We find that increasing the rate of self-generated information may have either a monotonic or non-monotonic effect on the rumor spread time, depending on the network structure and rate of non-self-generated internal communications. Then, taking an analytical approach, we analyze the spread behavior for cliques, and identify the conditions for monotonic behavior in a 2-clique network.Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis. Technology can be used to best mobilize resources such as food and water in the event of a natural disaster, by semi-automatically flagging tweets and short messages as indicating an urgent need. The problem is challenging not just because of the sparseness of data in the immediate aftermath of a disaster, but because of the varying characteristics of disasters in developing countries (making it difficult to train just one system) and the noise and quirks in social media. In this paper, we present a robust, low-supervision social media urgency system that adapts to arbitrary crises by leveraging both labeled and unlabeled data in an ensemble setting. The system is also able to adapt to new crises where an unlabeled background corpus may not be available yet by utilizing a simple and effective transfer learning methodology. Experimentally, our transfer learning and low-supervision approaches are found to outperform viable baselines with high significance on myriad disaster datasets.The outbreak of Covid-19 disease caused by SARS-CoV-19, along with the lack of targeted medicaments and vaccines, forced the scientific world to search for new antiviral formulations. In this review, we describe the current knowledge about plant extracts containing polyphenols that inhibit Covid-19. Many plant-derived natural compounds (polyphenols) might provide a starting point for the research on the use of plant extracts in coronavirus treatment and prevention. Antivirus polyphenolic drugs can inhibit coronavirus enzymes, which are essential for virus replication and infection. This group of natural substances (betulinic acid, indigo, aloeemodine, luteolin, and quinomethyl triterpenoids, quercitin or gallates) is a potential key to designing antiviral therapies for inhibiting viral proteases. The known pharmacophore structures of bioactive substances can be useful in the elaboration of new anti-Covid-19 formulations. The benefit of using preparations containing phytochemicals is their high safety for patients and no side effects.Human milk oligosaccharides (HMO) are complex sugars which are found in breast milk at significant concentrations and with unique structural diversity. These sugars are the fourth most abundant component of human milk after water, lipids, and lactose and yet provide no direct nutritional value to the infant. Recent research has highlighted that HMOs have various functional roles to play in infant development. These sugars act as prebiotics by promoting growth of beneficial intestinal bacteria thereby generating short-chain fatty acids which are critical for gut health. HMOs also directly modulate host-epithelial immune responses and can selectively reduce binding of pathogenic bacteria and viruses to the gut epithelium preventing the emergence of a disease. This review covers current knowledge related to the functional biology of HMOs and their associated impact on infant gut health.
  The aim of the present study was to investigate the cross-sectional association between physical activity levels with depressive symptoms, anxiety symptoms, and positive mental well-being in a sample of the UK public social distancing owing to COVID-19.
 
  This paper presents pre-planned interim analyses of data from a cross-sectional epidemiological study. Levels of physical activity during COVID-I9 social distancing were self-reported.  https://www.selleckchem.com/products/BIBF1120.html  Mental health was measured using the Beck Anxiety and Depression Inventory. Mental wellbeing was measured using The Short Warwick-Edinburgh Mental Well-being Scale. Participants also reported on sociodemographic and clinical data. The association between physical activity and mental health was studied using regression models.
 
  902 adults were included in this study (63.8% of women and 50.1% of people aged 35-64 years). After adjusting for covariates, there was a negative association between moderate-to-vigorous physical activity per day in hours and poor mental health (OR=0.88, 95% CI=0.80-0.97). Similar findings were obtained for moderate-to-severe anxiety symptoms, moderate-to-severe depressive symptoms and poor mental wellbeing.
 
  In the present sample of UK adults social distancing owing to COVID-19 those who were physically active have better overall mental health. Owing, to the cross-sectional design of the present study the direction of the association cannot be inferred.
 In the present sample of UK adults social distancing owing to COVID-19 those who were physically active have better overall mental health. Owing, to the cross-sectional design of the present study the direction of the association cannot be inferred.Long-term effects of Covid-19 disease are still poorly understood. However, similarities between the responses to SARS-CoV-2 and certain nanomaterials suggest fibrotic pulmonary disease as a concern for public health in the next future. Cross-talk between nanotoxicology and other relevant disciplines can help us to deploy more effective Covid-19 therapies and management strategies.The world has been facing the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time COVID-19 detection and monitoring system. The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR.