Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of dissolved organic nitrogen to N leaching from four German agricultural soils

Dissolved organic nitrogen (DON) substantially contributes to N leaching from forest ecosystems. However, little is known about the role of DON for N leaching from agricultural soils. Therefore, the aim of our study was to quantify the contribution of DON to total N leaching from four agricultural soils. Concentrations and fluxes of DON and mineral N were monitored at two cropped sites (Plaggic Anthrosols) and two fallow plots (Plaggic Anthrosol and Gleyic Podzol) from November 1999 till May 2001 by means of glass suction plates. The experimental sites were located near the city of Munster, NW Germany. Median DON concentrations in 90 cm depth were 2.3 mg l—1 and 2.0 mg l—1 at the cropped sites and 1.6 mg l—1 and 1.3 mg l—1 at the fallow sites. There was only a slight (Anthrosols) or no (Gleyic Podzol) decrease in median DON concentrations with increasing depth. Total N seepage was between 19 kg N ha—1 yr—1 and 46 kg N ha—1 yr—1 at the fallow sites and 16—159 kg N ha—1 yr—1 at the cropped sites. For the fallow plots, DON seepage contributed 10—21 % to the total N flux (4—5 kg DON ha—1 yr—1), at the cropped sites DON seepage was 6—21 % of the total N flux (6—10 kg DON ha—1 yr—1). Thus, even in highly fertilized agricultural soils, DON is a considerable N carrier in seepage that should be considered in detailed soil N budgets.
Genomics of altitude-associated wing shape in two tropical butterflies

Understanding how organisms adapt to their local environment is central to evolution. With new whole-genome sequencing technologies and the explosion of data, deciphering the genomic basis of complex traits that are ecologically relevant is becoming increasingly feasible. Here we study the genomic basis of wing shape in two Neotropical butterflies that inhabit large geographical ranges. Heliconius butterflies at high elevations have been shown to generally have rounder wings than those in the lowlands. We reared over 1100 butterflies from 71 broods of H. erato and H. melpomene in common-garden conditions and show that wing aspect ratio, i.e. elongatedness, is highly heritable in both species and elevation-associated wing shape differences are maintained. Genome-wide associations with a published dataset of 666 whole genomes from across a hybrid zone, uncovered a highly polygenic basis to wing shape variation in the wild. We identify several genes that have roles in wing morphogenesis or wing shape variation in Drosophila flies, making them promising candidates for future studies. There was little evidence for molecular parallelism in the two species, with only one shared candidate gene, nor for a role of the four known colour pattern loci, except for optix in H. erato. Thus, we present the first insights into the heritability and genomic basis of within-species wing shape in two Heliconius species, adding to a growing body of evidence that polygenic adaptation may underlie many ecologically relevant traits.
Text mining-based word representations for biomedical data analysis and machine learning tasks

Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on breast cancer gene expression data to predict the occurrence of metastatic events. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed best for the metastatic event prediction task compared to other networks. Word representations as produced by text mining algorithms like word2vec, therefore capture biologically meaningful relations between entities.
Increased seed carbohydrate reserves associated with domestication influence the optimal seminal root number of Zea mays

Background and Aims Domesticated maize (Zea mays ssp. mays) generally forms between two and six seminal roots, while its wild ancestor, Mexican annual teosinte (Zea mays ssp. parviglumis), typically lacks seminal roots. Maize also produces larger seeds than teosinte, and it generally has higher growth rates as a seedling. Maize was originally domesticated in the tropical soils of southern Mexico, but it was later brought to the Mexican highlands before spreading to other parts of the continent, where it experienced different soil resource constraints. The aims of this study were to understand the impact of increased seminal root number on seedling nitrogen acquisition and to model how differences in maize and teosinte phenotypes might have contributed to increased seminal root number in domesticated maize. Methods Seedling root architectural models of a teosinte accession and a maize landrace were constructed by parameterizing the functional-structural plant model OpenSimRoot using plants grown in mesocosms. Seedling growth was simulated in a low-phosphorus environment, multiple low-nitrogen environments, and at variable planting densities. Models were also constructed to combine individual components of the maize and teosinte phenotypes. Key Results Seminal roots contributed about 35% of the nitrogen and phosphorus acquired by maize landrace seedlings in the first 25 days after planting. Increased seminal root number improved plant N acquisition under low-N environments with varying precipitation patterns, fertilization rates, soil textures, and planting densities. Models suggested that the optimal number of seminal roots for nutrient acquisition in teosinte is constrained by its limited seed carbohydrate reserves. Conclusions Seminal roots can improve the acquisition of both nitrogen and phosphorus in maize seedlings, and the increase in seed size associated with maize domestication may have facilitated increased seminal root number.
Spatial organization of the transcriptome in individual neurons

Neurons are highly polarized cells with complex neurite morphology. Spatial organization and local translation of RNAs in dendrites and axons play an important role in many neuronal functions. Here we performed super-resolution spatial profiling of RNAs inside individual neurons at the genome scale using multiplexed error-robust fluorescence in situ hybridization (MERFISH), and mapped the spatial organization of up to ∼4,200 RNA species (genes) across multiple length scales, ranging from sub-micrometer to millimeters. Our data generated a quantitative intra-neuronal atlas of RNAs with distinct transcriptome compositions in somata, dendrites, and axons, and revealed diverse sub-dendritic distribution patterns of RNAs. Moreover, our spatial analysis identified distinct groups of genes exhibiting specific spatial clustering of transcripts at the sub-micrometer scale that were dependent on protein synthesis and differentially dependent on synaptic activity. Overall, these data provide a rich resource for characterizing the subcellular organization of the transcriptome in neurons with high spatial resolution.
Sweet and magnetic: Succession and CAZyme expression of marine bacterial communities encountering a mix of alginate and pectin particles

Polysaccharide particles are an important nutrient source and microhabitat for marine bacteria. However, substrate-specific bacterial dynamics in a mixture of particle types with different polysaccharide composition, as likely occurring in natural habitats, are undescribed. Here, we studied the composition, functional diversity and gene expression of marine bacterial communities encountering a mix of alginate and pectin particles. Communities were collected above macroalgal forests near Helgoland Island − where polysaccharide-rich particles might regularly occur − and exposed to a mix of magnetic particles of each polysaccharide, allowing the targeted evaluation by particle type. Amplicon, metagenome and metatranscriptome sequencing revealed that particle-associated (PA) and free-living (FL) communities significantly differed in composition and metabolism, whereas dynamics on alginate and pectin particles were unexpectedly similar. Amplicon sequence variants (ASVs) from Tenacibaculum, Colwellia, Psychrobium and Psychromonas dominated the community on both particle types. Corresponding metagenome-assembled genomes (MAGs) expressed diverse alginate lyases, several co-localized in polysaccharide utilization loci. One low-abundance MAG related to Catenovulum showed pectin specialization through upregulated GH53 and GH105 genes. A single Glaciecola ASV dominated the FL fraction, likely persisting on particle-derived oligomers through different glycoside hydrolases. The bacterial preference for alginate, whereas pectin mainly served as colonization scaffold, illuminates substrate-driven microbial dynamics within mixed polysaccharide resources. Moreover, elevated ammonium metabolism signifies nitrogen availability as important factor on particles, whereas elevated methylcitrate and glyoxylate cycles illustrate nutrient-limited conditions in the surrounding water. These insights expand our understanding of bacterial microscale ecology, niche specialization and the biological carbon pump in macroalgae-rich habitats.
The effect of acidic pH on the interaction and lytic activity of MP1 and its H-MP1 analog in anionic lipid membrane: a biophysical study by Molecular Dynamics and Spectroscopy

Antimicrobial peptides (AMPs) are part of the innate immune system of many species and are compounds with potential application against the development of resistant bacterial strains promoted by conventional antibiotics. The AMPs are rich in cationic and hydrophobic residues and act directly on the lipidic phase of the cell membranes. The MP1 has a broad-spectrum bactericide activity in both Gram-negative and positive bacteria, not being hemolytic or cytotoxic. H-MP1 is a synthetic analog of MP1 with lysines replaced by histidines so that its net charge could be responsive to changes in solution pH. In the present work, we investigated the effect of the solution pH on the structural properties, in the adsorption and insertion, and on the lytic activity of these peptides in lipid bilayers mimicking the cell membrane of Gram-negative bacteria, using experimental and computational biophysical techniques. The results indicate that the lytic activity of H-MP1 is sensitive to pH, increasing to an acidic environment, matching that of MP1, which is not influenced by solution pH. Molecular Dynamic simulations indicated that the adsorption process of both peptides started by the interaction of the N-terminus with the bilayer, followed by the complete adsorption of the peptide laying parallel to the bilayer plane, inducing an increase in the peptide’s helical content enhancing peptides contact with the bilayer hydrophobic phase.
Wholeness Egg II: A Design Technique Applyed in Everyday Life

This paper introduces different applications of the 'Wholeness Egg' method in everyday life. It was originally invented by Iba as a way of designing workshops, based on Christopher Alexander's idea of wholeness and centers. Since we presented a paper 'Wholeness Egg- Methodology of Designing a 'Living' Workshop by Differentiating a Whole' in the EuroPLoP Conference 2018, its usage has been extended and getting utilized by people in everyday life situations. In this paper, we present the overview of the Wholeness Egg approach and then introduce two ways of application; life designing and reflection on experience. Showing some example cases, this paper concludes that this method supports people to conduct a design of everyday life, based on Alexander's idea of beauty in design, without asking them to know such a background theory. We believe that this technique can provide a good practical assistance to enhance the quality of our lives.
Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Compressive sensing (CS) is a novel paradigm to recover a sparse signal in compressed domain. In some overcomplete dictionaries, most practical signals are sparse rather than orthonormal. Signal space greedy method can derive the optimal or near-optimal projections, making it possible to identify a few most relevant dictionary atoms of an arbitrary signal. More practically, such projections can be processed by standard CS recovery algorithms. This paper proposes a signal space subspace pursuit (SSSP) method to compute spare signal representations with overcomplete dictionaries, whenever the sensing matrix satisfies the restricted isometry property adapted to dictionary (D-RIP). Specifically, theoretical guarantees were provided to recover the signals from their measurements with overwhelming probability, as long as the sensing matrix satisfies the D-RIP. In addition, a thorough analysis was performed to minimize the number of measurements required for such guarantees. Simulation results demonstrate the validity of our hypothetical theory, as well as the superiority of the proposed approach.
Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.
ghostwriter19 @ SardiStance: Generating New Tweets to Classify SardiStance EVALITA 2020 Political Tweets (short paper)

English. Understanding the events and the dominant thought is of great help to convey the desired message to our potential audience, be it marketing or political propaganda. Succeeding while the event is still ongoing is of vital importance to prepare alerts that require immediate action. A micro message platform like Twitter is the ideal place to be able to read a large amount of data linked to a theme and selfcategorized by its users using hashtags and mentions. In this research, I will show how a simple translator can be used to bring styles, vocabulary, grammar, and other characteristics to a common factor that leads each of us to be unique in the way we express ourselves. Italiano. Comprendere gli eventi e il pensiero dominante è di grande aiuto per veicolare alla nostra potenziale audience il messaggio desiderato sia esso di marketing o di propaganda politica. Riuscirci mentre l'evento è ancora in corso è di vitale importanza per predisporre alert che richiedono un intervento immediato. Una piattaforma di micro messaggi come Twitter è il luogo ideale per poter leggere una grande quantità di dati legata ad un tema, e spesso auto categorizzati dai suoi 1 Copyright ©️2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). stessi utenti per mezzo di hashtag e menzioni. In questa ricerca mostrerò come un semplice traduttore può essere usato per portare a fattor comune stili, lessico, grammatica e altre caratteristiche che portano ognuno di noi ad essere unico nel modo di esprimersi.
EXTRALINGUISTIC CONTEXT ROLES IN DETERMINING MEANINGS OF JAVANESE PHATIC EXPRESSION ‘MBOTEN’: A SOCIOPRAGMATIC PERSPECTIVE

The indirect expression of intention in the Javanese culture-based speech community requires the speaker and the hearer to understand the importance of contexts in communication. Failure to understand the context of interaction will definitely impede communication and interaction, or even worse it may cause misunderstanding in the communication and interaction process. In the Javanese speech community, people may say ‘ mboten ’ or ‘no’ without intending to negate or to express falsity. On the contrary, people may say ‘ inggih ’ or ‘yes’ which does not necessarily mean to affirm or to express an assertion. Therefore, it is clear that in the Javanese speech community, the extralinguistic contexts in the communication is very important and defines the purpose of utterances. Based on the research background, the research on extralinguistic contexts to determine the meaning of ‘ mboten ’ was carried out. The objective of the research was to elaborate the extralinguistic contexts which determine the meaning of ‘ mboten . ’ The data consists of excerpts containing Javanese phatic ‘ mboten ’. The technique of collecting data was recording and note-taking. Besides, interview or speaking method was employed to gather the data. The data analysis was done using the distributional and content analysis methods. This research results in five functions of extralinguistic contexts to determine the meaning of the utterance. The five functions are: (1) the extralinguistic contexts to affirm the intention of negation; (2) the extralinguistic context as the background of negation; (3) the extralinguistic contexts to confirm the meaning of negation; (4) the extralinguistic contexts to affirm the intention of negation; (5) the extralinguistic contexts to affirm the phatic function. The result of the research is very important and contributes significantly to the development of linguistics, especially the development of pragmatics embedded in culture-specific concepts. DOI : 10.24071/ijhs.2019. 030103
Intranasal administration of SARS-CoV-2 neutralizing human antibody prevents infection in mice

Prevention of SARS-CoV-2 infection at the point of nasal entry is a novel strategy that has the potential to help contain the ongoing pandemic. Using our proprietary technologies, we have engineered a human antibody that recognizes SARS-CoV-2 S1 spike protein with an enhanced affinity for mucin to improve the antibody’s retention in respiratory mucosa. The modified antibody, when administered into mouse nostrils, was shown to block infection in mice that were exposed to high titer SARS-CoV-2 pseudovirus 10 hours after the initial antibody treatment. Our data show that the protection against SARS-CoV-2 infection is effective in both nasal and lung areas 7 days after viral exposure. The modified antibody is stable in a nasal spray formulation and maintains its SARS-CoV-2 neutralizing activity. Nasal spray of the modified antibody can be developed as an affordable and effective prophylactic product to protect people from infection by exposure to SARS-CoV-2 virus in the air. One-sentence summary A Fc-modified human antibody prevents SARS-CoV-2 viral infection via nasal administration
A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly’s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.
Repression of MUC1 promotes expansion and suppressive function of myeloid-derived suppressor cells in pancreatic ductal adenocarcinoma and breast cancer murine models

Myeloid-derived suppressor cells (MDSCs) are immature myeloid cells that are responsible for immunosuppression in tumor microenvironment. Here we report the impact of mucin 1 (MUC1), a transmembrane glycoprotein, on proliferation and functional activity of MDSCs. To determine the role of MUC1 in MDSC phenotype, we analyzed MDSCs derived from wild type (WT) and MUC1-knockout (MUC1KO) mice bearing pancreatic ductal adenocarcinoma KCKO and breast cancer C57MG xenografts. We observed enhanced tumor growth in MUC1KO mice compared to WT mice in both pancreatic KCKO and breast C57MG cancer models due to increased MDSC population and enrichment of Tregs in tumor microenvironment. Our current study shows that knockdown of MUC1 in MDSCs promotes proliferation and immature suppressive phenotype indicated by increased level of iNOS, ARG1 activity and TGF-β secretion under cancer conditions. Increased activity of MDSCs leads to repression of IL-2 and IFN-ɣ production by T-cells. We were able to find that MDSCs from MUC1KO mice have higher levels of c-Myc and activated pSTAT3 as compared to MUC1 WT mice, that are signaling pathways leading to increased survival, proliferation and prevention of maturation. In summary, MUC1 regulates signaling pathways that maintain immunosuppressive properties of MDSCs. Thus, immunotherapy must target only tumor associated MUC1 on epithelial cells and not MUC1 on hematopoietic cells to avoid expansion and suppressive functions of MDSC.