Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Prevalence of Adaptive and Prolonged Infections’ Mutations in the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein

Several vaccines with varying efficacies have been developed and are currently administered globally to minimize the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite having an RNA-dependent RNA polymerase with a proofreading activity, new variants of SARS-CoV-2 are on the rise periodically. Some of the mutations in these variants, especially mutations on the spike protein, aid the virus in transmission, infectivity and host immune evasion. Further, these mutations also reduce the effectiveness of some of the current vaccines and monoclonal antibodies (mAbs). In the present study, using the available 984,769 SARS-CoV-2 nucleotide sequences on the NCBI database from the end of 2019 till 28 July 2021, we have estimated the global prevalence of so-called ‘adaptive mutations’ and ‘mutations identified in the prolonged infections’, in the receptor-binding domain (RBD) of the spike (S) protein. Irrespective of the geographical region, in the case of the adaptive mutations, N501Y (48.38%) was found to be the dominant mutation followed by L452R (17.52%), T478K (14.31%), E484K (4.69%), S477N (3.29%), K417T (1.64%), N439K (0.7%) and S494P (0.7%). Other mutations were found to be less prevalent (less than 0.7%). Since the last two months, there has been a massive increase of L452R and T478K mutations (delta variant) in certain areas. In the case of prolonged infections’ mutations (long-term SARS-CoV-2 infections), V483A (0.009%) was found to be dominant followed by Q493R (0.009%), while other mutations were found in less than 0.007% of the studied sequences. The data obtained in this study will aid in the development of better infection control policies, thereby curbing the spread of this virus.
Exploration of the Experience of Care Home Managers of COVID-19 Vaccination Programme Implementation and Uptake by Residents and Staff in Care Homes in Northern Ireland

The Coronavirus 2019 (COVID-19) pandemic disproportionately affected people living and working in care homes. This study aimed to explore the experience of care home managers on the implementation and uptake of the COVID-19 vaccination programme by residents and staff in care homes in Northern Ireland. An exploratory mixed methods approach was used, i.e., semi-structured interviews to design the cross-sectional survey and content analysis of statements using open ended questions. Care home managers were approached and sixty-seven valid quantitative and forty-nine descriptive responses were analysed. The study identified eight themes which described factors that motivated residents (family visits and relationship with managers and staff), and staff vaccine uptake (return to normal life at work and trust in care home managers). The identified themes also confirmed that vaccine uptake is negatively influenced by perceived side effects. The findings indicated that social media can promote or decelerate the uptake of vaccine despite the accessibility to a successful vaccination programme. The study highlights the important role of managers in handling the challenges through building trust and establishing relationships with staff and residents. The findings identified challenges to the uptake of the COVID-19 vaccine by staff and residents that can inform the implementation of future vaccination programmes.
Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.
IgG Antibodies Develop to Spike but Not to the Nucleocapsid Viral Protein in Many Asymptomatic and Light COVID-19 Cases

Since SARS-CoV-2 appeared in late 2019, many studies on the immune response to COVID-19 have been conducted, but the asymptomatic or light symptom cases were somewhat understudied as respective individuals often did not seek medical help. Here, we analyze the production of the IgG antibodies to viral nucleocapsid (N) protein and receptor-binding domain (RBD) of the spike protein and assess the serum neutralization capabilities in a cohort of patients with different levels of disease severity. In half of light or asymptomatic cases the antibodies to the nucleocapsid protein, which serve as the main target in many modern test systems, were not detected. They were detected in all cases of moderate or severe symptoms, and severe lung lesions correlated with respective higher signals. Antibodies to RBD were present in the absolute majority of samples, with levels being sometimes higher in light symptom cases. We thus suggest that the anti-RBD/anti-N antibody ratio may serve as an indicator of the disease severity. Anti-RBD IgG remained detectable after a year or more since the infection, even with a slight tendency to raise over time, and the respective signal correlated with the serum capacity to inhibit the RBD interaction with the ACE-2 receptor.
Statistical Time-Series Analysis of Interferometric Coherence from Sentinel-1 Sensors for Landslide Detection and Early Warning

Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.
Control of Dynamic Positioning System with Disturbance Observer for Autonomous Marine Surface Vessels

The main goal of the research is to design an efficient controller for a dynamic positioning system for autonomous surface ships using the backstepping technique for the case of full-state feedback in the presence of unknown external disturbances. The obtained control commands are distributed to each actuator of the overactuated vessel via unconstrained control allocation. The numerical hydrodynamic model of CyberShip I and the model of environmental disturbances are applied to simulate the operation of the ship control system using the time domain analysis. Simulation studies are presented to illustrate the effectiveness of the proposed controller and its robustness to external disturbances.
Precursors of Viral Proteases as Distinct Drug Targets

Viral proteases are indispensable for successful virion maturation, thus making them a prominent drug target. Their enzyme activity is tightly spatiotemporally regulated by expression in the precursor form with little or no activity, followed by activation via autoprocessing. These cleavage events are frequently triggered upon transportation to a specific compartment inside the host cell. Typically, precursor oligomerization or the presence of a co-factor is needed for activation. A detailed understanding of these mechanisms will allow ligands with non-canonical mechanisms of action to be designed, which would specifically modulate the initial irreversible steps of viral protease autoactivation. Binding sites exclusive to the precursor, including binding sites beyond the protease domain, can be exploited. Both inhibition and up-regulation of the proteolytic activity of viral proteases can be detrimental for the virus. All these possibilities are discussed using examples of medically relevant viruses including herpesviruses, adenoviruses, retroviruses, picornaviruses, caliciviruses, togaviruses, flaviviruses, and coronaviruses.
Construction, Characterization and Application of Recombinant Porcine Deltacoronavirus Expressing Nanoluciferase

Porcine deltacoronavirus (PDCoV), an emerging enteropathogenic coronavirus, causes diarrhoea in suckling piglets and has the potential for cross-species transmission. No effective PDCoV vaccines or antiviral drugs are currently available. Here, we successfully generated an infectious clone of PDCoV strain CHN-HN-2014 using a combination of bacterial artificial chromosome (BAC)-based reverse genetics system with a one-step homologous recombination. The recued virus (rCHN-HN-2014) possesses similar growth characteristics to the parental virus in vitro. Based on the established infectious clone and CRISPR/Cas9 technology, a PDCoV reporter virus expressing nanoluciferase (Nluc) was constructed by replacing the NS6 gene. Using two drugs, lycorine and resveratrol, we found that the Nluc reporter virus exhibited high sensibility and easy quantification to rapid antiviral screening. We further used the Nluc reporter virus to test the susceptibility of different cell lines to PDCoV and found that cell lines derived from various host species, including human, swine, cattle and monkey enables PDCoV replication, broadening our understanding of the PDCoV cell tropism range. Taken together, our reporter viruses are available to high throughput screening for antiviral drugs and uncover the infectivity of PDCoV in various cells, which will accelerate our understanding of PDCoV.
A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous Phenomena with Vague Boundaries Using Sensor Data

Sensor networks (SN) are increasingly used for the observation and monitoring of spatiotemporal phenomena and their dynamics such as pollution, noise and forest fires. In multisensory systems, a sensor node may be equipped with different sensing units to observe and detect several spatiotemporal phenomena at the same time. Simultaneous detection of different phenomena can be used to infer their spatial interactions over space and time. For this purpose, decentralized spatial computing approaches have shown their potential for effective reasoning on spatial phenomena within a sensor network. However, in most cases, spatial extents of continuous dynamic phenomena are uncertain, and their relations and interactions cannot be inferred by the existing approaches at the sensor node level. To address this limitation, in this paper, we propose and develop a decentralized fuzzy rule-based spatial reasoning approach to depict the spatial relations that hold between two evolving spatial phenomena with fuzzy boundaries. The proposed method benefits from a more adapted fuzzy-crisp representation of dynamic phenomena observed by SN where each vague phenomenon is composed of five distinguished zones including the kernel, conjecture and exterior zone and their boundaries. For each detected phenomenon, a sensor node will report one of these zones based on its location. Aggregation of the information reported from the sensor nodes allows reasoning on spatial relations between the observed phenomena and their evolution. Such spatial information provides users with more valuable near real-time information on the state of different phenomena that can be used for informed decision-making.
Cross-Resistance of the Codling Moth against Different Isolates of Cydia pomonella Granulovirus Is Caused by Two Different but Genetically Linked Resistance Mechanisms

Cydia pomonella granulovirus (CpGV) is a widely used biological control agent of the codling moth. Recently, however, the codling moth has developed different types of field resistance against CpGV isolates. Whereas type I resistance is Z chromosomal inherited and targeted at the viral gene pe38 of isolate CpGV-M, type II resistance is autosomal inherited and targeted against isolates CpGV-M and CpGV-S. Here, we report that mixtures of CpGV-M and CpGV-S fail to break type II resistance and is expressed at all larval stages. Budded virus (BV) injection experiments circumventing initial midgut infection provided evidence that resistance against CpGV-S is midgut-related, though fluorescence dequenching assay using rhodamine-18 labeled occlusion derived viruses (ODV) could not fully elucidate whether the receptor binding or an intracellular midgut factor is involved. From our peroral and intra-hemocoel infection experiments, we conclude that two different (but genetically linked) resistance mechanisms are responsible for type II resistance in the codling moth: resistance against CpGV-M is systemic whereas a second and/or additional resistance mechanism against CpGV-S is located in the midgut of CpR5M larvae.
A Pilot Trial to Evaluate the Accuracy of a Novel Non-Invasive Glucose Meter

The non-invasive self-monitoring of blood glucose (SMBG) has been the subject of intense investigation over recent decades. We conducted a pilot study designed to examine a novel non-invasive glucometer, the HGR GWave, utilizing radiofrequency (RF) sensing. Blood glucose levels assessed by this HGR prototype were compared to measurements performed by a hexokinase core laboratory assay during an oral glucose tolerance test (oGTT) for 5 subjects with type 2 diabetes. The HGR glucose meter readings were also compared to two Abbot Freestyle® glucose meters, which were also used for calibration. The accuracy of the results was evaluated through the calculation of relative absolute difference (RAD), specified percentage differences between 43 reference glucose measurements, and using comparator measurements. The median RAD was −4.787. We detected 79.04%, 92.99% and 97.64% of HGR readings within ±10%, ±15% and ±20% of the reference glucose measurements. The HGR readings had a high correlation with reference lab glucose measurements with R2 = 0.924 (95% CI 0.929–0.979; p < 0.0001). When compared to the Freestyle® glucose meters 94.3% and 100% of the readings were within ±5% and ±10%, with R2 = 0.975 (0.975–0.994; p < 0.0001). The HGR prototype glucose meter was found to be accurate in detecting real-time blood glucose during an oGTT in this small pilot study. A study with a broader range of blood glucose levels is needed to further assess its accuracy and its suitability for clinical use.
A Novel Clutter Suppression Method Based on Sparse Bayesian Learning for Airborne Passive Bistatic Radar with Contaminated Reference Signal

The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is used for space–time adaptive processing (STAP), the power spectrum of the spatial–temporal clutter is broadened. This can cause a series of problems, such as affecting the performance of clutter estimation and suppression, increasing the blind area of target detection, and causing the phenomenon of target self-cancellation. To solve this problem, the authors of this paper propose a novel algorithm based on sparse Bayesian learning (SBL) for direct clutter estimation and multipath clutter suppression. The specific process is as follows. Firstly, the space–time clutter is expressed in the form of covariance matrix vectors. Secondly, the multipath cost is decorrelated in the covariance matrix vectors. Thirdly, the modeling error is reduced by alternating iteration, resulting in a space–time clutter covariance matrix without multipath components. Simulation results showed that this method can effectively estimate and suppress clutter when the reference signal is contaminated.
Advances in Understanding of the Immune Response to Mycobacterial Pathogens and Vaccines through Use of Cattle and Mycobacterium avium subsp. paratuberculosis as a Prototypic Mycobacterial Pathogen

Lack of understanding of the immune response to mycobacterial pathogens has impeded progress in development of vaccines. Infection leads to development of an immune response that controls infection but is unable to eliminate the pathogen, resulting in a persistent infection. Although this puzzle remains to be solved, progress has been made using cattle as a model species to study the immune response to a prototypic mycobacterium, Mycobacterium a. paratuberculosis (Map). As chronicled in the review, incremental advances in characterizing the immune response to mycobacteria during the last 30 years with increases in information on the evolution of mycobacteria and relA, a gene regulating the stringent response, have brought us closer to an answer. We provide a brief overview of how mycobacterial pathogens were introduced into cattle during the transition of humankind to nomadic pastoralists who domesticated animals for food and farming. We summarize what is known about speciation of mycobacteria since the discovery of Mybacterium tuberculsis Mtb, M. bovis Mbv, and Map as zoonotic pathogens and discuss the challenges inherent in the development of vaccines to mycobacteria. We then describe how cattle were used to characterize the immune response to a prototypic mycobacterial pathogen and development of novel candidate vaccines.
Construction and Evaluation of a Tumor Mutation Burden-Related Prognostic Signature for Thyroid Carcinoma

Thyroid carcinoma is a type of prevalent cancer. Its prognostic evaluation depends on clinicopathological features. However, such conventional methods are deficient. Based on mRNA, single nucleotide variants (SNV), and clinical information of thyroid carcinoma from The Cancer Genome Atlas (TCGA) database, this study statistically analyzed mutational signature of patients with this disease. Missense mutation and SNV are the most common variant classification and variant type, respectively. Next, tumor mutation burden (TMB) of sample was calculated. Survival status of high/low TMB groups was analyzed, as well as the relationship between TMB and clinicopathological features. Results revealed that patients with high TMB had poor survival status, and TMB was related to several clinicopathological features. Through analysis on DEGs in high/low TMB groups, 381 DEGs were obtained. They were found to be mainly enriched in muscle tissue development through enrichment analysis. Then, through Cox regression analysis, a 5-gene prognostic signature was established, which was then evaluated through survival curves and receiver operation characteristic (ROC) curves. The result showed that the signature was able to effectively predict patient's prognosis and to serve as an independent prognostic risk factor. Finally, through Gene Set Enrichment Analysis (GSEA) on high/low-risk groups, DEGs were found to be mainly enriched in signaling pathways related to DNA repair. Overall, based on the TCGA-THCA dataset, we constructed a 5-gene prognostic signature through a trail of bioinformatics analysis.
Imbalanced Immune Response of T-Cell and B-Cell Subsets in Patients with Moderate and Severe COVID-19

Background: The immunological changes associated with COVID-19 are largely unknown. Methods: Patients with COVID-19 showing moderate (n = 18; SpO2 > 93%, respiratory rate > 22 per minute, CRP > 10 mg/L) and severe (n = 23; SpO2 < 93%, respiratory rate >30 per minute, PaO2/FiO2 ≤ 300 mmHg, permanent oxygen therapy, qSOFA > 2) infection, and 37 healthy donors (HD) were enrolled. Circulating T- and B-cell subsets were analyzed by flow cytometry. Results: CD4+Th cells were skewed toward Th2-like phenotypes within CD45RA+CD62L− (CM) and CD45RA–CD62L− (EM) cells in patients with severe COVID-19, while CM CCR6+ Th17-like cells were decreased if compared with HD. Within CM Th17-like cells “classical” Th17-like cells were increased and Th17.1-like cells were decreased in severe COVID-19 cases. Circulating CM follicular Th-like (Tfh) cells were decreased in all COVID-19 patients, and Tfh17-like cells represented the most predominant subset in severe COVID-19 cases. Both groups of patients showed increased levels of IgD-CD38++ B cells, while the levels of IgD+CD38− and IgD–CD38− were decreased. The frequency of IgD+CD27+ and IgD–CD27+ B cells was significantly reduced in severe COVID-19 cases. Conclusions: We showed an imbalance within almost all circulating memory Th subsets during acute COVID-19 and showed that altered Tfh polarization led to a dysregulated humoral immune response.