Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction between NS1 and Cellular MAVS Contributes to NS1 Mitochondria Targeting

Influenza A virus nonstructural protein 1 (NS1) plays an important role in evading host innate immunity. NS1 inhibits interferon (IFN) responses via multiple mechanisms, including sequestering dsRNA and suppressing retinoic acid-inducible gene I (RIG-I) signaling by interacting with RIG-I and tripartite motif-containing protein 25 (TRIM25). In the current study, we demonstrated the mitochondrial localization of NS1 at the early stage of influenza virus infection. Since NS1 does not contain mitochondria-targeting signals, we suspected that there is an association between the NS1 and mitochondrial proteins. This hypothesis was tested by demonstrating the interaction of NS1 with mitochondrial antiviral-signaling protein (MAVS) in a RIG-I-independent manner. Importantly, the association with MAVS facilitated the mitochondrial localization of NS1 and thereby significantly impeded MAVS-mediated Type I IFN production.
Effectiveness of the 13-Valent Pneumococcal Conjugate Vaccine on Invasive Pneumococcal Disease in Greenland

The 13-valent pneumococcal conjugate vaccine (PCV13) was introduced in 2010 to the childhood vaccination program in Greenland. This study aimed to estimate the effectiveness of the PCV13 on the incidence of invasive pneumococcal disease (IPD) in children and in adults in Greenland. IPD cases from the pre-PCV13 period (January 1995–September 2010) were compared with the post-PCV13 period (September 2010–October 2020). Register data were collected from laboratory records, IPD reports, the national registry on admissions, and medical files. A total of 295 IPD cases were identified in the study period. Overall IPD incidence rate (IR) declined from the pre-PCV13 period to the post-PCV13 period (IR 23.3 to 15.3 per 100,000 person years). Overall IPD incidence among children decreased significantly, whereas overall IPD incidence among the elderly increased significantly. During the post-PCV13 period, the incidence of vaccine serotype (VT) IPD decreased in all ages, while the incidence of non-vaccine serotype (NVT) IPD increased. This increase was most substantial among elderly ≥60 years. In conclusion, the PCV13 has reduced incidence rates of IPD in Greenland. However, the increase in NVT IPD among the elderly is noteworthy, and sup-ports continued surveillance of IPD in the population of Greenland.
Identification and Characterization of Swine Influenza Virus H1N1 Variants Generated in Vaccinated and Nonvaccinated, Challenged Pigs

Influenza viruses represent a continuous threat to both animal and human health. The 2009 H1N1 A influenza pandemic highlighted the importance of a swine host in the adaptation of influenza viruses to humans. Nowadays, one of the most extended strategies used to control swine influenza viruses (SIVs) is the trivalent vaccine application, whose formulation contains the most frequently circulating SIV subtypes H1N1, H1N2, and H3N2. These vaccines do not provide full protection against the virus, allowing its replication, evolution, and adaptation. To better understand the main mechanisms that shape viral evolution, here, the SIV intra-host diversity was analyzed in samples collected from both vaccinated and nonvaccinated animals challenged with the H1N1 influenza A virus. Twenty-eight whole SIV genomes were obtained by next-generation sequencing, and differences in nucleotide variants between groups were established. Substitutions were allocated along all influenza genetic segments, while the most relevant nonsynonymous substitutions were allocated in the NS1 protein on samples collected from vaccinated animals, suggesting that SIV is continuously evolving despite vaccine application. Moreover, new viral variants were found in both vaccinated and nonvaccinated pigs, showing relevant substitutions in the HA, NA, and NP proteins, which may increase viral fitness under field conditions.
Experimental Evolution of West Nile Virus at Higher Temperatures Facilitates Broad Adaptation and Increased Genetic Diversity

West Nile virus (WNV, Flaviviridae, Flavivirus) is a mosquito-borne flavivirus introduced to North America in 1999. Since 1999, the Earth’s average temperature has increased by 0.6 °C. Mosquitoes are ectothermic organisms, reliant on environmental heat sources. Temperature impacts vector–virus interactions which directly influence arbovirus transmission. RNA viral replication is highly error-prone and increasing temperature could further increase replication rates, mutation frequencies, and evolutionary rates. The impact of temperature on arbovirus evolutionary trajectories and fitness landscapes has yet to be sufficiently studied. To investigate how temperature impacts the rate and extent of WNV evolution in mosquito cells, WNV was experimentally passaged 12 times in Culex tarsalis cells, at 25 °C and 30 °C. Full-genome deep sequencing was used to compare genetic signatures during passage, and replicative fitness was evaluated before and after passage at each temperature. Our results suggest adaptive potential at both temperatures, with unique temperature-dependent and lineage-specific genetic signatures. Further, higher temperature passage was associated with significantly increased replicative fitness at both temperatures and increases in nonsynonymous mutations. Together, these data indicate that if similar selective pressures exist in natural systems, increases in temperature could accelerate emergence of high-fitness strains with greater phenotypic plasticity.
Monitoring Flexions and Torsions of the Trunk via Gyroscope-Calibrated Capacitive Elastomeric Wearable Sensors

Reliable, easy-to-use, and cost-effective wearable sensors are desirable for continuous measurements of flexions and torsions of the trunk, in order to assess risks and prevent injuries related to body movements in various contexts. Piezo-capacitive stretch sensors, made of dielectric elastomer membranes coated with compliant electrodes, have recently been described as a wearable, lightweight and low-cost technology to monitor body kinematics. An increase of their capacitance upon stretching can be used to sense angular movements. Here, we report on a wearable wireless system that, using two sensing stripes arranged on shoulder straps, can detect flexions and torsions of the trunk, following a simple and fast calibration with a conventional tri-axial gyroscope on board. The piezo-capacitive sensors avoid the errors that would be introduced by continuous sensing with a gyroscope, due to its typical drift. Relative to stereophotogrammetry (non-wearable standard system for motion capture), pure flexions and pure torsions could be detected by the piezo-capacitive sensors with a root mean square error of ~8° and ~12°, respectively, whilst for flexion and torsion components in compound movements, the error was ~13° and ~15°, respectively.
An Investigation of Culicoides (Diptera: Ceratopogonidae) as Potential Vectors of Medically and Veterinary Important Arboviruses in South Africa

Culicoides-borne viruses such as bluetongue, African horse sickness, and Schmallenberg virus cause major economic burdens due to animal outbreaks in Africa and their emergence in Europe and Asia. However, little is known about the role of Culicoides as vectors for zoonotic arboviruses. In this study, we identify both veterinary and zoonotic arboviruses in pools of Culicoides biting midges in South Africa, during 2012–2017. Midges were collected at six surveillance sites in three provinces and screened for Alphavirs, Flavivirus, Orthobunyavirus, and Phlebovirus genera; equine encephalosis virus (EEV); and Rhaboviridae, by reverse transcription polymerase chain reaction. In total, 66/331 (minimum infection rate (MIR) = 0.4) pools tested positive for one or more arbovirus. Orthobunyaviruses, including Shuni virus (MIR = 0.1) and EEV (MIR = 0.2) were more readily detected, while only 2/66 (MIR = 0.1) Middelburg virus and 4/66 unknown Rhabdoviridae viruses (MIR = 0.0) were detected. This study suggests Culicoides as potential vectors of both veterinary and zoonotic arboviruses detected in disease outbreaks in Africa, which may contribute to the emergence of these viruses to new regions.
Wavelet Analysis and Self-Similarity of Photoplethysmography Signals for HRV Estimation and Quality Assessment

Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person’s movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals’ self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
A Systematic Review and Meta-Analysis of Seasonal Influenza Vaccination of Health Workers

A systematic review and meta-analysis was conducted to estimate the pooled effect of influenza vaccinations for health workers (HWs). Nine databases were screened to identify randomized clinical trials and comparative observational studies that reported the effect of influenza vaccination among HWs. The risk ratio (RR), standardized mean difference, and 95% confidence interval (CI) were employed to study the effect size using fixed/random-effect models. Subgroup analyses and sensitivity analyses were conducted accordingly. Publication bias was examined. Sixteen studies (involving 7971 HWs from nine countries) were included after a comprehensive literature search. The combined RR regarding the incidence of laboratory-confirmed influenza was 0.36 (95% CI: 0.25 to 0.54), the incidence of influenza-like illness (ILI) was 0.69 (95% CI: 0.45 to 1.06), the absenteeism rate was 0.63 (95% CI: 0.46 to 0.86), and the integrated standardized mean difference of workdays lost was −0.18 (95% CI: −0.28 to −0.07) days/person. The subgroup analysis indicated that vaccination significantly decreases the incidence of laboratory-confirmed influenza in different countries, study populations, and average-age vaccinated groups. Influenza vaccinations could effectively reduce the incidence of laboratory-confirmed influenza, absenteeism rates, and workdays lost among HWs. It is advisable, therefore, to improve the coverage and increase the influenza vaccination count among HWs, which may benefit both workers and medical institutions.
Semantic Mapping for Autonomous Subsea Intervention

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based simultaneous localization and mapping (SLAM) and 3D object recognition using a database of a priori known objects. The robot uses Doppler velocity log (DVL), pressure, and attitude and heading reference system (AHRS) sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the SLAM, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The SLAM provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.
Immune Protective Efficacy of China’s Traditional Inactivated and Attenuated Vaccines against the Prevalent Strains of Pasteurella multocida in Mice

Capsular type A and D strains of Pasteurella multocida are the main epidemic serogroups in pigs in China. In this study, we preliminarily evaluated the immune protective efficacy of the two traditional vaccines, an inactivated C44-1 aluminum-hydroxide-gel-adjuvanted (Alh–C44-1) vaccine and a live EO630 vaccine, against currently circulating strains of P. multocida in a mouse model. Mice immunized twice with conventional vaccines generated higher antibody titers, and significantly higher levels of IgG were observed in the mice inoculated with the inactivated Alh–C44-1 vaccine on day 35 (p < 0.05) than those with the live EO630 vaccine. The mice immune protection test showed that the vaccination groups had a 57% or 71% protection effect against the serogroup B strain, but had no protective effect against epidemic strains. In conclusion, our study found that the widely used traditional P. multocida vaccines in China provide good protection against homologous strains, but could not provide cross-protection against heterologous strains in a mouse model.
A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2

Modeling antigenic variation in influenza (flu) virus A H3N2 using amino acid sequences is a promising approach for improving the prediction accuracy of immune efficacy of vaccines and increasing the efficiency of vaccine screening. Antigenic drift and antigenic jump/shift, which arise from the accumulation of mutations with small or moderate effects and from a major, abrupt change with large effects on the surface antigen hemagglutinin (HA), respectively, are two types of antigenic variation that facilitate immune evasion of flu virus A and make it challenging to predict the antigenic properties of new viral strains. Despite considerable progress in modeling antigenic variation based on the amino acid sequences, few studies focus on the deep learning framework which could be most suitable to be applied to this task. Here, we propose a novel deep learning approach that incorporates a convolutional neural network (CNN) and bidirectional long-short-term memory (BLSTM) neural network to predict antigenic variation. In this approach, CNN extracts the complex local contexts of amino acids while the BLSTM neural network captures the long-distance sequence information. When compared to the existing methods, our deep learning approach achieves the overall highest prediction performance on the validation dataset, and more encouragingly, it achieves prediction agreements of 99.20% and 96.46% for the strains in the forthcoming year and in the next two years included in an existing set of chronological amino acid sequences, respectively. These results indicate that our deep learning approach is promising to be applied to antigenic variation prediction of flu virus A H3N2.
A Low-Cost Digital Colorimetry Setup to Investigate the Relationship between Water Color and Its Chemical Composition

Developments in digital image acquisition technologies and citizen science lead to more water color observations and broader public participation in environmental monitoring. However, the implications of the use of these simple water color indices for water quality assessment have not yet been fully evaluated. In this paper, we build a low-cost digital camera colorimetry setup to investigate quantitative relationships between water color indices and concentrations of optically active constituents (OACs). As proxies for colored dissolved organic matter (CDOM) and phytoplankton, humic acid and algae pigments were used to investigate the relationship between water chromaticity and concentration. We found that the concentration fits an ascending relationship with xy chromaticity values and a descending relationship with hue angle. Our investigations permitted us to increase the information content of simple water color observations, by relating them to chemical constituent concentrations in observed waters.
Function Is More Reliable Than Quantity to Follow Up the Humoral Response to the Receptor-Binding Domain of SARS-CoV-2-Spike Protein after Natural Infection or COVID-19 Vaccination

Both the SARS-CoV-2 pandemic and emergence of variants of concern have highlighted the need for functional antibody assays to monitor the humoral response over time. Antibodies directed against the spike (S) protein of SARS-CoV-2 are an important component of the neutralizing antibody response. In this work, we report that in a subset of patients—despite a decline in total S-specific antibodies—neutralizing antibody titers remain at a similar level for an average of 98 days in longitudinal sampling of a cohort of 59 Hispanic/Latino patients exposed to SARS-CoV-2. Our data suggest that 100% of seroconverting patients make detectable neutralizing antibody responses which can be quantified by a surrogate viral neutralization test. Examination of sera from ten out of the 59 subjects which received mRNA-based vaccination revealed that both IgG titers and neutralizing activity of sera were higher after vaccination compared to a cohort of 21 SARS-CoV-2 naïve subjects. One dose was sufficient for the induction of a neutralizing antibody, but two doses were necessary to reach 100% surrogate virus neutralization in subjects irrespective of previous SARS-CoV-2 natural infection status. Like the pattern observed after natural infection, the total anti-S antibodies titers declined after the second vaccine dose; however, neutralizing activity remained relatively constant for more than 80 days after the first vaccine dose. Furthermore, our data indicates that—compared with mRNA vaccination—natural infection induces a more robust humoral immune response in unexposed subjects. This work is an important contribution to understanding the natural immune response to the novel coronavirus in a population severely impacted by SARS-CoV-2. Furthermore, by comparing the dynamics of the immune response after the natural infection vs. the vaccination, these findings suggest that functional neutralizing antibody tests are more relevant indicators than the presence or absence of binding antibodies.
Impacts of Quarterly Sow Mass Vaccination with a Porcine Reproductive and Respiratory Syndrome Virus Type 1 (PRRSV-1) Modified Live Vaccine in Two Herds

In recent years, there has been a considerable increase in the use of Modified Live PRRSV Vaccines (MLV) for mass vaccination in Denmark. The potential risks and negative impact of this strategy have been sparsely studied. The aim of this study was to investigate the impact of quarterly sow mass vaccination in two Danish sow herds. The study was performed as an observational prospective cohort of 120 sows in each of two commercial breeding herds in a paired design. Blood samples were taken from sows and oral fluid samples from nursery pigs (four to ten weeks old) before and after vaccination. The presence of PRRSV-1 RNA was measured by real time quantitative reverse transcription-polymerase chain reaction (RT-qPCR), and the level of PRRSV-1 specific antibodies was measured by two different serological assays. PRRS virus was not detected in the sow herds two days before and two weeks after vaccination, but the vaccine strain virus was detected in the nursery pigs. The prevalence of sows without antibodies towards PRRSV-1 went from 6–15% before vaccination to 1–4% after vaccination depending on the serological assay used, despite the fact that they had previously been repeatedly vaccinated. Four sows tested negative for antibodies in both assays after vaccination.
Accuracy Tests and Precision Assessment of Localizing Underground Utilities Using GPR Detection

Applying georadar (GPR) technology for detecting underground utilities is an important element of the comprehensive assessment of the location and ground infrastructure status. These works are usually connected with the conducted investment processes or serialised inventory of underground fittings. The detection of infrastructure is also crucial in implementing the BIM technology, 3D cadastre, and planned network modernization works. GPR detection accuracy depends on the type of equipment used, the selected detection method, and external factors. The multitude of techniques used for localizing underground utilities and constantly growing accuracy demands resulting from the fact that it is often necessary to detect infrastructure under challenging conditions of dense urban development leads to the need to improve the existing technologies. The factor that motivated us to start research on assessing the precision and accuracy of ground penetrating radar detection was the need to ensure the appropriate accuracy, precision, and reliability of detecting underground utilities versus different methods and analyses. The results of the multi-variant GPR were subjected to statistical testing. Various analyses were also conducted, depending on the detection method and on the current soil parameters using a unique sensor probe. When planning detection routes, we took into account regular, established grids and tracked the trajectory of movement of the equipment using GNSS receivers (internal and external ones). Moreover, a specialist probe was used to evaluate the potential influence of the changing soil conditions on the obtained detection results. Our tests were conducted in a developed area for ten months. The results confirmed a strong correlation between the obtained accuracy and the measurement method used, while the correlation with the other factors discussed here was significantly weaker.
The Immune Response to SARS-CoV-2 and Variants of Concern

At the end of 2019 a newly emerged betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of an outbreak of severe pneumonia, subsequently termed COVID-19, in a number of patients in Wuhan, China. Subsequently, SARS-CoV-2 rapidly spread globally, resulting in a pandemic that has to date infected over 200 million individuals and resulted in more than 4.3 million deaths. While SARS-CoV-2 results in severe disease in 13.8%, with increasing frequency of severe disease with age, over 80% of infections are asymptomatic or mild. The immune response is an important determinant of outcome following SARS-CoV-2 infection. While B cell and T cell responses are associated with control of infection and protection against subsequent challenge with SARS-CoV-2, failure to control viral replication and the resulting hyperinflammation are associated with severe COVID-19. Towards the end of 2020, several variants of concern emerged that demonstrate increased transmissibility and/or evasion of immune responses from prior SARS-CoV-2 infection. This article reviews what is known about the humoral and cellular immune responses to SARS-CoV-2 and how mutation and structural/functional changes in the emerging variants of concern impact upon the immune protection from prior infection or vaccination.