Spatial heterogeneity in implicit housing prices: evidence from Hangzhou, China

Estimated coefficients in hedonic price models are generally assumed to be constant throughout the entire study area. However, increasing evidence reveals that the marginal prices of housing characteristics may vary over space and that the spatial heterogeneity problem in implicit housing prices should be given attention. Taking Hangzhou, China, as an example, this study uses the micro data of 603 residential communities in 2014 to examine spatial heterogeneity in implicit housing prices. On the basis of the traditional hedonic price model, we establish spatial expansion and geographically weighted regression (GWR) models for comparative analysis. Results show that the spatial expansion and GWR models have excellent goodness of fit and can improve the traditional hedonic price model. The mixed geographically weighted regression (MGWR) model further reveals that the implicit prices of nine housing characteristics vary significantly over space and that the impacts of the four remaining housing characteristics on housing prices are fixed throughout the entire study area. Unlike the traditional hedonic price model and spatial expansion model, the GWR/MGWR model has the unique advantage of visually providing the spatial distribution of implicit housing prices and accurately describing spatial heterogeneity

Photoactivatable nanogenerators of reactive species for cancer therapy

In recent years, reactive species-based cancer therapies have attracted tremendous attention due to their simplicity, controllability, and effectiveness. Herein, we overviewed the state-of-art advance for photo-controlled generation of highly reactive radical species with nanomaterials for cancer therapy. First, we summarized the most widely explored reactive species, such as singlet oxygen, superoxide radical anion (O2●), nitric oxide (●NO), carbon monoxide, alkyl radicals, and their corresponding secondary reactive species generated by interaction with other biological molecules. Then, we discussed the generating mechanisms of these highly reactive species stimulated by light irradiation, followed by their anticancer effect, and the synergetic principles with other therapeutic modalities. This review might unveil the advantages of reactive species-based therapeutic methodology and encourage the pre-clinical exploration of reactive species-mediated cancer treatments

Data-Driven Modeling of Landau Damping by Physics-Informed Neural Networks

Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the long-standing problems in plasma physics is the integration of kinetic physics into fluid models, which is often achieved through sophisticated analytical closure terms. In this study, we successfully construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN). The multi-moment fluid model constructed using either PINN or gPINN reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. For the first time, we introduce a new variant of the gPINN architecture, namely, gPINN$p$ to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN$p$ only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN$p$-constructed multi-moment fluid model offers the most accurate results. This work sheds new light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.Comment: 11 pages, 7 figure

Nutritional Risk, Health Outcomes, and Hospital Costs Among Chinese Immobile Older Inpatients: A National Study

Purpose: Evidence of the impact of nutritional risk on health outcomes and hospital costs among Chinese older inpatients is limited. Relatively few studies have investigated the association between clinical and cost outcomes and nutritional risk in immobile older inpatients, particularly those with neoplasms, injury, digestive, cardiac, and respiratory conditions. Methods: This China-wide prospective observational cohort study comprised 5,386 immobile older inpatients hospitalized at 25 hospitals. All patients were screened for nutritional risk using the Nutrition Risk Screening (NRS 2002). A descriptive analysis of baseline variables was followed by multivariate analysis (Cox proportional hazards models and generalized linear model) to compare the health and economic outcomes, namely, mortality, length of hospital stay (LoS), and hospital costs associated with a positive NRS 2002 result. Results: The prevalence of a positive NRS 2002 result was 65.3% (n = 3,517). The prevalence of “at-risk” patients (NRS 2002 scores of 3+) was highest in patients with cardiac conditions (31.5%) and lowest in patients with diseases of the respiratory system (6.9%). Controlling for sex, age, education, type of insurance, smoking status, the main diagnosed disease, and Charlson comorbidity index (CCI), the multivariate analysis showed that the NRS 2002 score = 3 [hazard ratio (HR): 1.376, 95% CI: 1.031–1.836] were associated with approximately a 1.5-fold higher likelihood of death. NRS 2002 scores = 4 (HR: 1.982, 95% CI: 1.491–2.633) and NRS scores ≥ 5 (HR: 1.982, 95% CI: 1.498–2.622) were associated with a 2-fold higher likelihood of death, compared with NRS 2002 scores <3. An NRS 2002 score of 3 (percentage change: 16.4, 95% CI: 9.6–23.6), score of 4 (32.4, 95% CI: 24–41.4), and scores of ≥ 5 (36.8, 95% CI 28.3–45.8) were associated with a significantly (16.4, 32.4, and 36.8%, respectively) higher likelihood of increased LoS compared with an NRS 2002 scores <3. The NRS 2002 score = 3 group (17.8, 95% CI: 8.6–27.7) was associated with a 17.8%, the NRS 2002 score = 4 group (31.1, 95% CI: 19.8–43.5) a 31.1%, and the NRS 2002 score ≥ 5 group (44.3, 95% CI: 32.3–57.4) a 44.3%, higher likelihood of increased hospital costs compared with a NRS 2002 scores <3 group. Specifically, the most notable mortality-specific comorbidity and LoS-specific comorbidity was injury, while the most notable cost-specific comorbidity was diseases of the digestive system. Conclusions: This study demonstrated the high burden of undernutrition at the time of hospital admission on the health and hospital cost outcomes for older immobile inpatients. These findings underscore the need for nutritional risk screening in all Chinese hospitalized patients, and improved diagnosis, treatment, and nutritional support to improve immobile patient outcomes and to reduce healthcare costs

Gene Network Analysis for Osteoporosis, Sarcopenia, Diabetes, and Obesity in Human Mesenchymal Stromal Cells

The systemic gene interactions that occur during osteoporosis and their underlying mechanisms remain to be determined. To this end, mesenchymal stromal cells (MSCs) were analyzed from bone marrow samples collected from healthy individuals (n = 5) and patients with osteoporosis (n = 5). A total of 120 osteoporosis-related genes were identified using RNA-sequencing (RNA-seq) and Ingenuity Pathway Analysis (IPA) software. In order to analyze these genes, we constructed a heatmap of one-way hierarchical clustering and grouped the gene expression patterns of the samples. The MSCs from one control participant showed a similar expression pattern to that observed in the MSCs of three patients with osteoporosis, suggesting that the differentiating genes might be important genetic determinants of osteoporosis. Then, we selected the top 38 genes based on fold change and expression, excluding osteoporosis-related genes from the control participant. We identified a network among the top 38 genes related to osteoblast and osteoclast differentiation, bone remodeling, osteoporosis, and sarcopenia using the Molecule Activity Predictor program. Among them, 25 genes were essential systemic genes involved in osteoporosis. Furthermore, we identified 24 genes also associated with diabetes and obesity, among which 10 genes were involved in a network related to bone and energy metabolism. The study findings may have implications for the treatment and prevention of osteoporosis

A randomized trial evaluating the association between related gene polymorphism and nausea and vomiting induced by cisplatin multi-day chemotherapy

Abstract Purpose We aim to investigate the correlation between gene polymorphisms and cisplatin chemotherapy-induced nausea and vomiting (CINV), which was prevented by olanzapine or aprepitant triple antiemetic regimen. Methods Before chemotherapy, the blood samples of 89 malignant tumor patients who received multi-day chemotherapy with cisplatin were collected for sequencing and typing. As there were duplicate patients enrolled in different chemotherapy cycles, there were a total of 190 cases. The patients were divided into two groups randomly, who received the triple antiemetic regimen of olanzapine or aprepitant combined with 5-HT3RA and dexamethasone. The main evaluation indicators were the total protection (TP) rate in the acute phase (0–24 h), the delayed phase (25–120 h) and the overall phase (0-120 h). Results Univariate analysis was performed on genetic loci that reached H-W balance with TP. In the olanzapine group, increased TP in the acute phase was associated with HTR3A rs1176719 non-GG (P < 0.05) genotype etc. Increased TP in the delayed phase was associated with HTR3A rs1176719 non-GG (P < 0.05) genotype etc. In the aprepitant group, increased TP in the acute phase was associated with the MTHFR rs1801131 TT (P < 0.05) genotype etc. Increased TP in the delayed phase was associated with HTR3A rs1062613 CC (P < 0.05) genetype ect. Multivariate Logistic regression analysis showed that HTR3B rs7943062GG (P < 0.05) genotype etc. were correlated with increased TP in the delayed phase. MTHFR rs1801131TT genotype was associated with increased TP in the acute phase (P < 0.05) and delayed phase (P < 0.05). Conclusion This study found that gene polymorphisms, including HTR3B (rs1062613, rs1176719, rs2276303), HTR3B (rs45460698, rs7943062), HTR3C (rs6766410), ERCC1 (rs3212986), ERCC4 (rs744154) and MTHFR(rs1801131), may be independent prognostic factors for CINV

Gene Network Analysis for Osteoporosis, Sarcopenia, Diabetes, and Obesity in Human Mesenchymal Stromal Cells

The systemic gene interactions that occur during osteoporosis and their underlying mechanisms remain to be determined. To this end, mesenchymal stromal cells (MSCs) were analyzed from bone marrow samples collected from healthy individuals (n = 5) and patients with osteoporosis (n = 5). A total of 120 osteoporosis-related genes were identified using RNA-sequencing (RNA-seq) and Ingenuity Pathway Analysis (IPA) software. In order to analyze these genes, we constructed a heatmap of one-way hierarchical clustering and grouped the gene expression patterns of the samples. The MSCs from one control participant showed a similar expression pattern to that observed in the MSCs of three patients with osteoporosis, suggesting that the differentiating genes might be important genetic determinants of osteoporosis. Then, we selected the top 38 genes based on fold change and expression, excluding osteoporosis-related genes from the control participant. We identified a network among the top 38 genes related to osteoblast and osteoclast differentiation, bone remodeling, osteoporosis, and sarcopenia using the Molecule Activity Predictor program. Among them, 25 genes were essential systemic genes involved in osteoporosis. Furthermore, we identified 24 genes also associated with diabetes and obesity, among which 10 genes were involved in a network related to bone and energy metabolism. The study findings may have implications for the treatment and prevention of osteoporosis

A study on detecting multi-dimensional clusters of infectious diseases

To indentify early signs of unusual health events is critical to early warning of infectious diseases. A new method for detecting multi-dimensional clusters of infectious diseases is presented in this paper. Ant colony clustering algorithm is applied to classify the cases of specified infectious diseases according to their crowd characters; then the cases belonging to the same class in terms of the space adjacency is separated; finally, the prior information about previous diseases outbreaks in the study area is applied to test the hypothesis that there was no disease cluster at various sub-regions. The detection ability of the method shows that this method does not need to accumulate case data within a long time period to detect irregular-shaped hot spots. It is useful for introducing spatial analysis to detection of infectious disease outbreaks.</p

Style and influencing factors of tutors-postgraduates’ interactions in Chinese medical colleges: a cross-sectional survey in Heilongjiang Province

Abstract Objectives This study assesses the style of tutor-postgraduate interactions in Chinese medical colleges and explores the association between postgraduates’ demographic factors and tutors’ demographic characteristics. Methods With the stratified sampling method, a cross-sectional online survey was used. A total of 813 medical postgraduates were recruited as participants, with an effective response rate of 85.49%. The two dimensions of “Professional Ability Interaction” and “Comprehensive Cultivation Interaction” in the self-developed “Instructor-Graduate Interaction Scale for Medical Colleges” were used as dependent variables. And tutors’ demographic characteristics and postgraduates’ demographic characteristics were taken as independent variables. Logistic regression analysis was used to explore the influencing factors of Tutor-Postgraduates Interactions in medical colleges. Results The Tutor-Postgraduates Interaction scale consists of 14 items from the two dimensions of “Professional Ability Interaction” and “Comprehensive Cultivation Interaction”. The results of the logistic regression analysis show the reasons for selecting the mentor students (industry recognition, the tutor’s research direction, charm in attracting mentors, and recommendations for mentor selection); student to mentor satisfaction; student to study life satisfaction; and regular academic seminars. Indirect guidance and a high postgraduate grade high are the protective factors of interaction between tutors and postgraduates of medical colleges and universities postgraduates. Older mentors and more graduate tutors are the risk factors for Tutor-Postgraduates Interaction in medical colleges (P < 0.05). Conclusion The current study proposes that managers should pay more attention to the double-track promotion of “Professional Ability Interaction” and “Comprehensive Cultivation Interaction”. We should not only pay attention to the cultivation of postgraduates’ professional ability but also pay more attention to the comprehensive cultivation including postgraduates’ mental and psychological aspects. The interaction between tutors and postgraduates in medical colleges is generally good, but much attention should be given to the dual-track promotion mentioned above. Regular academic seminars play an important role in the process of postgraduate training. The research findings, including the influencing factors regarding tutor-postgraduate interactions, the Professional Ability Interaction and Comprehensive Cultivation Interaction, are very informative and can contribute to strategies for postgraduate management systems that enhance this relationship