Domestic water pricing with household surveys : a study of acceptability and willingness to pay in Chongqing, China

In determining domestic water prices, policy makers often need to use information about the demand side rather than only relying on information about the supply side. Household surveys have frequently been employed to collect demand-side information. This paper presents a multiple bounded discrete choice household survey model. It discusses how the model can be utilized to collect and analyze information about the acceptability of different water prices by different types of households, as well as households'willingness to pay for water service improvement. The results obtained from these surveys can be directly utilized in the development of water pricing and subsidy policies. The paper also presents an empirical multiple bounded discrete choice study conducted in Chongqing, China. In this case, domestic water service quality was seriously inadequate, but financial resources were insufficient to improve service quality. With a survey of about 1,500 households in five suburban districts in Chongqing Municipality, this study shows that a significant increase in the water price is feasible as long as the poorest households can be properly subsidized and certain public awareness and accountability campaigns can be conducted to make the price increase more acceptable to the public. The analysis also indicates that the order in which hypothetical prices are presented to respondents systematically affects their answers, and should be taken into account when designing survey instruments.Town Water Supply and Sanitation,Water Supply and Sanitation Governance and Institutions,Environmental Economics&Policies,Water and Industry,Water Supply and Systems

Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity

Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms, such as Twitter, and endogenous responses within the content platform, such as YouTube. We develop a novel mathematical model, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion. Our model supplies a prototypical description of videos, called an endo-exo map. This map explains popularity as the result of an extrinsic factor -- the amount of promotions from the outside world that the video receives, acting upon two intrinsic factors -- sensitivity to promotion, and inherent virality. We use this model to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos, and found it to lower the average error by 28.6% from approaches based on popularity history. Finally, we can identify videos that have a high potential to become viral, as well as those for which promotions will have hardly any effect.This material is based on research sponsored by the Air Force Research Laboratory, under agreement number FA2386-15-1-4018

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency

Tree diversity depending on environmental gradients promotes biomass stability via species asynchrony in China's forest ecosystems

There is mounting evidence that biodiversity promotes ecological stability in changing environments. However, understanding diversity–stability relationships and their underlying mechanisms across large-scale tree diversity and natural environmental gradients are still controversial and largely lacking. We used thirty-nine 0.12 ha long-term permanent forest plots spanning China's various forest types to test the effects of multiple abiotic (climate, soil, age and topography) and biotic factors (taxonomic and structural diversity, functional diversity and community-mean traits, and species asynchrony) on biomass stability and its components (mean biomass and biomass variability) over time. We used multiple analytical methods to identify the best explanatory variables and complicated causal relationships for community biomass stability. Our results showed that species richness increased biomass stability by promoting species asynchrony. Structural and functional diversity had a weaker effect on biomass stability. Forest age and structural diversity increased mean biomass and biomass variability significantly and simultaneously. Communities dominated by tree species with high wood density had high biomass stability. Soil nitrogen enhanced biomass stability directly and indirectly through its effects on mean biomass. Soil nitrogen to phosphorus ratio increased biomass stability via increasing species asynchrony. Precipitation indirectly increased biomass stability by affecting tree diversity. Moreover, the direct and indirect effects of soil nutrients on biomass stability were greater than that of climate variables. Our results suggest that species asynchrony is the main mechanism proposed to explain the stabilizing effect of diversity on community biomass, supporting two mechanisms, namely, the biodiversity insurance hypothesis and complementary dynamics. Soil and climate factors also play an important role in shaping diversity–stability relationships. Our results provide a new insight into how tree diversity affects ecosystem stability across diverse community types and large-scale environmental gradients

PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach

In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule’s aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper

siRNA Screening of a Targeted Library of DNA Repair Factors in HIV Infection Reveals a Role for Base Excision Repair in HIV Integration

Host DNA repair enzymes have long been assumed to play a role in HIV replication, and many different DNA repair factors have been associated with HIV. In order to identify DNA repair pathways required for HIV infection, we conducted a targeted siRNA screen using 232 siRNA pools for genes associated with DNA repair. Mapping the genes targeted by effective siRNA pools to well-defined DNA repair pathways revealed that many of the siRNAs targeting enzymes associated with the short patch base excision repair (BER) pathway reduced HIV infection. For six siRNA pools targeting BER enzymes, the negative effect of mRNA knockdown was rescued by expression of the corresponding cDNA, validating the importance of the gene in HIV replication. Additionally, mouse embryo fibroblasts (MEFs) lacking expression of specific BER enzymes had decreased transduction by HIV-based retroviral vectors. Examining the role BER enzymes play in HIV infection suggests a role for the BER pathway in HIV integration