Atomic Layer Deposition of CdS Quantum Dots for Solid‐State Quantum Dot Sensitized Solar Cells

Functioning quantum dot (QD) sensitized solar cells have been fabricated using the vacuum deposition technique atomic layer deposition (ALD). Utilizing the incubation period of CdS growth by ALD on TiO₂, we are able to grow QDs of adjustable size which act as sensitizers for solid‐state QD‐sensitized solar cells (ssQDSSC). The size of QDs, studied with transmission electron microscopy (TEM), varied with the number of ALD cycles from 1‐10 nm. Photovoltaic devices with the QDs were fabricated and characterized using a ssQDSSC device architecture with 2,2',7,7'‐tetrakis‐(N,N‐di‐p methoxyphenylamine) 9,9'‐spirobifluorene (spiro‐OMeTAD) as the solid‐state hole conductor. The ALD approach described here can be applied to fabrication of quantum‐confined structures for a variety of applications, including solar electricity and solar fuels. Because ALD provides the ability to deposit many materials in very high aspect ratio substrates, this work introduces a strategy by which material and optical properties of QD sensitizers may be adjusted not only by the size of the particles but also in the future by the composition.     

Ferrous Iron Induces Nrf2 Expression in Mouse Brain Astrocytes to Prevent Neurotoxicity

Free radical damage caused by ferrous iron is involved in the pathogenesis of secondary brain injury after intracerebral hemorrhage (ICH). NF‐E2‐related factor 2 (Nrf2), a major phase II gene regulator that binds to antioxidant response element, represents an important cellular cytoprotective mechanism against oxidative damage. We hypothesized that Nrf2 might protect astrocytes from damage by Fe²⁺. Therefore, we examined cytotoxicity in primary astrocytes induced by iron overload and evaluated the effects of Fe²⁺ on Nrf2 expression. The results demonstrated that 24‐h Fe²⁺ exposure exerted time‐ and concentration‐dependent cytotoxicity in astrocytes. Furthermore, Fe²⁺ exposure in astrocytes resulted in time‐ and concentration‐dependent increases in Nrf2 expression, which preceded Fe²⁺ toxicity. Nrf2‐specific siRNA further knocked down Nrf2 levels, resulting in greater Fe²⁺‐induced astrocyte cytotoxicity. These data indicate that induction of Nrf2 expression could serve as an adaptive self‐defense mechanism, although it is insufficient to completely protect primary astrocytes from Fe²⁺‐induced neurotoxicity.     

Characterization of phytoplankton assemblages in a tropical coastal environment using Kohonen self‐organizing map

This study was aimed at understanding the main abiotic environmental factors controlling the distribution patterns of abundance and composition of phytoplankton (size less than 10 μm) assemblages in the coastal waters of south‐eastern Côte d'Ivoire. Data were collected during two cruises, in January (low‐water period) and October (high‐water period) of 2014. A total of 67 species were identified and assigned to Bacillariophyceae (49%), Cyanophyceae (21%), Chlorophyceae (13%), Euglenophyceae (10%), Dinophyceae (4%) and Chrysophyceae (3%). Three biotic zones (I, IIA and IIB) were distinguishable on a Kohonen self‐organizing map after an unsupervised learning process. The diatom genera Eunotia sp., Navicula sp. and Actinoptychus senarius are significantly associated with I, IIA and IIB biotic zones, respectively. A clear seasonal cum salinity trend was apparent in phytoplankton distribution patterns. Turbidity and nitrate levels were the main abiotic factors controlling phytoplankton distribution in I, the upland tidal regions of the lagoon. In regions along the lagoon–sea continuum, phosphate and turbidity exert the most control during the low‐water season (IIA), while total dissolved solids control phytoplankton distribution during the high‐water season (IIB). These are climate‐sensitive parameters whose concentrations depend on prevailing hydroclimatic processes. Therefore, seasonality can have important consequences on phytoplankton community and inadvertently the productivity of these systems.     

Inequality as a Powerful Predictor of Infant and Maternal Mortality around the World

Background

Maternal and infant mortality are highly devastating, yet, in many cases, preventable events for a community. The human development of a country is a strong predictor of maternal and infant mortality, reflecting the importance of socioeconomic factors in determinants of health. Previous research has shown that the Human Development Index (HDI) predicts infant mortality rate (IMR) and the maternal mortality ratio (MMR). Inequality has also been shown to be associated with worse health in certain populations. The main purpose of the present study was to determine the correlation and predictive power of the Inequality Adjusted Human Development Index (IHDI) as a measure of inequality with the Infant Mortality Rate (IMR), Maternal Mortality Rate (MMR), Early Neonatal Mortality Rate (ENMR), Late Neonatal Mortality Rate (LNMR), and the Post Neonatal Mortality Rate (PNMR).

Methods and Findings

Data for the present study were downloaded from two sources: infant and maternal mortality data were downloaded from the Global Burden of Disease 2013 Cause of Death Database and the Human Development Index (HDI) and Inequality-Adjusted Human Development Index (IHDI) data were downloaded from the United Nations Development Program (UNDP). Pearson correlation coefficients were estimated, following logarithmic transformations to the data, to examine the relationship between HDI and IHDI with MMR, IMR, ENMR, LNMR, and PNMR. Steiger’s Z test for the equality of two dependent correlations was utilized in order to determine whether the HDI or IHDI was more strongly associated with the outcome variables. Lastly, we constructed OLS regression models in order to determine the predictive power of the HDI and IHDI in terms of the MMR, IMR, ENMR, LNMR, and PNMR.Maternal and infant mortality were both strongly and negatively correlated with both HDI and IHDI; however, Steiger’s Z test for the equality of two dependent correlations revealed that IHDI was more strongly correlated than HDI with MMR (Z = 4.897, p < 0.001), IMR (Z = 2.524, p = 0.012), ENMR (Z = 2.936, p = 0.003), LNMR (Z = 2.272, p = 0.023), and PNMR (Z = 2.277, p = 0.023). Furthermore, side-by-side OLS regression models revealed that, when IHDI was used as the predictor variable instead of HDI, the R ² value was 0.053 higher for MMR, 0.025 higher for IMR, 0.038 higher for ENMR, 0.029 higher for LNMR, and 0.026 higher for PNMR.

Conclusions

Even when both the HDI and the IHDI correlate with the infant and maternal mortality rates, the IHDI is a better predictor for these two health indicators. Therefore, these results add more evidence that inequality is playing an important role in determining the health status of various populations in the world and more efforts should be put into programs to fight inequality.