The firefly genus Luciola sensu McDermott contains 282 species that are distributed across major parts of Asia, Europe, Africa, Australia, and the Pacific islands. Due to phenotypic similarities, species identification using external morphological characters can be unreliable for this group. Consequently, decades of piecemeal taxonomic treatments have resulted in numerous erroneous and contentious classifications. Furthermore, our understanding of the group's evolutionary history is limited due to the lack of a robust phylogenetic framework that has also impeded efforts to stabilize its taxonomy. Here, we constructed molecular phylogenies of Luciola and its allies based on combined mitogenomes and Cytochrome c oxidase subunit 1 (COX1) sequences including a newly sequenced mitogenome of an unidentified taxon from Singapore. Our results showed that this taxon represents a distinct and hitherto undescribed evolutionary lineage that forms a clade with L. filiformis from Japan and L. curtithorax from China. Additionally, the Singaporean lineage can be differentiated from other congeners through several external and internal diagnostic morphological characters, and is thus described herein as a new species. Our phylogeny also strongly supported the paraphyly of Luciola with regard to L. cruciata and L. owadai, which were inferred to be more closely related to the genus Aquatica as opposed to other members of Luciola sensu stricto. The genus Hotaria was inferred as a derived clade within Luciola (sister to L. italica), supporting its status as a subgenus of Luciola instead of a distinct genus. This is the first time since 1909 that a new species of luminous firefly has been discovered in Singapore, highlighting the need for continued biodiversity research, even in small, well-studied and highly developed countries, such as Singapore.
INTRODUCTION: The risk for diabetes progression varies greatly in individuals with type 2 diabetes mellitus (T2DM). We aimed to study the clinical determinants of diabetes progression in multiethnic Asians with T2DM.
MATERIALS AND METHODS: A total of 2057 outpatients with T2DM from a secondary-level Singapore hospital were recruited for the study. Diabetes progression was defined as transition from non-insulin use to requiring sustained insulin treatment or glycated haemoglobin (HbA1c) ≥8.5% when treated with 2 or more oral hypoglycaemic medications. Multivariable logistic regression (LR) was used to study the clinical and biochemical variables that were independently associated with diabetes progression. Forward LR was then used to select variables for a parsimonious model.
RESULTS: A total of 940 participants with no insulin use or indication for insulin treatment were analysed. In 3.2 ± 0.4 (mean ± SD) years' follow-up, 163 (17%) participants experienced diabetes progression. Multivariable LR revealed that age at T2DM diagnosis (odds ratio [95% confidence interval], 0.96 [0.94-0.98]), Malay ethnicity (1.94 [1.19-3.19]), baseline HbA1c (2.22 [1.80-2.72]), body mass index (0.96 [0.92-1.00]) and number of oral glucose-lowering medications (1.87 [1.39-2.51]) were independently associated with diabetes progression. Area under receiver operating characteristic curve of the parsimonious model selected by forward LR (age at T2DM diagnosis, Malay ethnicity, HbA1c and number of glucose-lowering medication) was 0.76 (95% CI, 0.72-0.80).
CONCLUSION: Young age at T2DM diagnosis, high baseline HbA1c and Malay ethnicity are independent determinants of diabetes progression in Asians with T2DM. Further mechanistic studies are needed to elucidate the pathophysiology underpinning progressive loss of glycaemic control in patients with T2DM.
Most of arthropod biodiversity is unknown to science. Consequently, it has been unclear whether insect communities around the world are dominated by the same or different taxa. This question can be answered through standardized sampling of biodiversity followed by estimation of species diversity and community composition with DNA barcodes. Here this approach is applied to flying insects sampled by 39 Malaise traps placed in five biogeographic regions, eight countries and numerous habitats (>225,000 specimens belonging to >25,000 species in 458 families). We find that 20 insect families (10 belonging to Diptera) account for >50% of local species diversity regardless of clade age, continent, climatic region and habitat type. Consistent differences in family-level dominance explain two-thirds of variation in community composition despite massive levels of species turnover, with most species (>97%) in the top 20 families encountered at a single site only. Alarmingly, the same families that dominate insect diversity are 'dark taxa' in that they suffer from extreme taxonomic neglect, with little signs of increasing activities in recent years. Taxonomic neglect tends to increase with diversity and decrease with body size. Identifying and tackling the diversity of 'dark taxa' with scalable techniques emerge as urgent priorities in biodiversity science.
Plastic waste circularity is a priority at a global level. Sustainable development goals (SDGs) set the ways to go, and the circular economy principles underlined the 'green' strategies to be employed. However, in practice, there is still much to do, especially in developing countries, where open burning and open dumping still represent the common way of plastic waste disposal. This review aims to analyse current plastic waste circular approaches in low-middle income settings. Seven countries were selected based on the economic level and data availability from the authors, and analysed to collect and critically discuss the actions implemented at a city level. Examples of waste minimization and recycling strategies, selective collection systems and public campaigns are reported from Africa, Asia and Latin America. First, a background analysis related to physical and governance aspects of municipal solid waste management systems of the chosen settings was conducted. The assessment was focused on the treatment processes or minimization actions. Then, the applicability of the projects to achieve the SDGs was commented on. The outcomes of the research underline the need to: (1) scale up small-scale and pilot projects, (2) disseminate good practices in more low- to middle-income settings, (3) create synergies among international partners for further replications in cities. Urgent solutions to plastic waste pollution are needed. The review presented practical actions to be implemented now to boost plastic waste circularity in developing cities.
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.