their scientific investigations? In this paper, by performing analysis to papers published in the American Physical Society (APS) Physical Review journals, it is found that papers are more likely to be attracted by hot fields, where the hotness of a field is measured by the number of papers belonging to the field. This indicates that scientists generally do follow hot topics. However, there are qualitative differences among scientists from various countries, among research works regarding different number of authors, different number of affiliations and different number of references. These observations could be valuable for policy makers when deciding research funding and also for individual researchers when searching for scientific projects.

[Relationship between activation of mannan-binding lectin complement and NF-kappaB in diabetic nephropathy].

OBJECTIVE: To study the activation of mannose-binding lectin (MBL) complement in STZ-induced diabetic nephropathy (DN) rats and its relationship with NF-kappaB. METHODS: Sprague-Dawley (SD) rats were randomly divided into two groups: normal control and diabetic nephropathy. Diabetes was induced by intraperitoneal injection of STZ. Five rats were sacrificed at the end of week 1, 2, 4, 8 respectively. Blood glucose, 24 h urine, 24 h urinary albumin, serum creatinine (Scr), body mass and kidney mass were examined at the same time points respectively. Creatinine clearance and renal hypertrophy index were calculated. The renal expression of MBL, membrane attack complex (MAC) and NF-kappaB were determined by immunohistochemistry. RESULT: MBL, MAC and NF-kappaB expression were significantly increased in glomerulus of diabetic nephropathy rats compared to the controls. The expression of MBL was positively correlated with NF-kappaB expression. CONCLUSION: The activation of mannose-binding lectin complement participates in the onset and development of DN.