The Role of Temperature Inversions in the Generation of Seasonal and Interannual SST Variability in the Far Northern Bay of Bengal

The northern Bay of Bengal is characterized by freshwater supply from the Ganges and Brahmaputra Rivers. The resulting shallow haline stratification and thick barrier layer lead to temperature inversions in fall and winter, that is, cool surface water overlaying warm subsurface water. This study examines sea surface temperature (SST) variability off Bangladesh and shows that temperature inversions play an essential role in generating seasonal and interannual SST variability there. Two satellite SST datasets reveal that the magnitude of SST variability has a local peak near the coast of Bangladesh on seasonal and interannual time scales. Output from a high-resolution ocean general circulation model, which is validated by satellite SST and Argo float observations, is used to calculate the mixed layer heat budget. Results show that inverted temperature profiles lead to SST warming on the seasonal time scale via heat exchange at the bottom of the mixed layer, which balances climatological atmospheric cooling in fall and winter. On interannual time scales, surface heat flux tends to damp SST variability, whereas heat exchange at the base of the mixed layer contributes to the growth of SST anomalies. SST off Bangladesh tends to be anomalously high in the year after an El Nino event and in the year of negative Indian Ocean dipole and La Nina events. The atmospheric circulations related to these climate modes force anomalous Ekman pumping, which advects more subsurface warm water to the surface in fall and winter, resulting in anomalous mixed layer warming. The deepening of the mixed layer entrains more subsurface warm water, which also contributes to anomalous warming

An overview of geospatial methods used in unintentional injury epidemiology

BACKGROUND: Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. METHODS: Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. RESULTS: From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). CONCLUSION: The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches

Reduction-Triggered Self-Cross-Linked Hyperbranched Polyglycerol Nanogels for Intracellular Delivery of Drugs and Proteins

Owing to the unique advantages of combining the characteristics of hydrogels and nanoparticles, nanogels are actively investigated as a promising platform for advanced biomedical applications. In this work, a self-cross-linked hyperbranched polyglycerol nanogel is synthesized using the thiol-disulfide exchange reaction based on a novel disulfide-containing polymer. A series of structural analyses confirm the tunable size and cross-linking density depending on the type of polymer (homo- or copolymer) and the amount of reducing agent, dithiothreitol, used in the preparation of the nanogels. The nanogels retain not only small molecular therapeutics irrespective of hydrophilic and hydrophobic nature but also large enzymes such as beta-galactosidase by exploiting the self-cross-linking chemistry. Their superior biocompatibility together with the controllable release of active therapeutic agents suggests the applicability of nanogels in smart drug delivery systems