Antimicrobial peptides as potential anti-tubercular leads: A concise review

Despite being considered a public health emergency for the last 25 years, tuberculosis (TB) is still one of the deadliest infectious diseases, responsible for over a million deaths every year. The length and toxicity of available treatments and the increasing emergence of multidrugresistant strains of Mycobacterium tuberculosis renders standard regimens increasingly inefficient and emphasizes the urgency to develop new approaches that are not only cost-and time-effective but also less toxic. Antimicrobial peptides (AMP) are small cationic and amphipathic molecules that play a vital role in the host immune system by acting as a first barrier against invading pathogens. The broad spectrum of properties that peptides possess make them one of the best possible alternatives for a new “post-antibiotic” era. In this context, research into AMP as potential anti-tubercular agents has been driven by the increasing danger revolving around the emergence of extremely-resistant strains, the innate resistance that mycobacteria possess and the low compliance of patients towards the toxic anti-TB treatments. In this review, we will focus on AMP from various sources, such as animal, non-animal and synthetic, with reported inhibitory activity towards Mycobacterium tuberculosis.This research was funded by Fundação para a Ciência e Tecnologia (FCT), Portugal, through projects UIDB/50006/2020, and PTDC/BTM-SAL/29786/2017

Genome variations: Effects on the robustness of neuroevolved control for swarm robotics systems

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary setup with GV, a slight mutation is applied to the evolving neural network parameters before they are copied to the robots in a swarm. The genome variation is individual to each robot, thereby generating a slightly heterogeneous swarm. GV represents a novel approach to the evolution of robust behaviors, expected to generate more stable and robust individual controllers, and bene t swarm behaviors that can deal with small heterogeneities in the behavior of other members in the swarm. We conduct experiments using an aggregation task, and compare the evolved solutions to solutions evolved under ideal, noise-free conditions, and to solutions evolved with traditional sensor noise.info:eu-repo/semantics/acceptedVersio

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. / Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. / Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. / Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America

First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA.

The NOvA experiment has seen a 4.4σ signal of ν[over ¯]_{e} appearance in a 2&nbsp;GeV ν[over ¯]_{μ} beam at a distance of 810&nbsp;km. Using 12.33×10^{20} protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 ν[over ¯]_{μ}→ν[over ¯]_{e} candidates with a background of 10.3 and 102 ν[over ¯]_{μ}→ν[over ¯]_{μ} candidates. This new antineutrino data are combined with neutrino data to measure the parameters |Δm_{32}^{2}|=2.48_{-0.06}^{+0.11}×10^{-3}  eV^{2}/c^{4} and sin^{2}θ_{23} in the ranges from (0.53-0.60) and (0.45-0.48) in the normal neutrino mass hierarchy. The data exclude most values near δ_{CP}=π/2 for the inverted mass hierarchy by more than 3σ and favor the normal neutrino mass hierarchy by 1.9σ and θ_{23} values in the upper octant by 1.6σ

Observation of seasonal variation of atmospheric multiple-muon events in the NOvA Near Detector

Using two years of data from the NOvA Near Detector at Fermilab, we report a seasonal variation of cosmic ray induced multiple-muon (Nμ≥2) event rates which has an opposite phase to the seasonal variation in the atmospheric temperature. The strength of the seasonal multiple-muon variation is shown to increase as a function of the muon multiplicity. However, no significant dependence of the strength of the seasonal variation of the multiple-muon variation is seen as a function of the muon zenith angle, or the spatial or angular separation between the correlated muons

Expression of MuRF1 or MuRF2 is essential for the induction of skeletal muscle atrophy and dysfunction in a murine pulmonary hypertension model

Background Pulmonary hypertension leads to right ventricular heart failure and ultimately to cardiac cachexia. Cardiac cachexia induces skeletal muscles atrophy and contractile dysfunction. MAFbx and MuRF1 are two key proteins that have been implicated in chronic muscle atrophy of several wasting states. Methods Monocrotaline (MCT) was injected over eight weeks into mice to establish pulmonary hypertension as a murine model for cardiac cachexia. The effects on skeletal muscle atrophy, myofiber force, and selected muscle proteins were evaluated in wild-type (WT), MuRF1, and MuRF2-KO mice by determining muscle weights, in vitro muscle force and enzyme activities in soleus and tibialis anterior (TA) muscle. Results In WT, MCT treatment induced wasting of soleus and TA mass, loss of myofiber force, and depletion of citrate synthase (CS), creatine kinase (CK), and malate dehydrogenase (MDH) (all key metabolic enzymes). This suggests that the murine MCT model is useful to mimic peripheral myopathies as found in human cardiac cachexia. In MuRF1 and MuRF2-KO mice, soleus and TA muscles were protected from atrophy, contractile dysfunction, while metabolic enzymes were not lowered in MuRF1 or MuRF2-KO mice. Furthermore, MuRF2 expression was lower in MuRF1KO mice when compared to C57BL/6 mice. Conclusions In addition to MuRF1, inactivation of MuRF2 also provides a potent protection from peripheral myopathy in cardiac cachexia. The protection of metabolic enzymes in both MuRF1KO and MuRF2KO mice as well as the dependence of MuRF2 expression on MuRF1 suggests intimate relationships between MuRF1 and MuRF2 during muscle atrophy signaling