Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Channel Electrocardiogram QRS Detection by Temporal Pattern Search

A highly constrained temporal pattern search (“TEPS”) based multiple channel heartbeat detector is described. TEPS generates sequences of peaks and statistically scores them according to: 1) peak time coherence across channels; 2) peak prominence; 3) temporal regularity; and 4) number of skipped beats. TEPS was tested on 31 records of three channel capacitive electrode data from the UnoViS automobile database. TEPS showed a sensitivity (SE) of 91.3% and a false discovery rate (FDR) of 3.0% compared to an SE and FDR of 75.3% and 65.0% respectively for a conventional single channel detector (OSEA) applied separately to the three channels. The peak matching window was 30ms. The percentage of 5 second segments with average heart rates within 5 beats/minute of reference was also measured. In 6 of the 31 records, TEPS’ percentage was at least 30% greater than OSEA’s. TEPS was also applied to synthetic data comprising a known signal corrupted with calibrated amounts of noise. At a fixed SE of 85%, increasing the number of channels from one to two resulted in an improvement of approximately 5dB in noise resistance, while increasing the number of channels from two to four resulted in an improvement of approximately 3dB in noise resistance. The quantification of noise resistance as a function of the number of channels could help guide the development of wearable electrocardiogram monitors.
Optimization of Non-Coding Regions Improves Protective Efficacy of an mRNA SARS-CoV-2 Vaccine in Nonhuman Primates

The CVnCoV (CureVac) mRNA vaccine for SARS-CoV-2 has recently been evaluated in a phase 2b/3 efficacy trial in humans. CV2CoV is a second-generation mRNA vaccine with optimized non-coding regions and enhanced antigen expression. Here we report a head-to-head study of the immunogenicity and protective efficacy of CVnCoV and CV2CoV in nonhuman primates. We immunized 18 cynomolgus macaques with two doses of 12 ug of lipid nanoparticle formulated CVnCoV, CV2CoV, or sham (N=6/group). CV2CoV induced substantially higher binding and neutralizing antibodies, memory B cell responses, and T cell responses as compared with CVnCoV. CV2CoV also induced more potent neutralizing antibody responses against SARS-CoV-2 variants, including B.1. 351 (beta), B.1. 617.2 (delta), and C.37 (lambda). While CVnCoV provided partial protection against SARS-CoV-2 challenge, CV2CoV afforded robust protection with markedly lower viral loads in the upper and lower respiratory tract. Antibody responses correlated with protective efficacy. These data demonstrate that optimization of non-coding regions can greatly improve the immunogenicity and protective efficacy of an mRNA SARS-CoV-2 vaccine in nonhuman primates.
Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

Infectious diseases, including those of viral, bacterial, fungal, and parasitic origin are often characterized by focal inflammation occurring in one or more distinct tissues. Tissue-specific outcomes of infection are also evident in many infectious diseases, suggesting that the local microenvironment may instruct complex and diverse innate and adaptive cellular responses resulting in locally distinct molecular signatures. In turn, these molecular signatures may both drive and be responsive to local metabolic changes in immune as well as non-immune cells, ultimately shaping the outcome of infection. Given the spatial complexity of immune and inflammatory responses during infection, it is evident that understanding the spatial organization of transcripts, proteins, lipids, and metabolites is pivotal to delineating the underlying regulation of local immunity. Molecular imaging techniques like mass spectrometry imaging and spatially resolved, highly multiplexed immunohistochemistry and transcriptomics can define detailed metabolic signatures at the microenvironmental level. Moreover, a successful complementation of these two imaging techniques would allow multi-omics analyses of inflammatory microenvironments to facilitate understanding of disease pathogenesis and identify novel targets for therapeutic intervention. Here, we describe strategies for downstream data analysis of spatially resolved multi-omics data and, using leishmaniasis as an exemplar, describe how such analysis can be applied in a disease-specific context.
Transcriptional Profiling of Human Peripheral Blood Mononuclear Cells Stimulated by Mycobacterium tuberculosis PPE57 Identifies Characteristic Genes Associated With Type I Interferon Signaling

Proline-glutamic acid (PE)- and proline-proline-glutamic acid (PPE)-containing proteins are exclusive to Mycobacterium tuberculosis (MTB), the leading cause of tuberculosis (TB). In this study, we performed global transcriptome sequencing (RNA-Seq) on PPE57-stimulated peripheral blood mononuclear cells (PBMCs) and control samples to quantitatively measure the expression level of key transcripts of interest. A total of 1367 differentially expressed genes (DEGs) were observed in response to a 6 h exposure to PPE57, with 685 being up-regulated and 682 down-regulated. Immune-related gene functions and pathways associated with these genes were evaluated, revealing that the type I IFN signaling pathway was the most significantly enriched pathway in our RNA-seq dataset, with 14 DEGs identified therein including ISG15, MX2, IRF9, IFIT3, IFIT2, OAS3, IFIT1, IFI6, OAS2, OASL, RSAD2, OAS1, IRF7, and MX1. These PPE57-related transcriptomic profiles have implications for a better understanding of host global immune mechanisms underlying MTB infection outcomes. However, more studies regarding these DEGs and type I IFN signaling in this infectious context are necessary to more fully clarify the underlying mechanisms that arise in response to PPE57 during MTB infection.
Research on the Parametric Modelling Approach of Vortex Generator on Wind Turbine Airfoil

Counter-rotating vortex generators (VGs) are typically employed to delay airflow separation on wind turbine blades. Large-size wind turbine blades equipped with small size VGs make the computational fluid dynamics (CFD) researches require a great deal of computational resources. Parametric models of VGs can effectively improve the numerical research efficiency of wind turbine blades with VGs. In order to improve the accuracy of such parametric models, this study proposed a series of modeling approaches to determine the positions of the adding source term in Cartesian coordinates, the VG vortex core radius, etc., on the wind turbine airfoils. These techniques are integrated with a maximum circulation algorithm by considering the interactions between VG pairs to predict the performance of a DU91-W2-250 blade section with VGs. The proposed parametric model and an entity model at different angles of attack (AoAs) are implemented on the blade section. Our approach is validated using experimental data. Comparisons demonstrate a strong agreement between the modelled and experimental results, proving the high accuracy of the two models. The numerical results of the models are then compared and analyzed at different incoming flow velocities and AoAs to verify the universality of the proposed parametric approaches. The results reveal a high consistency between the vortex structure, the velocity profile above the blade surface and the distribution of the pressure coefficient calculated by the two models. This proves the high universality of the proposed approaches and demonstrates the potential of the parametric model in replacing the VG entity model. The VG parametric model expresses VG parameters by program, which can improve the research efficiency of VG arrangement on wind turbine blades.
Coastal Ecosystems as Sources of Biofertilizers in Agriculture: From Genomics to Application in an Urban Orchard

Pantoea agglomerans RSO7, a rhizobacterium previously isolated from Spartina maritima grown on metal polluted saltmarshes, had demonstrated good plant growth promoting activity for its host halophyte, but was never tested in crops. The aims of this study were: (1) testing PGP activity on a model plant (alfalfa) in vitro; (2) testing a bacterial consortium including RSO7 as biofertilizer in a pilot experiment in urban orchard; and (3) identifying the traits related to PGP activities. RSO7 was able to enhance alfalfa growth in vitro, particularly the root system, besides improving plant survival and protecting plants against fungal contamination. In addition, in a pilot experiment in urban orchard, a consortium of three bacteria including RSO7 was able to foster the growth and yield of several winter crops between 1.5 and 10 fold, depending on species. Moreover, the analysis of chlorophyll fluorescence revealed that photosynthesis was highly ameliorated. Genome analysis of RSO7 depicted the robustness of this bacterial strain which showed resilience to multiple stresses (heat, cold, UV radiation, several xenobiotics). Together with wide metabolic versatility, genes conferring resistance to oxidative stress were identified. Many genes involved in metal resistance (As, Cu, Ni, Co, Zn, Se, Te) and in tolerance toward high osmolality (production of a battery of osmoprotectans) were also found. Regarding plant growth promoting properties, traits for phosphate solubilization, synthesis of a battery of siderophores and production of IAA were detected. In addition, the bacterium has genes related to key processes in the rhizosphere including flagellar motility, chemotaxis, quorum sensing, biofilm formation, plant-bacteria dialog, and high competitiveness in the rhizosphere. Our results suggest the high potential of this bacterium as bioinoculant for an array of crops. However, the classification in biosecurity group 2 prevents its use according to current European regulation. Alternative formulations for the application of the bioinoculant are discussed.
Confronting Missing Ecological Data in the Age of Pandemic Lockdown

The COVID-19 pandemic profoundly affected research in ecology and evolution, with lockdowns resulting in the suspension of most research programs and creating gaps in many ecological datasets. Likewise, monitoring efforts directed either at tracking trends in natural systems or documenting the environmental impacts of anthropogenic activities were largely curtailed. In addition, lockdowns have affected human activity in natural environments in ways that impact the systems under investigation, rendering many widely used approaches for handling missing data (e.g., available case analysis, mean substitution) inadequate. Failure to properly address missing data will lead to bias and weak inference. Researchers and environmental monitors must ensure that lost data are handled robustly by diagnosing patterns and mechanisms of missingness and applying appropriate tools like multiple imputation, full-information maximum likelihood, or Bayesian approaches. The pandemic has altered many aspects of society and it is timely that we critically reassess how we treat missing data in ecological research and environmental monitoring, and plan future data collection to ensure robust inference when faced with missing data. These efforts will help ensure the integrity of inference derived from datasets spanning the COVID-19 lockdown and beyond.
The Role of LINC00284 in the Development of Thyroid Cancer via Its Regulation of the MicroRNA-30d-5p-Mediated ADAM12/Notch Axis

Thyroid cancer is a commonly diagnosed endocrine malignancy with increasing incidence worldwide. Long noncoding RNAs (lncRNAs) are known to function in the invasion and metastasis of thyroid cancer. According to the GSE66783 microarray dataset, long intergenic nonprotein coding RNA 284 (LINC00284) is aberrantly upregulated in thyroid cancer tissues. However, information regarding the specific role of LINC00284 in thyroid cancer remains elusive. Therefore, the current study set out to determine the role of LINC00284 in the development of thyroid cancer, along with an investigation of the underlying molecular mechanism. In parallel with the microarray data from GSE66783, LINC00284 was observed to be expressed at high levels in thyroid cancer cell lines. Moreover, loss-of-function experiments revealed that the downregulation of LINC00284 reduced aldehyde dehydrogenase (ALDH) activity and thyroid cancer cell proliferation, colony formation, and invasiveness, which promoted cell apoptosis. Mechanistically, using dual-luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays, LINC00284 was identified to competitively bind to microRNA-30d-5p (miR-30d-5p), which was observed to be expressed at low levels in thyroid cancer tissues and cells and directly targets the oncogene a disintegrin and metalloproteinase 12 (ADAM12). Overexpression of miR-30d-5p exerted tumor-suppressive effects on the malignant activity of thyroid cancer cells, changes that were reversed by LINC00284 overexpression or ADAM12 overexpression. Furthermore, LINC00284 activated the Notch signaling pathway by competitively binding to miR-30d-5p and increasing the expression of ADAM12. Finally, by performing in vivo experiments, we found that LINC00284 silencing or miR-30d-5p overexpression suppressed the tumorigenic ability of thyroid cancer cells and that overexpression of miR-30d-5p inhibited the LINC00284-induced tumorigenesis of thyroid cancer cells. Collectively, our findings indicate that LINC00284 competitively binds to miR-30d-5p and activates the ADAM12-dependent Notch signaling pathway, thereby promoting the development of thyroid cancer.
Multi-Transcript Level Profiling Revealed Distinct mRNA, miRNA, and tRNA-Derived Fragment Bio-Signatures for Coping Behavior Linked Haplotypes in HPA Axis and Limbic System

The molecular basis of porcine coping behavior (CB) relies on a sophisticated interplay of genetic and epigenetic features. Deep sequencing technologies allowed the identification of a plethora of new regulatory small non-coding RNA (sncRNA). We characterized mRNA and sncRNA profiles of central parts of the physiological stress response system including amygdala, hippocampus, hypothalamus and adrenal gland using systems biology for integration. Therefore, ten each of high- (HR) and low- (LR) reactive pigs (n = 20) carrying a CB associated haplotype in a prominent QTL-region on SSC12 were selected for mRNA and sncRNA expression profiling. The molecular markers related to the LR group included ATP1B2, MPDU1, miR-19b-5p, let-7g-5p, and 5′-tiRNALeu in the adrenal gland, miR-194a-5p, miR-125a-5p, miR-7-1-5p, and miR-107-5p in the hippocampus and CBL and PVRL1 in the hypothalamus. Interestingly, amygdalae of the LR group showed 5′-tiRNA and 5′-tRF (5′-tRFLys, 5′-tiRNALys, 5′-tiRNACys, and 5′-tiRNAGln) enrichment. Contrarily, molecular markers associated with the HR group encompassed miR-26b-5p, tRNAArg, tRNAGlyiF in the adrenal gland, IGF1 and APOD in the amygdala and PBX1, TOB1, and C18orf1 in the hippocampus and miR-24 in the hypothalamus. In addition, hypothalami of the HR group were characterized by 3′-tiRNA enrichment (3′-tiRNAGln, 3′-tiRNAAsn, 3′-tiRNAVal, 3′-tRFPro, 3′-tiRNACys, and 3′-tiRNAAla) and 3′-tRFs enrichment (3′-tRFAsn, 3′-tRFGlu, and 3′-tRFVal). These evidence suggest that tRNA-derived fragments and their cleavage activity are a specific marker for coping behavior. Data integration revealed new bio-signatures of important molecular interactions on a multi-transcript level in HPA axis and limbic system of pigs carrying a CB-associated haplotype.
Genome-Wide Characterization and Expression Analysis Provide Basis to the Biological Function of Cotton FBA Genes

Fructose-1,6-biphosphate aldolase (FBA) is a multifunctional enzyme in plants, which participates in the process of Calvin-Benson cycle, glycolysis and gluconeogenesis. Despite the importance of FBA genes in regulating plant growth, development and abiotic stress responses, little is known about their roles in cotton. In the present study, we performed a genome-wide identification and characterization of FBAs in Gossypium hirsutum. Totally seventeen GhFBA genes were identified. According to the analysis of functional domain, phylogenetic relationship, and gene structure, GhFBA genes were classified into two subgroups. Furthermore, nine GhFBAs were predicted to be in chloroplast and eight were located in cytoplasm. Moreover, the promoter prediction showed a variety of abiotic stresses and phytohormone related cis-acting elements exist in the 2k up-stream region of GhFBA. And the evolutionary characteristics of cotton FBA genes were clearly presented by synteny analysis. Moreover, the results of transcriptome and qRT-PCR analysis showed that the expression of GhFBAs were related to the tissue distribution, and further analysis suggested that GhFBAs could respond to various abiotic stress and phytohormonal treatments. Overall, our systematic analysis of GhFBA genes would not only provide a basis for the understanding of the evolution of GhFBAs, but also found a foundation for the further function analysis of GhFBAs to improve cotton yield and environmental adaptability.
Evolution of Supramolecular Systems Towards Next-Generation Biosensors

Supramolecular materials, which rely on dynamic non-covalent interactions, present a promising approach to advance the capabilities of currently available biosensors. The weak interactions between supramolecular monomers allow for adaptivity and responsiveness of supramolecular or self-assembling systems to external stimuli. In many cases, these characteristics improve the performance of recognition units, reporters, or signal transducers of biosensors. The facile methods for preparing supramolecular materials also allow for straightforward ways to combine them with other functional materials and create multicomponent sensors. To date, biosensors with supramolecular components are capable of not only detecting target analytes based on known ligand affinity or specific host-guest interactions, but can also be used for more complex structural detection such as chiral sensing. In this Review, we discuss the advancements in the area of biosensors, with a particular highlight on the designs of supramolecular materials employed in analytical applications over the years. We will first describe how different types of supramolecular components are currently used as recognition or reporter units for biosensors. The working mechanisms of detection and signal transduction by supramolecular systems will be presented, as well as the important hierarchical characteristics from the monomers to assemblies that contribute to selectivity and sensitivity. We will then examine how supramolecular materials are currently integrated in different types of biosensing platforms. Emerging trends and perspectives will be outlined, specifically for exploring new design and platforms that may bring supramolecular sensors a step closer towards practical use for multiplexed or differential sensing, higher throughput operations, real-time monitoring, reporting of biological function, as well as for environmental studies.
The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model

While many studies have shown that toddlers are able to detect syntactic regularities in speech, the learning mechanism allowing them to do this is still largely unclear. In this article, we use computational modeling to assess the plausibility of a context-based learning mechanism for the acquisition of nouns and verbs. We hypothesize that infants can assign basic semantic features, such as “is-an-object” and/or “is-an-action,” to the very first words they learn, then use these words, the semantic seed, to ground proto-categories of nouns and verbs. The contexts in which these words occur, would then be exploited to bootstrap the noun and verb categories: unknown words are attributed to the class that has been observed most frequently in the corresponding context. To test our hypothesis, we designed a series of computational experiments which used French corpora of child-directed speech and different sizes of semantic seed. We partitioned these corpora in training and test sets: the model extracted the two-word contexts of the seed from the training sets, then used them to predict the syntactic category of content words from the test sets. This very simple algorithm demonstrated to be highly efficient in a categorization task: even the smallest semantic seed (only 8 nouns and 1 verb known) yields a very high precision (~90% of new nouns; ~80% of new verbs). Recall, in contrast, was low for small seeds, and increased with the seed size. Interestingly, we observed that the contexts used most often by the model featured function words, which is in line with what we know about infants' language development. Crucially, for the learning method we evaluated here, all initialization hypotheses are plausible and fit the developmental literature (semantic seed and ability to analyse contexts). While this experiment cannot prove that this learning mechanism is indeed used by infants, it demonstrates the feasibility of a realistic learning hypothesis, by using an algorithm that relies on very little computational and memory resources. Altogether, this supports the idea that a probabilistic, context-based mechanism can be very efficient for the acquisition of syntactic categories in infants.
Association of Plasma Metabolic Biomarker Sphingosine-1-Phosphate With Cerebral Collateral Circulation in Acute Ischemic Stroke

Background: The contribution of metabolic profile to the cerebral collateral circulation in acute ischemic stroke (AIS) has not been fully outlined. In this study, we conducted a metabolomic study to assess the relationship between the metabolic biomarkers and the collateral status of AIS. Methods: A two-stage study was conducted from September 2019 to June 2021 in our hospital. There were 96 subjects including 66 patients with AIS and 30 healthy controls in the discovery stage and 80 subjects including 53 patients with AIS and 27 healthy controls in the validation stage. Collateral circulation was assessed by the Tan score based on computed tomographic angiography (CTA). Liquid chromatography-tandem mass spectrometry was used to identify differential metabolic markers. Then, an ELISA was employed to detect the plasma levels of sphingosine-1-phosphate (S1P). Results:There were 114 differential metabolites between patients with AIS and control groups and 37 differential metabolites between good collateral circulation (GCC) and poor collateral circulation (PCC) groups. The pathway enrichment analysis revealed that arginine biosynthesis was the only statistically significant pathway between AIS and control groups and sphingolipid metabolism was the only statistically significant pathway between GCC and PCC groups. The differential metabolites sphinganine-1-phosphate (SA1P) and S1P belong to the sphingolipid metabolism. In the discovery stage, when the GCC group was compared with the PCC group, the receiver operating characteristic (ROC) analysis showed that plasma SA1P relative levels demonstrated an area under the curve (AUC) of 0.719 (95% CI: 0.582–0.834), and S1P levels demonstrated an AUC of 0.701 (95% CI: 0.567–0.819). In addition, both plasma SA1P and S1P relative levels showed significant negative correlations with the 90-day modified Rankin Scale (mRS) score. In the validation sample, higher plasma S1P levels were independent predictors of GCC (p = 0.014), and plasma S1P levels demonstrated an AUC of 0.738 (95% CI: 0.599–0.849) to differentiate patients with GCC from patients with PCC. In addition, plasma S1P levels also showed significant negative correlations with the 90-day mRS score. Conclusion: We first illustrated the association between plasma metabolic profiles and cerebral collateral circulation in patients with AIS. Plasma S1P levels might be a potential diagnostic biomarker for predicting collateral circulation status in patients with AIS.