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All experimental treatments failed ( less then 10%) to induce settlement in Montipora aequituberculata, Mycedium elephantotus, and Porites cylindrica. Individual species responded heterogeneously to all treatments, suggesting that none of the cues represent a universal settlement inducer. These results challenge the commonly-held notion that CCA ubiquitously induces coral settlement, and emphasize the critical need to assess additional cues to identify natural settlement inducers for a broad range of coral taxa.An amendment to this paper has been published and can be accessed via a link at the top of the paper.The COVID-19 outbreak is becoming a public health emergency. Data are limited on the clinical characteristics and causes of death. A retrospective analysis of COVID-19 deaths were performed for patients' clinical characteristics, laboratory results, and causes of death. In total, 56 patients (72.7%) of the decedents (male-female ratio 5126, mean age 71 ± 13, mean survival time 17.4 ± 8.4 days) had comorbidities. Acute respiratory failure (ARF) and sepsis were the main causes of death. Increases in C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer and lactic acid and decreases in lymphocytes were common laboratory results. Intergroup analysis showed that (1) most female decedents had cough and diabetes. (2) The proportion of young- and middle-aged deaths was higher than elderly deaths for males, while elderly decedents were more prone to myocardial injury and elevated CRP. (3) CRP and LDH increased and cluster of differentiation (CD) 4+ and CD8+ cells decreased significantly in patients with hypertension. The majority of COVID-19 decedents are male, especially elderly people with comorbidities. The main causes of death are ARF and sepsis. Most female decedents have cough and diabetes. Myocardial injury is common in elderly decedents. Patients with hypertension are prone to an increased inflammatory index, tissue hypoxia and cellular immune injury.Dengue virus (DENV) is a mosquito-borne pathogen that is becoming a serious global threat, owing to its rising incidence in inter-tropical regions that yield over 50 million annual infections. There are currently no approved antiviral agents for the management of dengue, and recent shortcomings in its immunization called for immediate action to develop effective drugs with prophylactic ability to better manage its infection. In an attempt to discover novel antiviral sources, we identified the medicinal herb Polygonum cuspidatum (PC) as a bioactive botanical material against DENV infectivity. Specifically, the methanolic extract from PC rhizomes (PCME) potently inhibited DENV infection without causing significant cytotoxicity. Further examination on the viral life cycle demonstrated that PCME particularly targeted the initial stages of DENV infection, while pre- and post-infection treatments had no effect. More importantly, the PCME could efficiently inactivate DENV free virus particles and block the viral attachment and entry/fusion events without apparently influencing viral replication, egress, and cell-to-cell spread. The antiviral effect of PCME was also recapitulated in infection analysis using DENV pseudoparticles displaying viral structural proteins that mediate DENV particle entry. Besides, PCME treatment also inhibited direct DENV entry into several cell types relevant to its infection and reduced viral infectivity of other members of the Flaviviridae family, including the hepatitis C virus (HCV) and Zika virus (ZIKV). Due to its potency against DENV entry, we suggest that the phytobioactive extract from PC is an excellent starting point as an antiviral source material for further development of therapeutic strategies in the prophylactic management of DENV infection.Anxiety and depression are distinct-albeit overlapping-psychiatric diseases, currently diagnosed by self-reported-symptoms. check details This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive-emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.
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