Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrient profiling reveals extracellular uridine as a fuel for pancreatic cancer through uridine phosphorylase 1

Pancreatic ductal adenocarcinoma (PDA) is a lethal disease characterized by high invasiveness, therapeutic resistance, and metabolic aberrations. Although altered metabolism drives PDA growth and survival, the complete spectrum of metabolites used as nutrients by PDA remains largely unknown. Here, we aimed to determine novel nutrients utilized by PDA. We assessed how >175 metabolites impacted metabolic activity in 19 PDA cell lines under nutrient-restricted conditions. This analysis identified uridine as a novel metabolite driver of PDA survival in glucose-deprived conditions. Uridine utilization strongly correlated with expression of the enzyme uridine phosphorylase 1 (UPP1). Metabolomics profiling, notably 13C-stable isotope tracing, revealed that uridine-derived ribose is the relevant component supporting redox balance, survival, and proliferation in glucose-deprived PDA cells. We demonstrate that UPP1 catabolizes uridine, shunting its ribose component into central carbon metabolism to support glycolysis, the tricarboxylic acid (TCA) cycle and nucleotide biosynthesis. Compared to non-tumoral tissues, we show that PDA tumors express high UPP1, which correlated with poor overall survival in multiple patient cohorts. Further, uridine is enriched in the pancreatic tumor microenvironment, and we demonstrate that this may be provided in part by tumor associated macrophages. Finally, we found that inhibition of UPP1 restricted the ability of PDA cells to use uridine, and that UPP1 knockout impairs tumor growth in vivo. Our data identifies uridine catabolism as a critical aspect of compensatory metabolism in nutrient-deprived PDA cells, suggesting a novel metabolic axis for PDA therapy.
Variation in human herpesvirus 6B telomeric integration, excision, and transmission between tissues and individuals

Human herpesviruses 6A and 6B (HHV-6A/6B) are ubiquitous pathogens that persist lifelong in latent form and can cause severe conditions upon reactivation. They are spread by community-acquired infection of free virus (acqHHV6A/6B) and by germline transmission of inherited chromosomally- integrated HHV-6A/6B (iciHHV-6A/6B) in telomeres. We exploited a hypervariable region of the HHV- 6B genome to investigate the relationship between acquired and inherited virus and revealed predominantly maternal transmission of acqHHV-6B in families. Remarkably, we demonstrate that some copies of acqHHV-6B in saliva from healthy adults gained a telomere, indicative of integration and latency, and that the frequency of viral genome excision from telomeres in iciHHV-6B carriers is surprisingly high and varies between tissues. In addition, newly formed short telomeres generated by partial viral genome release are frequently lengthened, particularly in telomerase-expressing pluripotent cells. Consequently, iciHHV-6B carriers are mosaic for different iciHHV-6B structures, including circular extra-chromosomal forms that have the potential to reactivate. Finally, we show transmission of an HHV-6B strain from an iciHHV-6B mother to her non-iciHHV-6B son. Altogether we demonstrate that iciHHV-6B can readily transition between telomere-integrated and free virus forms.
Detection of Harassment Type of Cyberbullying: A Dictionary of Approach Words and Its Impact

(e purpose of this paper is to analyse the effects of predatory approach words in the detection of cyberbullying and to propose a mechanism of generating a dictionary of such approach words. (e research incorporates analysis of chat logs from convicted felons, to generate a dictionary of sexual approach words. By analysing data across multiple social networks, the study demonstrates the usefulness of such a dictionary of approach words in detection of online predatory behaviour through machine learning algorithms. It also shows the difference between the nature of contents across specific social network platforms. (e proposed solution to detect cyberbullying and the domain of approach words are scalable to fit reallife social media, which can have a positive impact on the overall health of online social networks. Different types of cyberbullying have different characteristics. However, existing cyberbullying detection works are not targeted towards any of these specific types. (is research is tailored to focus on sexual harassment type of cyberbullying and proposes a novel dictionary of approach words. Since cyberbullying is a growing threat to the mental health and intellectual development of adolescents in the society, models targeted towards the detection of specific type of online bullying or predation should be encouraged among social network researchers.
PurExt: Automated Extraction of the Purpose-Aware Rule from the Natural Language Privacy Policy in IoT

The extensive data collection performed by the Internet of Things (IoT) devices can put users at risk of data leakage. Consequently, IoT vendors are legally obliged to provide privacy policies to declare the scope and purpose of the data collection. However, complex and lengthy privacy policies are unfriendly to users, and the lack of a machine-readable format makes it difficult to check policy compliance automatically. To solve these problems, we first put forward a purpose-aware rule to formalize the purpose-driven data collection or use statement. Then, a novel approach to identify the rule from natural language privacy policies is proposed. To address the issue of diversity of purpose expression, we present the concepts of explicit and implicit purpose, which enable using the syntactic and semantic analyses to extract purposes in different sentences. Finally, the domain adaption method is applied to the semantic role labeling (SRL) model to improve the efficiency of purpose extraction. The experiments that are conducted on the manually annotated dataset demonstrate that this approach can extract purpose-aware rules from the privacy policies with a high recall rate of 91%. The implicit purpose extraction of the adapted model significantly improves the F1-score by 11%.
CFA: A New Family of Hybrid CA-Based PRNGs

In this article, a new Pseudorandom Number Generator (PRNG) construction is proposed. It is based on cellular automata (CAs) and comprises other cryptographic primitives organized as blocks. Each of these blocks has a purpose and serves toward obtaining a higher level of randomness. The construction described is modular, and each of its blocks can be replaced, modified, or adapted according to the user, the application, or the level of randomness required. The authors first describe a general structure and the design principles behind each of the components. Next, a concrete example using the SHA-3 hash function, a hybrid cellular automaton, and the AES block cipher is provided. Then, the security analysis and the statistical properties for this specific instance of the scheme are presented.
Genome-Wide Histone H3K27 Acetylation Profiling Identified Genes Correlated With Prognosis in Papillary Thyroid Carcinoma

Thyroid carcinoma (TC) is the most common endocrine malignancy, and papillary TC (PTC) is the most frequent subtype of TC, accounting for 85–90% of all the cases. Aberrant histone acetylation contributes to carcinogenesis by inducing the dysregulation of certain cancer-related genes. However, the histone acetylation landscape in PTC remains elusive. Here, we interrogated the epigenomes of PTC and benign thyroid nodule (BTN) tissues by applying H3K27ac chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) along with RNA-sequencing. By comparing the epigenomic features between PTC and BTN, we detected changes in H3K27ac levels at active regulatory regions, identified PTC-specific super-enhancer-associated genes involving immune-response and cancer-related pathways, and uncovered several genes that associated with disease-free survival of PTC. In summary, our data provided a genome-wide landscape of histone modification in PTC and demonstrated the role of enhancers in transcriptional regulations associated with prognosis of PTC.
Features of Neutrophils From Atopic and Non-Atopic Elite Endurance Runners

We collected peripheral blood from thirty-nine elite male endurance runners at rest (24 hours after the last exercise session) and used the Allergy Questionnaire for Athletes score and plasma specific IgE level to separate them into atopic and non-atopic athletes. Neutrophils obtained from atopic and non-atopic athletes were subsequently stimulated in vitro with fMLP (N-formyl-methionyl-leucyl-phenylalanine), LPS (lipopolysaccharide), or PMA (phorbol 12-myristate 13-acetate). Neutrophils from non-atopic runners responded appropriately to LPS, as evidenced by the production of pro (IL-8, TNF-α, and IL-6) and anti-inflammatory (IL-10) cytokines. Neutrophils from atopic elite runners exhibited lower responses to LPS stimulus as indicated by no increase in IL-1β, TNF-α, and IL-6 production. Neutrophils from non-atopic and atopic runners responded similarly to fMLP stimulation, indicating that migration function remained unaltered. Both groups were unresponsive to PMA induced reactive oxygen species (ROS) production. Training hours and training volume were not associated with neutrophil IgE receptor gene expression or any evaluated neutrophil function. Since non-atopic runners normally responded to LPS stimulation, the reduced neutrophil response to the stimuli was most likely due to the atopic state and not exercise training. The findings reported are of clinical relevance because atopic runners exhibit a constant decline in competition performance and are more susceptible to invading microorganisms.
Self-Referential Processing Effects of Non-invasive Brain Stimulation: A Systematic Review

Systematic reviews of neuroimaging studies confirm stimulus-induced activity in response to verbal and non-verbal self-referential processing (SRP) in cortical midline structures, temporoparietal cortex and insula. Whether SRP can be causally modulated by way of non-invasive brain stimulation (NIBS) has also been investigated in several studies. Here we summarize the NIBS literature including 27 studies of task-based SRP comparing response between verbal and non-verbal SRP tasks. The studies differed in design, experimental tasks and stimulation parameters. Results support the role of left inferior parietal lobule (left IPL) in verbal SRP and for the medial prefrontal cortex when valenced stimuli were used. Further, results support roles for the bilateral parietal lobe (IPL, posterior cingulate cortex), the sensorimotor areas (the primary sensory and motor cortex, the premotor cortex, and the extrastriate body area) and the insula in non-verbal SRP (bodily self-consciousness). We conclude that NIBS may differentially modulate verbal and non-verbal SRP by targeting the corresponding brain areas.
Whole-Exome Sequencing Analysis of Human Semen Quality in Russian Multiethnic Population

The global trend toward the reduction of human spermatogenic function observed in many countries, including Russia, raised the problem of extensive screening and monitoring of male fertility and elucidation of its genetic and ethnic mechanisms. Recently, whole-exome sequencing (WES) was developed as a powerful tool for genetic analysis of complex traits. We present here the first Russian WES study for identification of new genes associated with semen quality. The experimental 3 × 2 design of the WES study was based on the analysis of 157 samples including three ethnic groups—Slavs (59), Buryats (n = 49), and Yakuts (n = 49), and two different semen quality groups—pathozoospermia (n = 95) and normospermia (n = 62). Additionally, our WES study group was negative for complete AZF microdeletions of the Y-chromosome. The normospermia group included men with normal sperm parameters in accordance with the WHO-recommended reference limit. The pathozoospermia group included men with impaired semen quality, namely, with any combined parameters of sperm concentration <15 × 106/ml, and/or progressive motility <32%, and/or normal morphology <4%. The WES was performed for all 157 samples. Subsequent calling and filtering of variants were carried out according to the GATK Best Practices recommendations. On the genotyping stage, the samples were combined into four cohorts: three sets corresponded to three ethnic groups, and the fourth set contained all the 157 whole-exome samples. Association of the obtained polymorphisms with semen quality parameters was investigated using the χ2 test. To prioritize the obtained variants associated with pathozoospermia, their effects were determined using Ensembl Variant Effect Predictor. Moreover, polymorphisms located in genes expressed in the testis were revealed based on the genomic annotation. As a result, the nine potential SNP markers rs6971091, rs557806, rs610308, rs556052, rs1289658, rs278981, rs1129172, rs12268007, and rs17228441 were selected for subsequent verification on our previously collected population sample (about 1,500 males). The selected variants located in seven genes FAM71F1, PPP1R15A, TRIM45, PRAME, RBM47, WDFY4, and FSIP2 that are expressed in the testis and play an important role in cell proliferation, meiosis, and apoptosis.
A Comparative Study on the Predictive Value of Different Resting-State Functional Magnetic Resonance Imaging Parameters in Preclinical Alzheimer's Disease

Objective: Diverse resting-state functional magnetic resonance imaging (rs-fMRI) studies showed that rs-fMRI might be able to reflect the earliest detrimental effect of cerebral beta-amyloid (Aβ) pathology. However, no previous studies specifically compared the predictive value of different rs-fMRI parameters in preclinical AD. Methods: A total of 106 cognitively normal adults (Aβ+ group = 66 and Aβ− group = 40) were included. Three different rs-fMRI parameter maps including functional connectivity (FC), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) were calculated. Receiver operating characteristic (ROC) curve analyses were utilized to compare classification performance of the three rs-fMRI parameters. Results: FC maps showed the best classifying performance in ROC curve analysis (AUC, 0.915, p < 0.001). Good but weaker performance was achieved by using ReHo maps (AUC, 0.836, p < 0.001) and fALFF maps (AUC, 0.804, p < 0.001). The brain regions showing the greatest discriminative power included the left angular gyrus for FC, left anterior cingulate for ReHo, and left middle frontal gyrus for fALFF. However, among the three measurements, ROI-based FC was the only measure showing group difference in voxel-wise analysis. Conclusion: Our results strengthen the idea that rs-fMRI might be sensitive to earlier changes in spontaneous brain activity and FC in response to cerebral Aβ retention. However, further longitudinal studies with larger sample sizes are needed to confirm their utility in predicting the risk of AD.
Bioreactor Suspension Culture: Differentiation and Production of Cardiomyocyte Spheroids From Human Induced Pluripotent Stem Cells

Human induced-pluripotent stem cells (hiPSCs) can be efficiently differentiated into cardiomyocytes (hiPSC-CMs) via the GiWi method, which uses small-molecule inhibitors of glycogen synthase kinase (GSK) and tankyrase to first activate and then suppress Wnt signaling. However, this method is typically conducted in 6-well culture plates with two-dimensional (2D) cell sheets, and consequently, cannot be easily scaled to produce the large numbers of hiPSC-CMs needed for clinical applications. Cell suspensions are more suitable than 2D systems for commercial biomanufacturing, and suspended hiPSCs form free-floating aggregates (i.e., spheroids) that can also be differentiated into hiPSC-CMs. Here, we introduce a protocol for differentiating suspensions of hiPSC spheroids into cardiomyocytes that is based on the GiWi method. After optimization based on cardiac troponin T staining, the purity of hiPSC-CMs differentiated via our novel protocol exceeded 98% with yields of about 1.5 million hiPSC-CMs/mL and less between-batch purity variability than hiPSC-CMs produced in 2D cultures; furthermore, the culture volume could be increased ∼10-fold to 30 mL with no need for re-optimization, which suggests that this method can serve as a framework for large-scale hiPSC-CM production.
Neonatal Sepsis: The Impact of Carbapenem-Resistant and Hypervirulent Klebsiella pneumoniae

The convergence of a vulnerable population and a notorious pathogen is devastating, as seen in the case of sepsis occurring during the first 28 days of life (neonatal period). Sepsis leads to mortality, particularly in low-income countries (LICs) and lower-middle-income countries (LMICs). Klebsiella pneumoniae, an opportunistic pathogen is a leading cause of neonatal sepsis. The success of K. pneumoniae as a pathogen can be attributed to its multidrug-resistance and hypervirulent-pathotype. Though the WHO still recommends ampicillin and gentamicin for the treatment of neonatal sepsis, K. pneumoniae is rapidly becoming untreatable in this susceptible population. With escalating rates of cephalosporin use in health-care settings, the increasing dependency on carbapenems, a “last resort antibiotic,” has led to the emergence of carbapenem-resistant K. pneumoniae (CRKP). CRKP is reported from around the world causing outbreaks of neonatal infections. Carbapenem resistance in CRKP is largely mediated by highly transmissible plasmid-encoded carbapenemase enzymes, including KPC, NDM, and OXA-48-like enzymes. Further, the emergence of a more invasive and highly pathogenic hypervirulent K. pneumoniae (hvKP) pathotype in the clinical context poses an additional challenge to the clinicians. The deadly package of resistance and virulence has already limited therapeutic options in neonates with a compromised defense system. Although there are reports of CRKP infections, a review on neonatal sepsis due to CRKP/ hvKP is scarce. Here, we discuss the current understanding of neonatal sepsis with a focus on the global impact of the CRKP, provide a perspective regarding the possible acquisition and transmission of the CRKP and/or hvKP in neonates, and present strategies to effectively identify and combat these organisms.
Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.
Leaf Counting: Fusing Network Components for Improved Accuracy

Leaf counting in potted plants is an important building block for estimating their health status and growth rate and has obtained increasing attention from the visual phenotyping community in recent years. Two novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study. The first method performs counting via direct regression but using multiple image representation resolutions to attend leaves of multiple scales. The leaf count from multiple resolutions is fused using a novel technique to get the final count. The second method is detection with a regression model that counts the leaves after locating leaf center points and aggregating them. The algorithms are evaluated on the Leaf Counting Challenge (LCC) dataset of the Computer Vision Problems in Plant Phenotyping (CVPPP) conference 2017, and a new larger dataset of banana leaves. Experimental results show that both methods outperform previous CVPPP LCC challenge winners, based on the challenge evaluation metrics, and place this study as the state of the art in leaf counting. The detection with regression method is found to be preferable for larger datasets when the center-dot annotation is available, and it also enables leaf center localization with a 0.94 average precision. When such annotations are not available, the multiple scale regression model is a good option.
Proerythroblast Cells of Diamond-Blackfan Anemia Patients With RPS19 and CECR1 Mutations Have Similar Transcriptomic Signature

Diamond Blackfan Anemia (DBA) is an inherited bone marrow (BM) failure syndrome, characterized by a paucity of erythroid differentiation. DBA is mainly caused by the mutations in ribosomal protein genes, hence classified as ribosomopathy. However, in approximately 30% of patients, the molecular etiology cannot be discovered. RPS19 germline mutations caused 25% of the cases. On the other hand, CECR1 mutations also cause phenotypes similar to DBA but not being a ribosomopathy. Due to the blockade of erythropoiesis in the BM, we investigated the transcriptomic profile of three different cell types of BM resident cells of DBA patients and compared them with healthy donors. From BM aspirates BM mononuclear cells (MNCs) were isolated and hematopoietic stem cells (HSC) [CD71–CD34+ CD38mo/lo], megakaryocyte–erythroid progenitor cells (MEP) [CD71–CD34+ CD38hi] and Proerythroblasts [CD71+ CD117+ CD38+] were sorted and analyzed with a transcriptomic approach. Among all these cells, proerythroblasts had the most different transcriptomic profile. The genes associated with cellular stress/immune responses were increased and some of the transcription factors that play a role in erythroid differentiation had altered expression in DBA proerythroblasts. We also showed that gene expression levels of ribosomal proteins were decreased in DBA proerythroblasts. In addition to these, colony formation assay (CFU-E) provided functional evidence of the failure of erythroid differentiation in DBA patients. According to our findings that all patients resembling both RPS19 and CECR1 mutations have common transcriptomic signatures, it may be possible that inflammatory BM niche may have a role in DBA pathogenesis.