Rural-urban differences of neonatal mortality in a poorly developed province of China

<p>Abstract</p> <p>Background</p> <p>The influence of rural-urban disparities in children's health on neonatal death in disadvantaged areas of China is poorly understood. In this study of rural and urban populations in Gansu province, a disadvantaged province of China, we describe the characteristics and mortality of newborn infants and evaluated rural-urban differences of neonatal death.</p> <p>Methods</p> <p>We analyzed all neonatal deaths in the data from the Surveillance System of Child Death in Gansu Province, China from 2004 to 2009. We calculated all-cause neonatal mortality rates (NMR) and cause-specific death rates for infants born to rural or urban mothers during 2004-09. Rural-urban classifications were determined based on the residence registry system of China. Chi-square tests were used to compare differences of infant characteristics and cause-specific deaths by rural-urban maternal residence.</p> <p>Results</p> <p>Overall, NMR fell in both rural and urban populations during 2004-09. Average NMR for rural and urban populations was 17.8 and 7.5 per 1000 live births, respectively. For both rural and urban newborn infants, the four leading causes of death were birth asphyxia, preterm or low birth weight, congenital malformation, and pneumonia. Each cause-specific death rate was higher in rural infants than in urban infants. More rural than urban neonates died out of hospital or did not receive medical care before death.</p> <p>Conclusions</p> <p>Neonatal mortality declined dramatically both in urban and rural groups in Gansu province during 2004-09. However, profound disparities persisted between rural and urban populations. Strategies that address inequalities of accessibility and quality of health care are necessary to improve neonatal health in rural settings in China.</p

Silver nanowire-based transparent, flexible, and conductive thin film

The fabrication of transparent, conductive, and uniform silver nanowire films using the scalable rod-coating technique is described in this study. Properties of the transparent conductive thin films are investigated, as well as the approaches to improve the performance of transparent silver nanowire electrodes. It is found that silver nanowires are oxidized during the coating process. Incubation in hydrogen chloride (HCl) vapor can eliminate oxidized surface, and consequently, reduce largely the resistivity of silver nanowire thin films. After HCl treatment, 175 Ω/sq and approximately 75% transmittance are achieved. The sheet resistivity drops remarkably with the rise of the film thickness or with the decrease of transparency. The thin film electrodes also demonstrated excellent flexible stability, showing < 2% resistance change after over 100 bending cycles

Impact of phosphorus application on drought resistant responses of Eucalyptus grandis seedlings

Eucalyptus grandis is the most widely planted tree species worldwide and can face severe drought during the initial months after planting because the root system is developing. A complete randomized design was used to study the effects of two water regimes (well-watered and water-stressed) and phosphorus (P) applications (with and without P) on the morphological and physio-biochemical responses of E. grandis. Drought had negative effects on the growth and metabolism of E. grandis, as indicated by changes in morphological traits, decreased net photosynthetic rates (Pn), pigment concentrations, leaf relative water contents (LRWCs), nitrogenous compounds, over-production of reactive oxygen species (ROS) and higher lipid peroxidation. However, E. grandis showed effective drought tolerance strategies, such as reduced leaf area and transpiration rate (E), higher accumulation of soluble sugars and proline and a strong antioxidative enzyme system. P fertilization had positive effects on well-watered seedlings due to improved growth and photosynthesis, which indicated the high P requirements during the initial E. grandis growth stage. In drought-stressed seedlings, P application had no effects on the morphological traits, but it significantly improved the LRWC, Pn, quantum efficiency of photosystem II (Fv/Fm), chlorophyll pigments, nitrogenous compounds and reduced lipid peroxidation. P fertilization improved E. grandis seedling growth under well-watered conditions but also ameliorated some leaf physiological traits under drought conditions. The effects of P fertilization are mainly due to the enhancement of plant N nutrition. Therefore, P can be used as a fertilizer to improve growth and production in the face of future climate change.Fil: Tariq, Akash. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Pan, Kaiwen. Chinese Academy of Sciences; República de ChinaFil: Olatunji, Olusanya A. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Graciano, Corina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Fisiología Vegetal. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Instituto de Fisiología Vegetal; ArgentinaFil: Li, Zilong. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Li, Ningning. Southwest University; ChinaFil: Song, Dagang. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Sun, Feng. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Wu, Xiaogang. Chinese Academy of Sciences; República de ChinaFil: Dakhil, Mohammed A.. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; China. Helwan University; EgiptoFil: Sun, Xiaoming. Chinese Academy of Sciences; República de ChinaFil: Zhang, Lin. Chinese Academy of Sciences; República de Chin

A pivotal role for starch in the reconfiguration of 14C-partitioning and allocation in Arabidopsis thaliana under short-term abiotic stress.

Plant carbon status is optimized for normal growth but is affected by abiotic stress. Here, we used 14C-labeling to provide the first holistic picture of carbon use changes during short-term osmotic, salinity, and cold stress in Arabidopsis thaliana. This could inform on the early mechanisms plants use to survive adverse environment, which is important for efficient agricultural production. We found that carbon allocation from source to sinks, and partitioning into major metabolite pools in the source leaf, sink leaves and roots showed both conserved and divergent responses to the stresses examined. Carbohydrates changed under all abiotic stresses applied; plants re-partitioned 14C to maintain sugar levels under stress, primarily by reducing 14C into the storage compounds in the source leaf, and decreasing 14C into the pools used for growth processes in the roots. Salinity and cold increased 14C-flux into protein, but as the stress progressed, protein degradation increased to produce amino acids, presumably for osmoprotection. Our work also emphasized that stress regulated the carbon channeled into starch, and its metabolic turnover. These stress-induced changes in starch metabolism and sugar export in the source were partly accompanied by transcriptional alteration in the T6P/SnRK1 regulatory pathway that are normally activated by carbon starvation

Characterizing Spatiotemporal Dynamics of Methane Emissions from Rice Paddies in Northeast China from 1990 to 2010

BACKGROUND: Rice paddies have been identified as major methane (CH(4)) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH₄ emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels. METHODOLOGY/PRINCIPAL FINDINGS: Integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH(4) emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH₄ fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH₄ emission in the cool paddies. CONCLUSIONS/SIGNIFICANCE: The study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory

Construction of Vascular Tissues with Macro-Porous Nano-Fibrous Scaffolds and Smooth Muscle Cells Enriched from Differentiated Embryonic Stem Cells

Vascular smooth muscle cells (SMCs) have been broadly used for constructing tissue-engineered blood vessels. However, the availability of mature SMCs from donors or patients is very limited. Derivation of SMCs by differentiating embryonic stem cells (ESCs) has been reported, but not widely utilized in vascular tissue engineering due to low induction efficiency and, hence, low SMC purity. To address these problems, SMCs were enriched from retinoic acid induced mouse ESCs with LacZ genetic labeling under the control of SM22α promoter as the positive sorting marker in the present study. The sorted SMCs were characterized and then cultured on three-dimensional macro-porous nano-fibrous scaffolds in vitro or implanted subcutaneously into nude mice after being seeded on the scaffolds. Our data showed that the LacZ staining, which reflected the corresponding SMC marker SM22α expression level, was efficient as a positive selection marker to dramatically enrich SMCs and eliminate other cell types. After the sorted cells were seeded into the three-dimensional nano-fibrous scaffolds, continuous retinoic acid treatment further enhanced the SMC marker gene expression level while inhibited pluripotent maker gene expression level during the in vitro culture. Meanwhile, after being implanted subcutaneously into nude mice, the implanted cells maintained the positive LacZ staining within the constructs and no teratoma formation was observed. In conclusion, our results demonstrated the potential of SMCs derived from ESCs as a promising cell source for therapeutic vascular tissue engineering and disease model applications

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

One-Pot Green Synthesis and Bioapplication ofl-Arginine-Capped Superparamagnetic Fe3O4 Nanoparticles

Water-solublel-arginine-capped Fe3O4 nanoparticles were synthesized using a one-pot and green method. Nontoxic, renewable and inexpensive reagents including FeCl3,l-arginine, glycerol and water were chosen as raw materials. Fe3O4 nanoparticles show different dispersive states in acidic and alkaline solutions for the two distinct forms of surface bindingl-arginine. Powder X-ray diffraction and X-ray photoelectron spectroscopy were used to identify the structure of Fe3O4 nanocrystals. The products behave like superparamagnetism at room temperature with saturation magnetization of 49.9 emu g−1 and negligible remanence or coercivity. In the presence of 1-ethyl-3-(dimethylaminopropyl) carbodiimide hydrochloride, the anti-chloramphenicol monoclonal antibodies were connected to thel-arginine-capped magnetite nanoparticles. The as-prepared conjugates could be used in immunomagnetic assay

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches