Notes

Broad-spectrum antifungal activity associated with lipopeptide brevilaterin N as well as inhibition consequences in opposition to Fusarium oxysporum and Penicillium chrysogenum.
Systematic revisions of South American marsupials have contributed to our knowledge about genus and species diversity in the last decades, including studies of the most recently described genus Cryptonanus (Didelphidae), currently comprising four recognized species. Herein we provide the first phylogeny for these mouse opossums based on comprehensive sampling, including representatives from all nominal taxa, encompassing most of the geographic distribution of the genus while also extending its known range. The taxonomic status of Cryptonanus species was explored by analyses of multiple mitochondrial and nuclear DNA markers to assess phylogenetic relationships and to provide divergence time estimates, species delimitations and biogeographical hypotheses. Cryptonanus monophyly remained highly supported despite the inclusion of abundant new data from more than a hundred specimens, comprising 10 independent evolutionary lineages. Species-complexes within valid nominal taxa reveal higher species richness in the genus. Based on divergence estimates from a dated phylogeny, we suggest that Cryptonanus diversified along the Quaternary, with speciation events occurring well into the Pleistocene. The best supported biogeographical hypothesis endorses speciation by vicariance and subset speciation across open formations in shaping the evolutionary history of this didelphid genus, strongly associated with dry tropical landscapes of South America.Modern genetic data sets present unprecedented opportunities to understand the evolutionary origins of diverse taxonomic groups. When the timing of key events is known, it is possible to investigate biogeographic history in the context of major phenomena (e.g., cooling of a major ocean). In this study, we investigated the biogeographic history of the suborder Zoarcoidei, a globally distributed fish group that includes species inhabiting both poles that produce antifreeze proteins to survive chronic subfreezing temperatures. We first generated a multi-locus, time-calibrated phylogeny for the group. We then used biogeographic modeling to reconstruct ancestral ranges across the tree and to quantify the type and frequency of biogeographic events (e.g., founder, dispersal). With these results, we considered how the cooling of the Southern and Arctic Oceans, which reached their present-day subfreezing temperatures 10-15 million years ago (Mya) and 2-3 Mya, respectively, may have shaped the group's evolutionary history, with an emphasis on the most speciose and widely distributed family, eelpouts (family Zoarcidae). Our phylogenetic results clarified the Zoarcoidei taxonomy and showed that the group began to diversify in the Oligocene ~31-32 Mya, with the center of origin for all families in north temperate waters. Within-area speciation was the most common biogeographic event in the group's history (80% of all events) followed by dispersal (20%). Finally, we only found evidence, albeit limited, for ocean cooling underpinning diversification of eelpouts living in the high Antarctic over the last 10 million years.More than 130,000 peer-reviewed studies have been published within one year after COVID-19 emerged in many countries. This large and rapidly growing field may overwhelm the synthesizing abilities of both researchers and policy-makers. To provide a sinopsis, prevent errors, and detect cognitive gaps that may require interdisciplinary research methods, the literature on COVID-19 is summarized, twice. The overall purpose of this study is to generate a dialogue meant to explain the genesis of and/or find remedies for omissions and contradictions. The first review starts in Biology and ends in Policy. Policy is chosen as a destination because it is the setting where cognitive integration must occur. The second review follows the opposite path it begins with stated policies on COVID-19 and then their assumptions and disciplinary relationships are identified. The purpose of this interdisciplinary method on methods is to yield a relational and explanatory view of the field -one strategy likely to be incomplete but usowledge; and (9) new educational programs that utilize visual aids and audience-specific communication strategies. 6Benzylaminopurine The analysis indicates that, to optimally address these problems, disciplinary and social integration is needed. By asking what is/are the potential cause(s) and consequence(s) of each issue, this methodology generates visualizations that reveal possible relationships as well as omissions and contradictions. While inherently limited in scope and likely to become obsolete, these shortcomings are avoided when this 'method on methods' is frequently practiced. Open-ended, inter-/trans-disciplinary perspectives and broad social participation may help researchers and citizens to construct, de-construct, and re-construct COVID-19 related research.Automatic medical image segmentation plays an important role as a diagnostic aid in the identification of diseases and their treatment in clinical settings. Recently proposed methods based on Convolutional Neural Networks (CNNs) have demonstrated their potential in image processing tasks, including some medical image analysis tasks. Those methods can learn various feature representations with numerous weight-shared convolutional kernels, however, the missed diagnosis rate of regions of interest (ROIs) is still high in medical image segmentation. Two crucial factors behind this shortcoming, which have been overlooked, are small ROIs from medical images and the limited context information from the existing network models. In order to reduce the missed diagnosis rate of ROIs from medical images, we propose a new segmentation framework which enhances the representative capability of small ROIs (particularly in deep layers) and explicitly learns global contextual dependencies in multi-scale feature spaces. In particular, the local features and their global dependencies from each feature space are adaptively aggregated based on both the spatial and the channel dimensions. Moreover, some visualization comparisons of the learned features from our framework further boost neural networks' interpretability. Experimental results show that, in comparison to some popular medical image segmentation and general image segmentation methods, our proposed framework achieves the state-of-the-art performance on the liver tumor segmentation task with 91.18% Sensitivity, the COVID-19 lung infection segmentation task with 75.73% Sensitivity and the retinal vessel detection task with 82.68% Sensitivity. Moreover, it is possible to integrate (parts of) the proposed framework into most of the recently proposed Fully CNN-based models, in order to improve their effectiveness in medical image segmentation tasks.
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