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Lastly, thanks to transduction of new HLA molecules, the PDC platform can benefit many patients through the easy addition of matched HLA-I molecules. The demonstration of the effective retroviral transduction of PDC*line cells strengthens and broadens the scope of the PDC*line platform, which can be used in adoptive or active immunotherapy for the treatment of infectious diseases or cancer.The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). Here, we review the molecular pathogenesis of SARS-CoV-2 and its relationship with oxidative stress (OS) and inflammation. Furthermore, we analyze the potential role of antioxidant and anti-inflammatory therapies to prevent severe complications. this website OS has a potential key role in the COVID-19 pathogenesis by triggering the NOD-like receptor family pyrin domain containing 3 inflammasome and nuclear factor-kB (NF-kB). While exposure to many pro-oxidants usually induces nuclear factor erythroid 2 p45-related factor2 (NRF2) activation and upregulation of antioxidant related elements expression, respiratory viral infections often inhibit NRF2 and/or activate NF-kB pathways, resulting in inflammation and oxidative injury. Hence, the use of radical scavengers like N-acetylcysteine and vitamin C, as well as of steroids and inflammasome inhibitors, has been proposed. The NRF2 pathway has been shown to be suppressed in severe SARS-CoV-2 patients. Pharmacological NRF2 inducers have been reported to inhibit SARS-CoV-2 replication, the inflammatory response, and transmembrane protease serine 2 activation, which for the entry of SARS-CoV-2 into the host cells through the angiotensin converting enzyme 2 receptor. Thus, NRF2 activation may represent a potential path out of the woods in COVID-19 pandemic.The oxidative phosphorylation (OXPHOS) system localized in the inner mitochondrial membrane secures production of the majority of ATP in mammalian organisms. Individual OXPHOS complexes form supramolecular assemblies termed supercomplexes. The complexes are linked not only by their function but also by interdependency of individual complex biogenesis or maintenance. For instance, cytochrome c oxidase (cIV) or cytochrome bc1 complex (cIII) deficiencies affect the level of fully assembled NADH dehydrogenase (cI) in monomeric as well as supercomplex forms. It was hypothesized that cI is affected at the level of enzyme assembly as well as at the level of cI stability and maintenance. However, the true nature of interdependency between cI and cIV is not fully understood yet. We used a HEK293 cellular model where the COX4 subunit was completely knocked out, serving as an ideal system to study interdependency of cI and cIV, as early phases of cIV assembly process were disrupted. Total absence of cIV was accompanied by profound deficiency of cI, documented by decrease in the levels of cI subunits and significantly reduced amount of assembled cI. Supercomplexes assembled from cI, cIII, and cIV were missing in COX4I1 knock-out (KO) due to loss of cIV and decrease in cI amount. Pulse-chase metabolic labeling of mitochondrial DNA (mtDNA)-encoded proteins uncovered a decrease in the translation of cIV and cI subunits. Moreover, partial impairment of mitochondrial protein synthesis correlated with decreased content of mitochondrial ribosomal proteins. In addition, complexome profiling revealed accumulation of cI assembly intermediates, indicating that cI biogenesis, rather than stability, was affected. We propose that attenuation of mitochondrial protein synthesis caused by cIV deficiency represents one of the mechanisms, which may impair biogenesis of cI.The lack of high-resolution thermal images is a limiting factor in the fusion with other sensors with a higher resolution. Different families of algorithms have been designed in the field of remote sensors to fuse panchromatic images with multispectral images from satellite platforms, in a process known as pansharpening. Attempts have been made to transfer these pansharpening algorithms to thermal images in the case of satellite sensors. Our work analyses the potential of these algorithms when applied to thermal images from unmanned aerial vehicles (UAVs). We present a comparison, by means of a quantitative procedure, of these pansharpening methods in satellite images when they are applied to fuse high-resolution images with thermal images obtained from UAVs, in order to be able to choose the method that offers the best quantitative results. link2 This analysis, which allows the objective selection of which method to use with this type of images, has not been done until now. link3 This algorithm selection is used here to fuse images from thermal sensors on UAVs with other images from different sensors for the documentation of heritage, but it has applications in many other fields.Flocculation is still one of the most important and efficient processes for water treatment. However, most industrial processes, such as in water treatment plants, still use huge amounts of synthetic polyelectrolytes for the flocculation process. Here we compare the flocculation of two different suspended particles, i.e., silica particles and china clay, with the biopolymer chitosan and two common strong synthetic polyelectrolytes. As a flocculant, chitosan featured a minimum uptake rate of 0.05 mg/g for silica and 1.8 mg/g for china clay. Polydiallyldimethylammonium chloride (PDADMAC) for comparison possessed a minimum uptake rate of 0.05 mg/g for silica and 2.2 mg/g for china clay. Chitosan as an environmentally friendly biopolymer competes with the synthetic polyelectrolytes and thus represents a beneficial economic alternative to synthetic flocculants.Objective The purpose of this research was to assess the workforce characteristics associated with public health employees' perceived impact of emerging trends in public health on their day-to-day work. Methods Multinomial logistic regression was performed to analyze data from the 2017 PH WINS, a cross-sectional survey utilizing a nationally representative sample of the United States public health workforce. Results More than 55% of the public health workforce perceived that their day-to-day work was impacted by the emerging public health trends. Workplace environment was significantly associated with the perception of their day-to-day work being impacted by emerging public health trends such as quality improvement (QI) (AOR = 1.04, p less then 0.001), and evidence-based public health practice (EBPH) (AOR = 1.04, p less then 0.001). Race, ethnicity, and educational status were also positively associated with the perceived impact of the emerging public health trends. Conclusions The organizational culture of a public health agency influences the engagement of the workforce and their perception of the meaningfulness of their work. As practitioners shift into chief health strategists, it will be imperative for them to have training in public health foundations and tools in order to efficiently serve their communities.Sustainability of the workforce becomes a crucial issue, of which responsible care for employees can increase job satisfaction and human capital that impact corporate ability to absorb and generate new knowledge. Firms are obligated to provide a healthy and safe working environment for their employees, but it may in turn hinder innovation due to rigid and structured institutional regulations. Drawing on data of 308 China's pharmaceutical firms from 2010 to 2017, we investigated whether employee care can trigger innovation under corporate adoption of the occupational health and safety management system (OHSMS). Our results suggest that both employee care and OHSMS adoption have a positive impact on innovation. Moreover, the positive relationship between employee care and innovation was more pronounced in firms that had adopted the OHSMS certification. These findings are valuable to policymakers and corporate managers in emerging economies through corroborating the important role of workforce sustainability in facilitating firm innovation.Olive is considered one of the oldest and the most important cultivated fruit trees in Albania. In the present study, the genetic diversity and structure of Albanian olive germplasm is represented by a set of 194 olive genotypes collected in-situ in their natural ecosystems and in the ex-situ collection. The study was conducted using 26 microsatellite markers (14 genomic SSR and 12 Expressed Sequence Tag microsatellites). The identity analysis revealed 183 unique genotypes. Genetic distance-based and model-based Bayesian analyses were used to investigate the genetic diversity, relatedness, and the partitioning of the genetic variability among the Albanian olive germplasm. The genetic distance-based analysis grouped olives into 12 clusters, with an average similarity of 50.9%. Albanian native olives clustered in one main group separated from introduced foreign cultivars, which was also supported by Principal Coordinate Analysis (PCoA) and model-based methods. A core collection of 57 genotypes representing all allelic richness found in Albanian germplasm was developed for the first time. Herein, we report the first extended genetic characterization and structure of olive germplasm in Albania. The findings suggest that Albanian olive germplasm is a unique gene pool and provides an interesting genetic basis for breeding programs.Human Locomotion Mode Recognition (LMR) has the potential to be used as a control mechanism for lower-limb active prostheses. Active prostheses can assist and restore a more natural gait for amputees, but as a medical device it must minimize user risks, such as falls and trips. As such, any control system must have high accuracy and robustness, with a detailed understanding of its internal operation. Long Short-Term Memory (LSTM) machine-learning networks can perform LMR with high accuracy levels. However, the internal behavior during classification is unknown, and they struggle to generalize when presented with novel users. The target problem addressed in this paper is understanding the LSTM classification behavior for LMR. A dataset of six locomotive activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing both steady-state and transitions between activities in natural environments. Non-amputees are used as a substitute for amputees to provide a larger dataset. The dataset is used to analyze the internal behavior of a reduced complexity LSTM network. This analysis identifies that the model primarily classifies activity type based on data around early stance. Evaluation of generalization for unseen subjects reveals low sensitivity to hyper-parameters and over-fitting to individuals' gait traits. Investigating the differences between individual subjects showed that gait variations between users primarily occur in early stance, potentially explaining the poor generalization. Adjustment of hyper-parameters alone could not solve this, demonstrating the need for individual personalization of models. The main achievements of the paper are (i) the better understanding of LSTM for LMR, (ii) demonstration of its low sensitivity to learning hyper-parameters when evaluating novel user generalization, and (iii) demonstration of the need for personalization of ML models to achieve acceptable accuracy.