Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-cell analysis reveals divergent responses of human dendritic cells to the MVA vaccine

Infected and noninfected bystander dendritic cells have distinct roles in the response to the vaccine vector MVA. Division of labor for vaccine responses The modified vaccinia virus Ankara (MVA) is an attenuated pox virus vaccine and may be a safe vector for vaccines against other viruses. Dendritic cells (DCs) are among the first responders to invading pathogens and are thus critical to vaccine efficacy. Doring et al. used single-cell RNA sequencing to analyze the response of human DCs to MVA. They found a division of labor among DCs in which infected DCs produced inflammatory cytokines that activated nearby, noninfected DCs that, in turn, expressed T cell costimulatory molecules. The subsequent and rapid death of infected DCs occurred in a parallel but independent manner from that of the cytokine response. The resulting insight into the pathways involved may inform the development of additional MVA-based vaccines. Modified vaccinia Ankara (MVA) is a live, attenuated human smallpox vaccine and a vector for the development of new vaccines against infectious diseases and cancer. Efficient activation of the immune system by MVA partially relies on its encounter with dendritic cells (DCs). MVA infection of DCs leads to multiple outcomes, including cytokine production, activation of costimulatory molecules for T cell stimulation, and cell death. Here, we examined how these diverse responses are orchestrated in human DCs. Single-cell analyses revealed that the response to MVA infection in DCs was limited to early viral gene expression. In response to the early events in the viral cycle, we found that DCs grouped into three distinct clusters. A cluster of infected cells sensed the MVA genome by the intracellular innate immunity pathway mediated by cGAS, STING, TBK1, and IRF3 and subsequently produced inflammatory cytokines. In response to these cytokines, a cluster of noninfected bystander cells increased costimulatory molecule expression. A separate cluster of infected cells underwent caspase-dependent apoptosis. Induction of apoptosis persisted after inhibition of innate immunity pathway mediators independently of previously described IRF-dependent or replication-dependent pathways and was a response to early MVA gene expression. Together, our study identified multiple mechanisms that underlie the interactions of MVA with human DCs.
Review of bio-optical imaging systems with a high space-bandwidth product

Abstract. Optical imaging has served as a primary method to collect information about biosystems across scales—from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics—the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.
A Factor Analysis of the Entrepreneurial Success, Motivation andProblems Faced: The Case of Chinese Entrepreneurs

We examined the following factors in this study of Chinese entrepreneurs’ success: entrepreneurial motivation, success factors, start-up challenges, business growth, the role of family and social network in reducing stress, and professional satisfaction. In this study, we examined Chinese entrepreneurs for their motivations to start a business, factors that support their success, problems that occur during start-up and growth of the business, the role of family and social network in creating a less stressful working environment, and the overall level of satisfaction with their profession. Results indicate that economic worries were the most important reason for starting one’s own business than career and personal motives. Chinese small and medium-sized enterprise(SME) owners cited honesty and customer relations as the other most important factors in their success. Additionally, Chinese entrepreneurs rated employee-related problems as the most critical problem among all problems we asked in the survey. Results also showed that the gender of the entrepreneurs we sampled was not a significant differentiating factor in identifying entrepreneurial motivations.
Recent Trends in the Material Well-Being of the Working Class in America

I examine trends in the material well-being of working-class households using data from the Current Population Survey in the two decades surrounding the Great Recession. In the years leading up to the Great Recession, average earnings, homeownership, and insurance coverage all fell, and absolute poverty and food insecurity accelerated. After-tax incomes were, for the most part, stagnant. The economic hemorrhaging either abated or reversed, however, in the decade after the Great Recession, especially for the least skilled and for households headed by a Hispanic person. This includes robust earnings growth, which led to declines in earnings inequality, absolute poverty, and food insecurity, coupled with increased insurance coverage and a modest rebound in after-tax incomes. As many of these recent advances likely stalled with the onset of the COVID-19 pandemic, I discuss various policy options.
GC-UNet: An Improved UNet Model for Mangrove Segmentation Using Landsat8

Accurate and effective recognition of mangrove on the remote sensing images is necessary in protecting the mangrove ecosystem. Nowadays, many methods have been developed for remote sensing image segmentation. They can be classified as methods based on geographic and methods based on machine learning. However, these methods may be too time-consuming or not accurate enough in some cases. Due to these factors, we propose GC-UNet, a novel method based on UNet, which embeds Global Context Block to get long-range dependency, uses SPConv to focus on intrinsic information and adopts Adaptively Spatial Feature Fusion to process features from different levels. It greatly strengthens the presentation ability of the model. The experimental results demonstrate that our proposed model obviously enhances the performance of the image segmentation. We compare our method with DeepLabV3 and OCNet. Our method achieves a dice coefficient of 0.8604, exceeding the above popular semantic segmentation models.
Collaborative Filtering Based on Clustering and Simulated Annealing

Collaborative filtering (CF) is a typical and widely used recommendation method that can be categorized into user-based CF and item-based CF. For user-based CF, how the approach chosen more suitable similar users is important. To solve this problem, we first define the user-genre vector as the objects used for clustering. The user-genre vector uses not only genre information to enrich the source data for recommendation but also users’ rating biases, which can be differentiated, to certain extent. Then, we propose a k-means clustering method enhanced by simulated annealing to ensure stable clustering results. Finally, we define comprehensive similarity, which combines original and transformed ratings, to select more appropriate similar users for recommendation. Our experimental results showed that the proposed method can make recommendations accurately and reliably.
Deep Learning Network in Relation Inference from Financial Report

The complexity of enterprises’ annual reports has increased significantly under supervision. Although the interactive relations of enterprises attracted interest both among scholars and practitioners, however, the decision support information is scattered in heterogeneous and enormous data. Here introduces a new learnable deep network—which implements an inference for entity- and relation-centric representations of multilayer, dynamical but sparse system. The results show the approach supporting accurate predictions from real data extracted from financial reports. Indeed, the method shows robustness with incomplete data, thus representing a valuable tool for gaining insights on privacy-protected financial systems.
The evolution of morality and the role of commitment

Abstract Abstract A considerable share of the literature on the evolution of human cooperation considers the question why we have not evolved to play the Nash equilibrium in prisoners’ dilemmas or public goods games. In order to understand human morality and pro-social behaviour, we suggest that it would actually be more informative to investigate why we have not evolved to play the subgame perfect Nash equilibrium in sequential games, such as the ultimatum game and the trust game. The ‘rationally irrational’ behaviour that can evolve in such games gives a much better match with actual human behaviour, including elements of morality such as honesty, responsibility and sincerity, as well as the more hostile aspects of human nature, such as anger and vengefulness. The mechanism at work here is commitment, which does not need population structure, nor does it need interactions to be repeated. We argue that this shift in focus can not only help explain why humans have evolved to know wrong from right, but also why other animals, with similar population structures and similar rates of repetition, have not evolved similar moral sentiments. The suggestion that the evolutionary function of morality is to help us commit to otherwise irrational behaviour stems from the work of Robert Frank (American Economic Review, 77(4), 593–604, 1987; Passions within reason: The strategic role of the emotions, WW Norton, 1988), which has played a surprisingly modest role in the scientific debate to date.
A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins

Deep mutational scanning is a powerful method to explore the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, would facilitate their in-depth characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli combining synthetic gene circuits based on CRISPR interference with flow cytometry-coupled sequencing and mathematical modeling. Using this pipeline, we created and characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of Streptococcus pyogenes Cas9. The resulting mutational fitness landscapes revealed that both Acrs possess a considerable mutational tolerance as well as an intrinsic redundancy with respect to Cas9 inhibitory features, suggesting evolutionary pressure towards high plasticity and robustness. Finally, to demonstrate that our pipeline can inform the optimization and fine-tuning of Acrs for genome editing applications, we cross-validated a subset of AcrIIA4 mutants via gene editing assays in mammalian cells and in vitro affinity measurements. Together, our work establishes deep mutational scanning as powerful method for anti-CRISPR protein characterization and optimization.
MEK1/2 inhibition transiently alters the tumor immune microenvironment to enhance immunotherapy efficacy against head and neck cancer

Background Although the mitogen-activated protein kinases (MAPK) pathway is hyperactive in head and neck cancer (HNC), inhibition of MEK1/2 in HNC patients has not shown clinically meaningful activity. Therefore, we aimed to characterize the effect of MEK1/2 inhibition on the tumor microenvironment (TME) of MAPK-driven HNC, elucidate tumor-host interaction mechanisms facilitating immune escape on treatment, and apply rationale-based therapy combination immunotherapy and MEK1/2 inhibitor to induce tumor clearance. Methods Mouse syngeneic tumors and xenografts experiments were used to analyze tumor growth in vivo. Single-cell cytometry by time of flight, flow cytometry, and tissue stainings were used to profile the TME in response to trametinib (MEK1/2 inhibitor). Co-culture of myeloid-derived suppressor cells (MDSC) with CD8+ T cells was used to measure immune suppression. Overexpression of colony-stimulating factor-1 (CSF-1) in tumor cells was used to show the effect of tumor-derived CSF-1 on sensitivity to trametinib and anti-programmed death- 1 (αPD-1) in mice. In HNC patients, the ratio between CSF-1 and CD8A was measured to test the association with clinical benefit to αPD-1 and αPD-L1 treatment. Results Using preclinical HNC models, we demonstrated that treatment with trametinib delays HNC initiation and progression by reducing tumor cell proliferation and enhancing the antitumor immunity of CD8+ T cells. Activation of CD8+ T cells by supplementation with αPD-1 antibody eliminated tumors and induced an immune memory in the cured mice. Mechanistically, an early response to trametinib treatment sensitized tumors to αPD-1-supplementation by attenuating the expression of tumor-derived CSF-1, which reduced the abundance of two CSF-1R+CD11c+ MDSC populations in the TME. In contrast, prolonged treatment with trametinib abolished the antitumor activity of αPD-1, because tumor cells undergoing the epithelial to mesenchymal transition in response to trametinib restored CSF-1 expression and recreated an immune-suppressive TME. Conclusion Our findings provide the rationale for testing the trametinib/αPD-1 combination in HNC and highlight the importance of sensitizing tumors to αPD-1 by using MEK1/2 to interfere with the tumor–host interaction. Moreover, we describe the concept that treatment of cancer with a targeted therapy transiently induces an immune-active microenvironment, and supplementation of immunotherapy during this time further activates the antitumor machinery to cause tumor elimination.
Coiled coil control of growth factor and inhibitor-dependent EGFR trafficking and degradation

EGFR exhibits biased signaling, whereby growth factor or mutation-dependent changes in receptor conformation and/or dynamics elicit distinct intracellular outcomes. We report that many outcomes associated with activated EGFR are controlled by a two-state coiled coil switch located within the juxtamembrane segment (JM), an essential component of the cytosolic dimer interface. The position of this switch defines the path of endocytic trafficking and whether or not EGFR is degraded within lysosomes. JM coiled coil identity also predicts kinase-independent effects of oncogenic EGFR mutations and clinically relevant tyrosine kinase inhibitors (TKIs) that promote efficient, lysosome-based EGFR degradation. These findings provide a model for biased EGFR signaling, insights into kinase-independent activities of EGFR and clinically relevant TKIs, and identify new strategies for modulating protein lifetime.
Memory Clinics and Day Care Centers in Thessaloniki, Northern Greece: 30 Years of Clinical Practice and Experience

Background: This review describes the diagnostic and interventional procedures conducted in two university memory clinics (established network of G. Papanikolaou Hospital: 1988–2017 and AHEPA hospital: 2017–today) and 2 day care centers (established network of DCCs: 2005–today) in North Greece and their contribution in the scientific field of dementia. The aims of this work are (1) to provide a diagnosis and treatment protocol established in the network of memory clinics and DCCs and (2) to present further research conducted in the aforementioned network during the last 30 years of clinical practice. Methods: The guidelines to set a protocol demand a series of actions as follows: (1) set the diagnosis criteria, neuropsychological assessment, laboratory examinations, and examination of neurophysiological, neuroimaging, cerebrospinal fluid, blood, and genetic markers; and (2) apply non-pharmacological interventions according to the needs and specialized psychosocial interventions of the patient to the caregivers of the patient. Results: In addition to the guidelines followed in memory clinics at the 1st and 3rd Department of Neurology and two DCCs, a database of patients, educational programs, and further participation in international research programs, including clinical trials, make our contribution in the dementia field strong. Conclusion: In the current paper, we provide useful guidelines on how major and minor neurocognitive disorders are being treated in Thessaloniki, Greece, describing successful practices which have been adapted in the last 30 years.
Discovering Potential Taxonomic Biomarkers of Type 2 Diabetes From Human Gut Microbiota via Different Feature Selection Methods

Human gut microbiota is a complex community of organisms including trillions of bacteria. While these microorganisms are considered as essential regulators of our immune system, some of them can cause several diseases. In recent years, next-generation sequencing technologies accelerated the discovery of human gut microbiota. In this respect, the use of machine learning techniques became popular to analyze disease-associated metagenomics datasets. Type 2 diabetes (T2D) is a chronic disease and affects millions of people around the world. Since the early diagnosis in T2D is important for effective treatment, there is an utmost need to develop a classification technique that can accelerate T2D diagnosis. In this study, using T2D-associated metagenomics data, we aim to develop a classification model to facilitate T2D diagnosis and to discover T2D-associated biomarkers. The sequencing data of T2D patients and healthy individuals were taken from a metagenome-wide association study and categorized into disease states. The sequencing reads were assigned to taxa, and the identified species are used to train and test our model. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization, Maximum Relevance and Minimum Redundancy, Correlation Based Feature Selection, and select K best approach. To test the performance of the classification based on the features that are selected by different methods, we used random forest classifier with 100-fold Monte Carlo cross-validation. In our experiments, we observed that 15 commonly selected features have a considerable effect in terms of minimizing the microbiota used for the diagnosis of T2D and thus reducing the time and cost. When we perform biological validation of these identified species, we found that some of them are known as related to T2D development mechanisms and we identified additional species as potential biomarkers. Additionally, we attempted to find the subgroups of T2D patients using k-means clustering. In summary, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of T2D, investigates potential biomarkers of T2D, and finds out which subset of microbiota is more informative than other taxa by applying state-of-the art feature selection methods.
Adaptation of Soil Fungal Community Structure and Assembly to Long- Versus Short-Term Nitrogen Addition in a Tropical Forest

Soil fungi play critical roles in ecosystem processes and are sensitive to global changes. Elevated atmospheric nitrogen (N) deposition has been well documented to impact on fungal diversity and community composition, but how the fungal community assembly responds to the duration effects of experimental N addition remains poorly understood. Here, we aimed to investigate the soil fungal community variations and assembly processes under short- (2 years) versus long-term (13 years) exogenous N addition (∼100 kg N ha–1 yr–1) in a N-rich tropical forest of China. We observed that short-term N addition significantly increased fungal taxonomic and phylogenetic α-diversity and shifted fungal community composition with significant increases in the relative abundance of Ascomycota and decreases in that of Basidiomycota. Short-term N addition also significantly increased the relative abundance of saprotrophic fungi and decreased that of ectomycorrhizal fungi. However, unremarkable effects on these indices were found under long-term N addition. The variations of fungal α-diversity, community composition, and the relative abundance of major phyla, genera, and functional guilds were mainly correlated with soil pH and NO3––N concentration, and these correlations were much stronger under short-term than long-term N addition. The results of null, neutral community models and the normalized stochasticity ratio (NST) index consistently revealed that stochastic processes played predominant roles in the assembly of soil fungal community in the tropical forest, and the relative contribution of stochastic processes was significantly increased by short-term N addition. These findings highlighted that the responses of fungal community to N addition were duration-dependent, i.e., fungal community structure and assembly would be sensitive to short-term N addition but become adaptive to long-term N enrichment.
Signaling Modulation by miRNA-221-3p During Tooth Morphogenesis in Mice

miRNAs are conserved short non-coding RNAs that play a role in the modulation of various biological pathways during tissue and organ morphogenesis. In this study, the function of miRNA-221-3p in tooth development, through its loss or gain in function was evaluated. A variety of techniques were utilized to evaluate detailed functional roles of miRNA-221-3p during odontogenesis, including in vitro tooth cultivation, renal capsule transplantation, in situ hybridization, real-time PCR, and immunohistochemistry. Two-day in vitro tooth cultivation at E13 identified altered cellular events, including cellular proliferation, apoptosis, adhesion, and cytoskeletal arrangement, with the loss and gain of miRNA-221-3p. qPCR analysis revealed alterations in gene expression of tooth-related signaling molecules, including β-catenin, Bmp2, Bmp4, Fgf4, Ptch1, and Shh, when inhibited with miRNA-221-3p and mimic. Also, the inhibition of miRNA-221-3p demonstrated increased mesenchymal localizations of pSMAD1/5/8, alongside decreased expression patterns of Shh and Fgf4 within inner enamel epithelium (IEE) in E13 + 2 days in vitro cultivated teeth. Moreover, 1-week renal transplantation of in vitro cultivated teeth had smaller tooth size with reduced enamel and dentin matrices, along with increased cellular proliferation and Shh expression along the Hertwig epithelial root sheath (HERS), within the inhibitor group. Similarly, in 3-week renal calcified teeth, the overexpression of miRNA-221-3p did not affect tooth phenotype, while the loss of function resulted in long and slender teeth with short mesiodistal length. This study provides evidence that a suitable level of miRNA-221-3p is required for the modulation of major signaling pathways, including Wnt, Bmp, and Shh, during tooth morphogenesis.