A Dataset on Population Activity Patterns in Typical Regions of North China.

This data article describes the "Typical Regional Activity Patterns" (TRAP) dataset, which is based on the Tackling Key Problems in Air Pollution Control Program. In order to explore the interaction between air pollution and physical activity, we collected activity patterns of 9,221 residents with different occupations and lifestyles for three consecutive days in typical regions (Jinan and Baoding) where air pollutant concentrations were higher than those in neighboring areas. The TRAP dataset consists of two aspects of information: demographic indicators (personal information, occupation, personal habits, and living situation) and physical activity pattern data (activity location and intensity); additionally, the exposure measures of physical activity patterns are included, which data users can match to various endpoints for their specific purpose. This dataset provides evidence for exploring the attributes of activity patterns of residents in northern China and for interdisciplinary researchers to develop strategies and measures for health education and health promotion.

Screening of Organic Small Molecule Excipients on Ternary Solid Dispersions Based on Miscibility and Hydrogen Bonding Analysis: Experiments and Molecular Simulation.

The preparation of solid dispersions by mixing insoluble drugs with polymers is the main way to improve the aqueous solubility of drugs. The introduction of organic small molecule excipients into binary solid dispersions is expected to further enhance drug solubility by regulating intermolecular hydrogen bonding within the system at the microscopic level. In this study, we used carbamazepine (CBZ) as the target drug and polyvinylpyrrolidone as the solid dispersion matrix and screened the third component from 13 organic small molecules with good miscibility in the solid dispersion based on the principle of similarity of solubility parameters. The hydrogen bonding parameters and dissociation Gibbs free energy of the 13 organic small molecule-CBZ dimer were calculated by quantum mechanical simulation, and the tryptophan (Try) was identified as the optimal third component of organic small molecule. The migration of CBZ in binary and ternary systems was also analyzed by molecular dynamics simulation. On this theoretical basis, the corresponding solid dispersions were prepared, characterized, and tested for solubility analysis, which verified that the drug solubility was stronger for the system with the addition of polar fractions and the Try was indeed the best third component of organic small molecule compound, which was consistent with the simulation predictions. This screening method may provide theoretical guidance for drug modification design and clinical studies.

Multiple-Droplet Selective Manipulation Enabled by Laser-Textured Hydrophobic Magnetism-Responsive Slanted Micropillar Arrays with an Ultrafast Reconfiguration Rate.

Biomimetic structures based on the magnetic response have attracted ever-increasing attention in droplet manipulation. Till now, most methods for droplet manipulation by a magnetic response are only applicable to a single droplet. It is still a challenge to achieve on-demand and precise control of multiple droplets (≥2). In this paper, a strategy for on-demand manipulation of multiple droplets based on magnetism-responsive slanted micropillar arrays (MSMAs) is proposed. The Glaco-modified superhydrophobic surface is the basis of multiple-droplet manipulation. The droplet's motion mode (pinned, unidirectional, and bidirectional) can be readily fine-tuned by changing the volume of droplets and the speed of the magnetic field. The rapid movement of droplets (10-80 mm/s) in the horizontal direction is realized by the unidirectional waves of the micropillar array driven by a specific magnetic field. The bending angle of micropillars can be rapidly and reversibly adjusted from 0 to 90° under the action of a magnetic field. Meanwhile, the liquid-involved light, electric switch, and biomedical detection can be designed by manipulating the droplets on demand. The superiority of MSMAs in multiple-droplet programmable manipulation opens up an avenue for applications in microfluidic and biomedical engineering.

Hydrogen Bonding Principle-Based Molecular Design of a Polymer Excipient and Impacts on Hydrophobic Drug Properties: Molecular Simulation and Experiment.

Although some commercial excipients for improving the solubility of highly crystalline drugs are widely used, they still cannot cover all types of hydrophobic drugs. In this regard, with phenytoin as the target drug, related molecular structures of polymer excipients were designed. The optimal repeating units of NiPAm and HEAm were screened out through quantum mechanical simulation and Monte Carlo simulation methods, and the copolymerization ratio was also determined. Using molecular dynamics simulation technology, it was confirmed that the dispersibility and intermolecular hydrogen bonds of phenytoin in the designed copolymer were better than those in the commercial PVP materials. At the same time, the designed copolymers and solid dispersions were also prepared during the experiment, and the improvement of their solubility was confirmed, which is in accordance with the simulation predictions. The new ideas and simulation technology may be used for drug modification and development.

Magnetic-Actuated Robot Enables High-Performance Underwater Bubble Maneuvering on Laser-Textured Biomimetic Slippery Surfaces.

Controllable underwater gas bubble (UGB) transport on a surface is realized by geography-/stimuli-induced wettability gradient force (Fwet-grad). Unfortunately, the high-speed maneuvering of UGBs along free routes on planar surfaces remains challenging. Herein, a regime of magnetism-actuated robot (MAR) mounting on biomimetic laser-ablated lubricant-impregnated slippery surfaces (LA-LISS) is reported. Leveraging on LA-LISS, MAR-entrained UGBs can move along arbitrary directions through the loading of a tracing magnetic trigger. The underlying hydrodynamics is that MAR-entrained UGBs would be actuated slipping upon a giant magnetic-induced towing force (FM//). Once the magnetism stimuli is discharged, FM// vanishes immediately to immobilize the UGBs on LA-LISS. Thanks to the MAR's robust bubble affinity, a typical UGB (20 µL) on the optimized LA-LISS can be accelerated at 500 mm/s2 and gain an ultrafast velocity of over 205 mm/s that far exceeds previously reported figures. Moreover, fundamental physics renders MAR antibuoyancy, steering locomotive UGBs on the inclined LA-LISS. Significantly, an MAR propelling UGBs to configure desirable patterns, realize on-demand coalescence, remedy the cutoff switch, as well as facilitate a programmable light-control-light optical shutter is successfully deployed. Compared with previous smart surfaces, the current multifunctional regime is more competent for harnessing UGBs featuring an unparalleled transport velocity independent of the feeble Fwet-grad.

Polyurethanes Based on Polylactic Acid for 3D Printing and Shape-Memory Applications.

Polylactic acid (PLA) has received increased attention in the development of shape-memory polymers and biomedical materials owing to its excellent physical properties and good biocompatibility and biodegradability. However, the inherent brittleness and high shape-recovery temperature of this material limit its application in the human body. Herein, we fabricated a PLA-based thermoplastic polyurethane (PLA-TPU) prepared from modified PLA-diol, dicyclohexylmethane-4,4'-diisocyanate, and 1,4-butanediol to solve the limitations of pure PLA. The glass transition temperature (Tg) of the designed TPU can be tailored from 6 to 40.5 °C by adjusting the content of hard segments or molecular weight of soft segments. The shape of the designed TPU can be fixed at room temperature and recovered at temperatures above 37 °C. Moreover, the prepared PLA-TPUs exhibited recyclability, three-dimensional printing capability, non-cytotoxicity, blood compatibility, and biodegradability. The shape of PLA-TPU/nano-Fe3O4 composites can be recovered by exposure to near-infrared light. These results collectively indicate that PLA-TPUs and their composites may have potential applications as intelligent flexible medical scaffolds for surgical and medical implantation equipment.

Bio-Based, Recyclable and Self-Healing Polyurethane Composites with High Energy Dissipation and Shape Memory.

Rubber composites make an important contribution to eliminating vibration and noise owing to their unique viscoelasticity. However, it is important to find alternative bio-based products with high damping properties owing to the shortage of petrochemical resources and poor performance. The ability to self-heal is an additional characteristic that is highly desirable because it can further increase the service life and safety of such products. In this study, a bio-based polylactic acid thermoplastic polyurethane (PLA-TPU) and its composites (PLA-TPU/AO-80) are synthesized. The reversible sacrificial hydrogen bonds in the composites increase the peak value of the loss factor (tan δmax ) from 0.87 to 2.12 with a high energy dissipation efficiency of 99% at 50% strain. After being heated for 15 min, the healed sample recovers 81.98% of its comprehensive mechanical properties due to the reorganization of the hydrogen bonds. Its tensile strength remains at 93.4% after recycling five times. Moreover, its shape memory properties show a response temperature close to the human body temperature making it an ideal candidate for medical applications.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants.

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study.

BACKGROUND: Lung cancer screening and intervention might be important to help detect lung cancer early and reduce the mortality, but little was known about lung cancer intervention strategy associated with intervention effect for preventing lung cancer. We employed Deep Q-Networks (DQN) to respond to this gap. The aim was to quantitatively predict lung cancer optimal intervention strategy and assess intervention effect in aged 65 years and older (the elderly). METHODS: We screened lung cancer high risk with web-based survey data and conducted simulative intervention. DQN models were developed to predict optimal intervention strategies to prevent lung cancer in elderly men and elderly women separately. We assessed the intervention effects to evaluate the optimal intervention strategy. RESULTS: Proposed DQN models quantitatively predicted and assessed lung cancer intervention. DQN models performed well in five stratified groups (elderly men, elderly women, men, women and the whole population). Stopping smoking and extending quitting smoking time were optimal intervention strategies in elderly men. Extending quitting time and reducing smoked cigarettes number were optimal intervention strategies in elderly women. In elderly men and women, the maximal reductions of lung cancer incidence were 31.81% and 24.62% separately. Lung cancer incidence trend was deduced from the year of 1984 to 2050, which predicted that the difference of lung cancer incidence between elderly men and women might be significantly decreased after thirty years quitting time. CONCLUSIONS: We quantitatively predicted optimal intervention strategy and assessed lung cancer intervention effect in the elderly through DQN models. Those might improve intervention effects and reasonably prevent lung cancer.

Smart Control for Water Droplets on Temperature and Force Dual-Responsive Slippery Surfaces.

Responsive slippery lubricant-infused porous surfaces (SLIPSs), featuring excellent liquid repelling/sliding capabilities in response to external stimuli, have attracted great attention in smart droplet manipulations. However, most of the reported responsive SLIPSs function under a single stimulus. Here, we report a kind of smart slippery surface capable of on-demand control between sliding and pinning for water droplets via alternately freezing/thawing the stretchable polydimethylsiloxane sheet in different strains. Diverse parameters are quantified to investigate the critical sliding volume of the droplet, including lubricant infusion amount, laser-scanning power, and pillar spacing. By virtue of the cooperation of temperature and force fields acting on the SLIPS, we demonstrate the intriguing applications including controllable chemical reaction and on-demand electrical circuit control. We envision that this dual-responsive surface should provide more possibilities in smart control of microscale droplets, especially in active vaccine-involved biochemical microreactions where a lower temperature is highly favored.

The US Public's Perception of the Threat of COVID-19 During the Rapid Spread of the COVID-19 Outbreak: Cross-Sectional Survey Study.

BACKGROUND: The rapid spread of the COVID-19 pandemic in the United States has made people uncertain about their perceptions of the threat of COVID-19 and COVID-19 response measures. To mount an effective response to this epidemic, it is necessary to understand the public's perceptions, behaviors, and attitudes. OBJECTIVE: We aimed to test the hypothesis that people's perceptions of the threat of COVID-19 influence their attitudes and behaviors. METHODS: This study used an open dataset of web-based questionnaires about COVID-19. The questionnaires were provided by Nexoid United Kingdom. We selected the results of a questionnaire on COVID-19-related behaviors, attitudes, and perceptions among the US public. The questionnaire was conducted from March 29 to April 20, 2020. A total of 24,547 people who lived in the United States took part in the survey. RESULTS: In this study, the average self-assessed probability of contracting COVID-19 was 33.2%, and 49.9% (12,244/24,547) of the respondents thought that their chances of contracting COVID-19 were less than 30%. The self-assessed probability of contracting COVID-19 among women was 1.35 times that of males. A 5% increase in perceived infection risk was significantly associated with being 1.02 times (OR 1.02, 95% CI 1.02-1.02; P<.001) more likely to report having close contact with >10 people, and being 1.01 times (OR 1.01, 95% CI 1.01-1.01; P<.001) more likely to report that cohabitants disagreed with taking steps to reduce the risk of contracting COVID-19. However, there was no significant association between participants who lived with more than 5 cohabitants or less than 5 cohabitants (P=.85). Generally, participants who lived in states with 1001-10,000 COVID-19 cases, were aged 20-40 years, were obese, smoked, drank alcohol, never used drugs, and had no underlying medical conditions were more likely to be in close contact with >10 people. Most participants (21,017/24,547, 85.6%) agreed with washing their hands and maintaining social distancing, but only 20.2% (4958/24,547) of participants often wore masks. Additionally, male participants and participants aged <20 years typically disagreed with washing their hands, maintaining social distancing, and wearing masks. CONCLUSIONS: This survey is the first attempt to describe the determinants of the US public's perception of the threat of COVID-19 on a large scale. The self-assessed probability of contracting COVID-19 differed significantly based on the respondents' genders, states of residence, ages, body mass indices, smoking habits, alcohol consumption habits, drug use habits, underlying medical conditions, environments, and behaviors. These findings can be used as references by public health policy makers and health care workers who want to identify populations that need to be educated on COVID-19 prevention and health.

COVID term: a bilingual terminology for COVID-19.

BACKGROUND: The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination. METHODS: We developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services. RESULTS: The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn ). CONCLUSIONS: COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application.

Molecular Dynamics Simulation Study on Two-Component Solubility Parameters of Carbon Nanotubes and Precisely Tailoring the Thermodynamic Compatibility between Carbon Nanotubes and Polymers.

Solubility parameters play an important role in predicting compatibility between components. The current study on solubility parameters of carbon materials (graphene, carbon nanotubes, and fullerene, etc.) is unsatisfactory and stagnant due to experimental limitations, especially the lack of a quantitative relationship between functional groups and solubility parameters. Fundamental understanding of the high-performance nanocomposites obtained by carbon material modification is scarce. Therefore, in the past, the trial and error method was often used for the modification of carbon materials, and no theory has been formed to guide the experiment. In this work, the effect of defects, size, and the number of walls on the Hildebrand solubility parameter (δT) of carbon nanotubes (CNTs) was investigated by molecular dynamics (MD) simulation. Besides, three-component Hansen solubility parameters (δD, δp, δH) were transformed into two-component solubility parameters (δvdW, δelec). The quantitative relation between functional groups and two-component solubility parameters of single-walled carbon nanotubes (SWCNTs) was then given. An important finding is that the δT and δvdW of SWCNTs first decrease, reach a minimum, and then increase with increasing grafting ratio. The thermodynamic compatibility between functionalized SWCNTs and six typical polymers was investigated by the Flory-Huggins mixing model. Two-component solubility parameters were proven to be able to effectively predict their compatibility. Importantly, we theoretically gave the optimum grafting ratio at which the compatibility between functionalized SWCNTs and polymers is the best. The functionalization principle of SWCNTs toward good compatibility between SWCNTs and polymers was also given. This study gives a new insight into the solubility parameters of functionalized SWCNTs and provides theoretical guidance for the preparation of high-performance SWCNTs/polymers composites.

Femtosecond Laser-Assisted Top-Restricted Self-Growth Re-Entrant Structures on Shape Memory Polymer for Dynamic Pressure Resistance.

Bioinspired surface material with re-entrant texture has been proven effective in exhibiting good pressure resistance to droplets with low surface tension under static conditions. In this work, we combined femtosecond laser cutting with shape memory polymer (SMP) and tape to fabricate re-entrant micropillar arrays by proposing a top-restricted self-growth (TRSG) strategy. Our proposed TRSG strategy simplifies the fabrication process and improves the processing efficiency of the re-entrant structure-based surface material. The structural parameters of the re-entrant micropillar array (microdisk diameter D, center-to-center distance P, and height H) can be adjusted through our TRSG processing method. To better characterize the anti-infiltration ability of various re-entrant micropillars, we studied the dynamic process of ethylene glycol droplet deformation by applying external vertical vibration to the surface material. Three parameters (vibration mode, amplitude, and frequency) of the external excitation and structural parameters of the re-entrant micropillar array were systemically investigated. We found that the surface material had better dynamic pressure resistance when P and D of the re-entrant texture were 650 and 500 µm, respectively, after heating for 6 min. This work provides new insights into the preparation and characterization of the surface material, which may find potential applications in microdroplet manipulation, drug testing, and biological sensors.

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data.

BACKGROUND: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. With a rapid growth of the elderly population in recent years, lung cancer prevention and control are increasingly of fundamental importance, but are complicated by the fact that the pathogenesis of lung cancer is a complex process involving a variety of risk factors. OBJECTIVE: This study aimed at identifying key risk factors of lung cancer incidence in the elderly and quantitatively analyzing these risk factors' degree of influence using a deep learning method. METHODS: Based on Web-based survey data, we integrated multidisciplinary risk factors, including behavioral risk factors, disease history factors, environmental factors, and demographic factors, and then preprocessed these integrated data. We trained deep neural network models in a stratified elderly population. We then extracted risk factors of lung cancer in the elderly and conducted quantitative analyses of the degree of influence using the deep neural network models. RESULTS: The proposed model quantitatively identified risk factors based on 235,673 adults. The proposed deep neural network models of 4 groups (age ≥65 years, women ≥65 years old, men ≥65 years old, and the whole population) achieved good performance in identifying lung cancer risk factors, with accuracy ranging from 0.927 (95% CI 0.223-0.525; P=.002) to 0.962 (95% CI 0.530-0.751; P=.002) and the area under curve ranging from 0.913 (95% CI 0.564-0.803) to 0.931(95% CI 0.499-0.593). Smoking frequency was the leading risk factor for lung cancer in men 65 years and older. Time since quitting and smoking at least 100 cigarettes in their lifetime were the main risk factors for lung cancer in women 65 years and older. Men 65 years and older had the highest lung cancer incidence among the stratified groups, particularly non-small cell lung cancer incidence. Lung cancer incidence decreased more obviously in men than in women with smoking rate decline. CONCLUSIONS: This study demonstrated a quantitative method to identify risk factors of lung cancer in the elderly. The proposed models provided intervention indicators to prevent lung cancer, especially in older men. This approach might be used as a risk factor identification tool to apply in other cancers and help physicians make decisions on cancer prevention.

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.

This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk factors of acute respiratory diseases were identified in Beijing, China. We conducted age and gender stratified deep neural network models in air pollution epidemiology. We ranked risk factors of respiratory diseases in stratified populations and conducted quantitative comparison. People ≥50 years were more sensitive to PM2.5 pollution than <50 years people, especially women ≥50 years. Compared with women, both men ≥50 years and <50 years were more susceptible to PM10. Young women <50 years were more sensitive to general air pollutants such as SO2 and NO2 than <50 years young men. Meteorological factors such as wind speed and precipitation could promote the diffusion of fine particulate matter and general air pollutants (SO2, NO2, etc.), which could help to reduce the incidence of acute respiratory diseases. This study represents a quantitative analysis of environmental health risk factors identification related to acute respiratory diseases based on deep neural network method. The results of this study could help people to improve their awareness of acute respiratory diseases prevention.

Photothermal Actuation of Diverse Liquids on an Fe3O4-Doped Slippery Surface for Electric Switching and Cell Culture.

The photoinduced manipulation of liquids on a slippery lubricant-infused porous surface (SLIPS) has attracted a tremendous amount of attention because of its merits of contactless stimulation and excellent spatial and temporal control. However, tedious fabrication methods by a combination of template transfer and fluorination for a photothermal-material-doped SLIPS and the lack of deeper systematically quantitative analysis with respect to droplet hydrokinetics are greatly perplexing in both academic research and industrial applications. Here we demonstrate a kind of Fe3O4-doped SLIPS by one-step femtosecond laser cross-scanning, which can readily steer diverse liquids toward arbitrary directions with a fast velocity of up to 1.15 mm/s in the presence of a unilateral NIR stimulus. The underlying mechanism is that the wettability gradient force (Fwet-grad) induced by the temperature gradient arising from asymmetric near-infrared-irradiation (NIR) loading would be generated within 1 s to actuate a targeted droplet's sliding behavior. Through tuning the NIR irradiating sites, we can slide a targeted droplet with controllable directions and routes. On the basis of fundamental physics, we have quantitatively analyzed the relationship among Fe3O4-doped content, lubricant rheological performance, droplet wettability variations, Fwet-grad, and the sliding velocity for diverse liquid species. Accordingly, we can remotely steer liquid droplets to realize the on-off state of an electrical circuit on demand, the droplet fusion of a microfluidic reactor, and the culture/inhibition of biological cells.

Mining Open Payments Data: Analysis of Industry Payments to Thoracic Surgeons From 2014-2016.

BACKGROUND: The financial relationship between physicians and industries has become a hotly debated issue globally. The Physician Payments Sunshine Act of the US Affordable Care Act (2010) promoted transparency of the transactions between industries and physicians by making remuneration data publicly accessible in the Open Payments Program database. Meanwhile, according to the World Health Organization, the majority of all noncommunicable disease deaths were caused by cardiovascular disease. OBJECTIVE: This study aimed to investigate the distribution of non-research and non-ownership payments made to thoracic surgeons, to explore the regularity of financial relationships between industries and thoracic surgeons. METHODS: Annual statistical data were obtained from the Open Payments Program general payment dataset from 2014-2016. We characterized the distribution of annual payments with single payment transactions greater than US $10,000, quantified the major expense categories (eg, Compensation, Consulting Fees, Travel and Lodging), and identified the 30 highest-paying industries. Moreover, we drew out the financial relations between industries to thoracic surgeons using chord diagram visualization. RESULTS: The three highest categories with single payments greater than US $10,000 were Royalty or License, Compensation, and Consulting Fees. Payments related to Royalty or License transferred from only 5.38% of industries to 0.75% of surgeons with the highest median (US $13,753, $11,992, and $10,614 respectively) in 3-year period. In contrast, payments related to Food and Beverage transferred from 93.50% of industries to 98.48% of surgeons with the lowest median (US $28, $27, and $27). The top 30 highest-paying industries made up approximately 90% of the total payments (US $21,036,972, $23,304,996, and $28,116,336). Furthermore, just under 9% of surgeons received approximately 80% of the total payments in each of the 3 years. Specifically, the 100 highest cumulative payments, accounting for 52.69% of the total, transferred from 27 (6.05%) pharmaceutical industries to 86 (1.89%) thoracic surgeons from 2014-2016; 7 surgeons received payments greater than US $1,000,000; 12 surgeons received payments greater than US $400,000. The majority (90%) of these surgeons received tremendous value from only one industry. CONCLUSIONS: There exists a great discrepancy in the distribution of payments by categories. Royalty or License Fees, Compensation, and Consulting Fees are the primary transferring channels of single large payments. The massive transfer from industries to surgeons has a strong "apical dominance" and excludability. Further research should focus on discovering the fundamental driving factors for the strong concentration of certain medical devices and how these payments will affect the industry itself.

Tunable random polymer fiber laser.

We have demonstrated the realization of on-line temperature-controlled random lasers (RLs) in the polyhedral oligomeric silsesquioxanes (POSS) nanoparticles (NPs) as well as Pyrromethene 597 (PM597) laser dye, Fe3O4/SiO2 NPs as well as PM597, and only PM597 doped polymer optical fibers (POFs), respectively. The RLs can be obtained from the gained POFs system caused by multiple scattering of emitted light. The refractive index of the fiber core materials can be easily tuned via temperature due to the polymer with large thermo-optic coefficient. Meanwhile, the scattering mean free path of core in the POFs, which is the key role for the emission wavelength of RLs, is strongly dependent on the matrix refractive index. Thus emission wavelength of RLs in the POF temperature can be controlled through changing the temperature. With the increasing the temperature, the RL emission wavelength has occurred red-shift effect for the POFs.

In-situ self-crosslinking strategy for super-tough polylactic acid/ bio-based polyurethane blends.

As a bio-based degradable plastic, polylactic acid (PLA) is highly commercialized, but its inherent brittleness limits its widespread use. In-situ polymerization techniques are effective in improving the toughness of PLA. However, the enhancement of the toughening effect in polyurethanes (PUs) through in-situ self-crosslinking still requires improvement and heavily relies on petroleum-derived feedstocks in certain approaches. In this paper, 1,3-polypropanediol (PO3G) of bio-based origin rather than conventional polyols like polyethylene glycol (PEG) and poly propylene glycol (PPG) was used. PLA/PO3G-PU blends were prepared via an in-situ self-crosslinking strategy. With a notch impact and tensile strength of 55.95 kJ/m2 and 47.77 MPa (a retention rate of 68.9 % compared with pure PLA), respectively, PLA/PO3G-PU blends achieved a better balance between stiffness and toughness. This work provides a new option for PLA to achieve a stiffness-toughness balance and get rid of dependence on petrochemical resources.