A frequent observation in plant-animal mutualistic networks is that abundant species tend to be more generalised, interacting with a broader range of interaction partners than rare species. Uncovering the causal relationship between abundance and generalisation has been hindered by a chicken-and-egg dilemma: is generalisation a by-product of being abundant, or does high abundance result from generalisation? Here, we analyse a database of plant-pollinator and plant-seed disperser networks, and provide strong evidence that the causal link between abundance and generalisation is uni-directional. Specifically, species appear to be generalists because they are more abundant, but the converse, that is that species become more abundant because they are generalists, is not supported by our analysis. Furthermore, null model analyses suggest that abundant species interact with many other species simply because they are more likely to encounter potential interaction partners.

Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor. Methods:We propose to use Residual Blocks with a 3⨯3 kernel size for local feature extraction, and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps. Results:We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1,018 computed tomography (CT) scans. We followed a rigorous procedure for experimental setup namely, 10-fold crossvalidation and ignored the nodules that had been annotated by less than 3 radiologists. The proposed method achieved state-of-the-art results with AUC=95.62%, while significantly outperforming other baseline methods. Conclusions:Our proposed Deep Local-Global network has the capability to accurately extract both local and global features. Our new method outperforms state-of-the-art architecture including Densenet and Resnet with transfer learning.

Coronavirus disease (COVID-19) is a global health crisis caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard test for diagnosing COVID-19. Although it is highly accurate, this lab test requires highly-trained personnel and the turn-around time is long. Rapid and inexpensive immuno-diagnostic tests (antigen or antibody test) are available, but these point of care (POC) tests are not as accurate as the RT-PCR test. Biosensors are promising alternatives to these rapid POC tests. Here we review three types of recently developed biosensors for SARS-CoV-2 detection: surface plasmon resonance (SPR)-based, electrochemical and field-effect transistor (FET)-based biosensors. We explain the sensing principles and discuss the advantages and limitations of these sensors. The accuracies of these sensors need to be improved before they could be translated into POC devices for commercial use. We suggest potential biorecognition elements with highly selective target-analyte binding that could be explored to increase the true negative detection rate. To increase the true positive detection rate, we suggest two-dimensional materials and nanomaterials that could be used to modify the sensor surface to increase the sensitivity of the sensor.

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Th e fl uctuations of natural populations have deep impact on important ecological issues, such as pest outbreaks, fi sheries or the formation of harmful algal blooms (HABs). However, consensus on the appropriate descriptor of such fl uctuations is still lacking. Here, using 16 to 20 years of weekly data on marine microbial population abundance comprising more than 200 species, we analysed the distribution of the population fl uctuations. We found that population fl uctuations of all groups and in 12 out of 17 species were not Gaussian (D'Agostino test p Ͻ 0.05) but instead were adequately described by Levy-stable distributions (LSD). Consistent with ecological theories, the LSD characteristic parameter ( α ) characterizes as highly volatile those groups known to form HABs, such as dinofl agellates ( α ϭ 1.48), and as lowly volatile, nanofl agellates ( α ϭ 1.92), a group which can be subjected to predatory control. Moreover, zooplankton groups composed of species with sexual reproduction and complex life cycles such as crustacean and Appendicullaria also showed departures from Gaussian population fl uctuations and adequate fi ts to LSD. Our results suggest that heavy-tailed population fl uctuations are widespread, implying that extreme population fl uctuations are more likely than previously expected, a fact that has important consequences for the predictability of population outbreaks.