This study presents a sea-level curve from w9500 to w6500 cal BP for the farfield location of Singapore, on the Sunda Shelf in southeast Asia. The curve is based on more than 50 radiocarbon dates from elevations of þ1.43 m to À15.09 m representing sea-level index points in intertidal mangrove and shallow marine sediments deposited by sea-level rise accompanying deglaciation. The results indicate that mean sea level rose rapidly from around À17 m at 9500 cal BP to around À3 m by 8000 cal BP. After this time, the data suggest (but do not unequivocally prove) that the rate of sea-rise slowed for a period of 300e500 years centred on w7700 cal BP, shortly after the cessation of meltwater input to the oceans from the northern hemisphere. Renewed sea-level rise amounting to 3e5 m began around 7400 cal BP and was complete by 7000 cal BP. The existence of an inflection in the rate of sea-level rise, with a slow-down centred on w7700 cal BP, is broadly consistent with other available sea-level curves over this interval and is supported by evidence of stable shorelines and delta initiation elsewhere at this time, as well as evidence of comparatively rapid retreat of the West Antarctic ice sheet beginning around 7500 cal BP. 'Stepped' sea-level rise occurring shortly after 7500 cal BP and also earlier during deglaciation may have served to focus significant post-glacial episodes of human maritime/coastal dispersal, into comparatively narrow time intervals.

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The statistical relationship between urban canyon height-to-width (H/W) ratios and nocturnal heat island intensities for public housing estates in Singapore has been examined. Although a number of similar studies have been conducted for temperate cities, this is a first attempt at correlating H/W to heat island intensities for a tropical city. Heat island intensities were examined specifically at 22:00 h because a previous study of Singapore's heat island determined that the heat islands were well developed by that time. A total of 17 Housing Development Board (HDB) estates were studied and at least two vehicle traverses were conducted for each estate on nights with a few days of antecedent dry weather conditions. H/W ratios for each estate were tabulated by proportion of building length. The statistical analysis demonstrates that there is a positive relationship between the heat island intensities and the median H/W, such that DT u − r(max) =0.952 (median H/W)−0.021, statistically significant at h = 0.05 with a p-value of 0.001 and a correlation coefficient of 0.53.

Background The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow. Main text A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections. Conclusion The review will undoubtedly assist in updating the scientific community’s understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.

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