Enhanced drug loading capacity of polypyrrole nanowire network for confrolled drug release

For a conducting polymer (CP) based drug release system, drug loading is often accomplished by a doping process, in which drug is incorporated into polymer as dopant. Therefore, the drug loading capacity is relatively low and the range of drugs can be loaded is limited. In the present work, a polypyrrole (PPy) nanowire network is prepared by an electrochemical method and it is found that the micro- and nano- gaps among the individual nanowires of the PPy nanowire network can be used as reservoir to store drugs. Therefore, the drug loading capacity is dependent on the volume of these micro- and nano-vacancies, instead of the doping level. The range of loaded drugs also can be theoretically extended to any drugs, instead of only charged dopants. In fact, it is confirmed here that both hydrophilic and lipophilic drugs can be loaded into the micro- and nano-gaps due to the amphilicity of the PPy nanowire network. As a result, both drug loading capacity and the range of drugs can be loaded are significantly improved. After being covered with a protective PPy film, controlled drug release from the prepared system is achieved by electrical stimulation (cyclic voltammetry, CV) and the amount of drug released can be controlled by changing the scan rate of CV and the thickness of the protective PPy film.Web of Scienc

Prominence activation, optical flare, and post-flare loops on the RS Canum Venaticorum star SZ Piscium

We present the results of time-resolved high-resolution spectroscopic observations of the very active RS Canum Venaticorum (RS CVn) star SZ Piscium (SZ Psc), obtained during two consecutive observing nights on October 24 and 25, 2011. Several optical chromospheric activity indicators are analyzed using the spectral subtraction technique, which show the remarkably different behavior between two nights. Gradually blue-shifted and strengthened excess absorption features presented in the series of the subtracted spectra (especially for the H$_{\alpha}$, He I D$_{3}$ and H$_{\beta}$ lines), as a result of active stellar prominence that is rising its height along the line of our sight, was detected in the observations on October 24. This prominence activation event was probably associated with the subsequently occurred optical flare, and part of that flare decay phase was hunted in the observations on October 25. The flare was characterized by the prominent He I D$_{3}$ line emission, as well as stronger chromospheric emission in the H$_{\alpha}$, H$_{\beta}$ and other active lines. The gradual decay of flare was accompanied by an obviously developmental absorption feature in the blue wing of the H$_{\alpha}$ and other active lines, which could be explained as cool post-flare loops which projected against the bright flare background. Therefore, a series of possibly associated magnetic activity phenomena, including flare-related prominence activation, optical flare and post-flare loops, were detected during our observations

Starch-assisted synthesis of polypyrrole nanowires by a simple electrochemical approach

Starch, one of the most commonly used polysaccharides, has been adopted for the first time as morphology-directing agent to the electrochemical synthesis of polypyrrole (PPy) nanowires on various electrodes

Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

Brain‐age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.<br/

Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

Electrodeposition of Adherent Polypyrrole Film on Titanium Surface with Enhanced Anti-corrosion Performance

A method of producing extremely adhesive polypyrrole film on Titanium (Ti) substrate was investigated. The Ti substrate was chemically pretreated and then modified by polydopamine (PDA), the polypyrrole film synthesized by electrochemical method on the treated Ti substrate displayed good adhesion and enhanced anticorrosion performance. The study of the corrosion process was conducted through open circuit potential, tafel polarization and alternating current impedance test. The adhesive polypyrrole film coated titanium showed higher positive shift in corrosion potential and lower corrosion rate, indicating the great enhanced anti-corrosion performance

Influence of Financial Conditions on the Environmental Information Disclosure of Construction Firms

Corporate social responsibility (CSR) has become a crucial issue for firms seeking to achieve sustainable development. To achieve transparent CSR, firms usually publish their environmental initiatives and accomplishments - otherwise known as an environmental information disclosure (EID). There is a growing interest in the incentives behind EID and the implications for firms' market capitalization and profitability. However, existing EID studies are mainly rooted in the manufacturing industry; there is a dearth of research related to the construction industry. It is still unclear whether construction engineering firms with superior financial conditions (i.e., doing well financially) have the incentives to signal and distinguish themselves from others by making more extensive EID (i.e., doing good environmentally). Therefore, this study conducts a secondary data analysis of 60 listed construction firms. The results reveal that the financial leverage (i.e., liabilities to assets ratio) is negatively correlated with the level of EID, whereas the relationship between financial performance (i.e., return on equity) and EID is not significant. Furthermore, the ownership structure (i.e., the proportion of state-owned equity) yields a negative moderating effect on the relationship between financial leverage and EID, i.e., the ownership structure buffers firms from (rather than binds firms to) institutional pressures. This study contributes to extending the influencing mechanism of financial conditions and shedding new light on governing environmental responsibility in construction firms. </p

Synthesis of Polypyrrole/V2O5 Composite Film on the Surface of Magnesium Using a Mild Vapor Phase Polymerization (VPP) Method for Corrosion Resistance

The vapor phase polymerization (VPP) method is a conventional strategy for synthesizing conducting polymers (CPs) on the surfaces of various materials. However, the current VPP method performed on a metal surface usually requires harsh reaction conditions, such as high temperature and low vacuum. In this paper, a polypyrrole (PPy) and vanadium pentoxide (V2O5) composite film was synthesized on the surface of Mg using a mild VPP method. Here, V2O5 was used as an oxidant, and it was found that the oxidation of pyrrole (Py) vapor on the surface of V2O5, which had been previously coated on the surface of Mg, could be performed at room temperature under normal atmospheric pressure. The formation of the PPy/V2O5 composite was verified by Fourier transform infrared spectroscopy (FTIR) and energy dispersive X-ray (EDX) spectroscopy. A thermogravimetric analyzer (TGA) was used to study the thermal stability of the composite. Subsequent corrosion tests showed that the PPy/V2O5 composite film could slow down the corrosion of Mg in 3.5 wt% NaCl. It is expected that the mild VPP method may find great potential in the fields of synthesis of CPs and the corrosion protection of reactive metals