Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional radiomics similarity networks (R2SN) in the human brain: reproducibility, small-world and biological basis

Background Structural covariance network (SCN) has been applied successfully to structural magnetic resonance imaging (MRI) study. However, most SCNs were constructed by the unitary marker, which was insensitive for the different disease phases. The aim of this study is to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. Methods Regional radiomics similarity network (R2SN) was constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject. We further assessed the small-world property of R2SN using the graph theory method, as well as the reproducibility in the different datasets and the reliability with test-retest analysis. The relationship between the R2SN and inter-regional co-expression of gene enriched was also explored, as well as the relationship with general intelligence. Results The R2SN can be replicated in different datasets, also regardless of using different feature subsets. The R2SN showed high reliability with the test-retest analysis (ICC>0.7). Besides, the small-word property (σ>2) and the high correlation with the gene expression (R=0.24, P<0.001) and the general intelligence was found by R2SN. Conclusion R2SN provides a novel, reliable, and biologically plausible method to understand human morphological covariance based on structural MRI. Impact Statement Imaging biomarkers are the cornerstone of modern radiology, and the development of valid biomarkers is crucial for optimizing individualized prediction in neurological disorders like AD. Thus, the development of the data mining method from neuroimaging is crucial for adding the biomarkers of disease. This study confirmed that R2SN provides a novel, robust and biologically plausible model and a new perspective for understanding the human brain, therefore. Thus, the R2SN has great promise in further study.
Myogenin controls via AKAP6 non-centrosomal microtubule organizing center formation at the nuclear envelope

Non-centrosomal microtubule organizing centers (MTOC) are pivotal for the function of multiple cell types, but the processes initiating their formation are unknown. Here, we find that the transcription factor myogenin is required in myoblasts for the localization of MTOC proteins to the nuclear envelope. Moreover, myogenin is sufficient in fibroblasts for nuclear envelope MTOC (NE-MTOC) formation and centrosome attenuation. Bioinformatics combined with loss- and gain-of-function experiments identified induction of AKAP6 expression as one central mechanism for myogenin-mediated NE-MTOC formation. Promoter studies indicate that myogenin preferentially induces the transcription of muscle- and NE-MTOC-specific isoforms of Akap6 and Syne1, which encodes nesprin-1α, the NE-MTOC anchor protein in muscle cells. Overexpression of AKAP6β and nesprin-1α was sufficient to recruit endogenous MTOC proteins to the nuclear envelope of myoblasts in the absence of myogenin. Taken together, our results illuminate how mammals transcriptionally control the switch from a centrosomal MTOC to an NE-MTOC and identify AKAP6 as a novel NE-MTOC component in muscle cells.
Robo2 Drives Target-Selective Peripheral Nerve Regeneration in Response to Glia-Derived Signals

Peripheral nerves are divided into multiple branches leading to divergent synaptic targets. This poses a remarkable challenge for regenerating axons as they select their original trajectory at nerve branch-points. Despite implications for functional regeneration, the molecular mechanisms underlying target selectivity are not well characterized. Danio Rerio (zebrafish) motor nerves are composed of a ventral and a dorsal branch that diverge at a choice-point, and we have previously shown that regenerating axons faithfully select their original branch and targets. Here we identify robo2 as a key regulator of target-selective regeneration (sex of experimental subjects unknown). We demonstrate that robo2 function in regenerating axons is required and sufficient to drive target-selective regeneration, and that robo2 acts in response to glia located precisely where regenerating axons select the branch-specific trajectory to prevent and correct axonal errors. Combined, our results reveal a glia-derived mechanism that acts locally via axonal robo2 to promote target-selective regeneration. SIGNIFICANCE STATEMENT Despite its relevance for functional recovery, the molecular mechanisms that direct regenerating peripheral nerve axons toward their original targets are not well defined. Zebrafish spinal motor nerves are composed of a dorsal and a ventral branch that diverge at a stereotyped nerve branch-point, providing a unique opportunity to decipher the molecular mechanisms critical for target-selective regeneration. Using a combination of live cell imaging and molecular-genetic manipulations, we demonstrate that the robo2 guidance receptor is necessary and sufficient to promote target-selective regeneration. Moreover, we demonstrate that robo2 is part of a genetic pathway that generates transient, spatially restricted, and tightly coordinated signaling events that direct axons of the dorsal nerve branch toward their original, pre-injury targets.
Oligodendrocyte progenitor proliferation is disinhibited following traumatic brain injury in LIF heterozygous mice

Traumatic brain injury (TBI) is a significant problem that affects ∼500,000 children each year. As cell proliferation is disturbed by injury and is required for normal brain development, we investigated how a pediatric closed head injury (CHI) would affect the progenitors of the subventricular zone (SVZ). Additionally, we evaluated the contribution of Leukemia Inhibitory Factor (LIF) using LIF-heterozygous mice (LIF Het), as LIF is an injury-induced cytokine, known to influence neurogenesis and gliogenesis. CHI’s were performed on P20 LIF Het and WT mice. Ki-67 staining and stereology revealed that cell proliferation increased ∼250% in injured LIF Het mice compared to the 30% increase observed in injured WT mice at 48 h post CHI. Furthermore, Olig2+ cell proliferation increased in the SVZ and white matter of LIF Het injured mice at 48 h recovery. Using an 8-color flow cytometry panel, the proliferation of three distinct multipotential progenitors were greater in LIF Het injured mice compared to WT injured mice. Early oligodendrocyte progenitor cell (OPC) proliferation was 6-fold higher in LIF Het injured mice compared to WT injured mice. In vitro, addition of LIF decreased overall cell proliferation and OPC proliferation compared to controls. Addition of LIF to OPC cultures induced an increase of phospho-Akt after 20 minutes and an increase of phospho-S6RP at 20 and 40 minutes of exposure, suggesting that LIF stimulates the mammalian target of Rapamycin pathway. Altogether, our data provide new insights into the regulatory role of LIF in suppressing neural progenitor cell proliferation after a mild TBI. Main Points OPC proliferation is dis-inhibited in LIF haplodeficient mice. LIF directly inhibits glial progenitor cell proliferation. LIF stimulates the mTOR pathway.
Host-age prediction from fecal microbiome composition in laboratory mice

The life-long relationship between microorganisms and hosts has a profound impact on the overall health and physiology of the holobiont. Changes in microbiome composition throughout the lifespan of a host remain, however, largely understudied. In this study, the fecal microbiome of conventionally raised C57BL/6J mice was analyzed throughout almost the entire expected lifespan, from ‘maturing’ (9 weeks) until ‘very old’ age (112 weeks). Analysis of alpha and beta diversity suggests that gradual microbiome changes occur throughout the entire murine life but appear to be more pronounced in ‘maturing’ to ‘middle-aged’ phases. Phylum-level analysis indicates a shift in the Firmicutes/Bacteroidetes ratio in favor of the Firmicutes in the second year of adulthood. Varying successional patterns throughout life were observed for many Firmicutes OTUs, while relative abundances of Bacteroidetes OTUs varied primarily in the early life phases. Microbiome configurations at given time points were used as training sets in a Bayesian model, which in turn effectively enabled the prediction of host age. The fecal microbiome composition may therefore serve as an accurate biomarker for aging. This study further suggests that age-associated compositional differences may have considerable implications for the interpretation and comparability of animal model-based microbiome studies. Importance The life-long relationship between microorganisms and hosts has a profound impact on the overall physiology of the holobiont. Understanding the extent of gut microbiome compositional changes over the expected mouse lifespan may allow to better understand the interplay of microbiome and the host at the different life stages. In this study, we performed a two-year longitudinal study of murine fecal microbiome. Using fine-scale microbiome profiling we were able to predict the host age from the fecal microbiome composition. Moreover, we observed that the rate of compositional change appears to slow with age. The description of the compositional changes in commonly used C57BL/6J mice can be used to optimize selection of age-associated mouse models and highlights the use of microbiome-profiling as biomarker for aging.
The convergence and divergence of episodic and semantic functions across lateral parietal cortex

Several decades of neuropsychological and neuroimaging research have highlighted the importance of lateral parietal cortex (LPC) across a myriad of cognitive domains. Yet, despite the prominence of this region the underlying function of LPC remains unclear. Two domains that have placed particular emphasis on LPC involvement are semantic memory and episodic memory retrieval. From each domain, sophisticated models have been proposed as to the underlying function, as well as the more domain-general assumption that LPC is engaged by any form of internally-directed cognition (episodic and semantic retrieval both being examples if this process). Here we directly address these alternatives using a combination of fMRI, functional connectivity and DTI white-matter connectivity data. The results show that ventral LPC (angular gyrus) was positively engaged during episodic retrieval but disengaged during semantic memory retrieval. In addition, the level of activity negatively varied with task difficulty in the semantic task whereas episodic activation was independent of difficulty. In contrast, dorsal LPC (intraparietal sulcus) showed domain general activation that was positively correlated with task difficulty. In terms of functional and structural connectivity, a dorsal-ventral and anterior-posterior gradient of connectivity was found to different processing networks (e.g., mid-angular gyrus (AG) connected with episodic retrieval). We propose a unifying model in which LPC as a whole might share a common underlying neurocomputation (e.g., multimodal buffering) with variations in the emergent, expressed cognitive functions across subregions arising from differences in the underlying white matter connectivity.
Venses @ HaSpeeDe2 & SardiStance: Multilevel Deep Linguistically Based Supervised Approach to Classification

In this paper we present the results obtained with ItVENSES a system for syntactic and semantic processing that is based on the parser for Italian called ItGetaruns to analyse each sentence. In previous EVALITA tasks we only used semantics to produce the results. In this year EVALITA, we used both a fully and mixed statistically based approach and the semantic one used previously. The statistic approaches are all characterized by the use of n-grams and the usual tf-idf indices. We added another parameter called the Kullback-Leibler Divergence to compute similarities. In addition we used emoticons and hashtags. Results for the two runs allowed have been fairly low – around 40% F1-score. We continued producing other runs on the basis of the statistical approach and after receiving the goldtest version and the evaluation script we discovered that in one of these additional runs the fourth we improved up to 54% macro F1 for HaSpeeDe2 task and up to 48% macro F1 for Sardines.
GOLIATH regulates LDLR availability and plasma LDL cholesterol levels

Increasing the availability of hepatic low-density lipoprotein receptors (LDLR) remains a major clinical target for reducing circulating plasma LDL cholesterol (LDL-C) levels. Here, we identify the molecular mechanism underlying genome-wide significant associations in the GOLIATH locus with plasma LDL-C levels. We demonstrate that GOLIATH is an E3 ubiquitin ligase that ubiquitinates the LDL Receptor resulting in redistribution away from the plasma membrane. Overexpression of GOLIATH decreases hepatic LDLR and increases plasma LDL-C levels. Silencing of Goliath using antisense oligonucleotides, germline deletion, or AAV-CRISPR in vivo strategies increases hepatic LDLR abundance and availability, thus decreasing plasma LDL-C. In vitro ubiquitination assays demonstrate RING-dependent regulation of LDLR abundance at the plasma membrane. Our studies identify GOLIATH as a novel post-translational regulator of LDL-C levels via modulation of LDLR availability, which is likely important for understanding the complex regulation of hepatic LDLR.
Genetically Engineered Proportional-Integral Feedback Controllers for Robust Perfect Adaptation in Mammalian Cells

To support their survival, cells adapt to environmental disturbances by maintaining a constant internal milieu. Robust perfect adaptation is a strategy that utilizes integral feedback to promote adaptation by robustly driving regulated physiological variables to their pre-disturbance levels. Present in natural systems, this stringent regulatory strategy promises to enable the engineering of sophisticated genetic programs with diverse applications. Here, we present the first synthetic implementations of integral and proportional-integral feedback controllers in mammalian cells. We show that the integral controller robustly and precisely maintains a desired level of a transcription factor, in spite of induced disturbances and network perturbations. Augmenting proportional feedback reduces stochastic variability while maintaining robust perfect adaptation. We demonstrate the benefits of these controllers in mitigating the impact of resource burden and investigate their use in cell therapy. The synthetic biological realization of robust perfect adaptation holds promise for substantial advances in industrial biotechnology and cell-based therapies.
TiMEG: an integrative approach for partially missing multi-omics data with an application to tuberous sclerosis

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omics analyses. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.
An Automatic Insect Recognition Algorithm in Complex Background Based on Convolution Neural Network

The existing insect recognition methods mostly segment the target region by traditional classification technology, failing to achieve a high accuracy in complex background. To solve the problem, this paper introduces the morphology-based edgeless active contour strategy to segment insects in complex background. The strategy integrates the morphological operation of gray image, and detects insect contours by narrow-band fast method. To enhance the background diversity of new samples, the authors improved the synthetic minority over-sampling technique (SMOTE) algorithm into a variable weight edge enhancement algorithm. Based on the SMOTE algorithm, the proposed algorithm increases the weight of the edge area as adjacent images are superimposed into a new image, making the background of the new image more complex. Finally, the proposed method was coupled with DenseNet-121 to recognize insects in images with complex background. The results show that the accuracy of the network was nearly 10% higher on the balanced set than on the unbalanced set, suggesting that our method is feasible and accurate.
Heavy metal pollutants have additive negative effects on honey bee cognition

Environmental pollutants can exert sublethal deleterious effects on animals. These include disruption of cognitive functions underlying crucial behaviours. While agrochemicals have been identified as a major threat to pollinators, other compounds, such as heavy metals that are often found in complex mixtures, have largely been overlooked. Here, we assessed the impact of acute exposure to field-realistic concentrations of lead, copper, arsenic, and their combinations, on honey bee learning and memory. All treatments involving single metals slowed down appetitive learning and disrupted memory retrieval at 24 h. Importantly, combinations of these metals induced additive negative effects on both processes, suggesting common pathways of toxicity. Our results highlight the need to further assess the risks of heavy metal pollution on invertebrates and to their associated ecosystem services. Summary statement Honey bees displayed reduced learning and memory performances following acute exposure to arsenic, copper or lead. Exposure to combinations of these metals induced additive effects.
Solvent Extraction of Indium, Gallium, and Zinc Ions with Acidic Organophosphates having Bulky Alkyl Groups

The extraction equilibria of In3+, Ga3, and Zn2+ with bis(4‐ethylcyclohexyl)phosphoric acid (D4ECHPA), bis(4‐cyclohexylcyclohexyl)phosphoric acid (D4DCHPA), and bis(2‐ethylhexyl)phosphoric acid (D2EHPA) were investigated in acidic aqueous sulfate media. The order of extractability of metal ions is D4DCHPA > D2EHPA > D4ECHPA, which corresponds to the lipophilicity (log P) of the extractants. The separation factors, β(In/Ga) and β(Ga/Zn), of D4ECHPA and D4DCHPA are greater or comparable than that of D2EHPA, because of the steric hindrance of the bulky cyclohexyl groups. In3+ can be therefore separated from simulated liquor containing a high concentration of Zn2+ by D4DCHPA.
Water structure and water/protein interactions in biological materials characterized by Raman spectroscopy

The R(v)-representation of the low-frequency Raman spectrum was used to investigate the low-frequency Raman spectrum of water. The advantages of using reduced representations in low-frequency Raman studies to display water structure are discussed. Tetrahedrically hydrogen bonded water molecules showed a characteristic low-frequency band with a peak maximum around 180 cm-1. O-18 and O-17 isotopic substitution revealed that the corresponding vibrational mode mainly involves displacements of the oxygen atoms, but no significant hydrogen motion. This mode can be used to monitor the existence of water with a bulk-like structure in biological macromolecular materials. To test its applicability NIR-FT-Raman spectroscopy was used in studies of biopolymers in order to avoid fluorescence.
The Style of a Successful Story: a Computational Study on the Fanfiction Genre

This paper presents a new corpus for the Italian language representative of the fan-ﬁction genre. It comprises about 55k user-generated stories inspired to the original fantasy saga “Harry Potter” and published on a popular website. The corpus is large enough to support data-driven investigations in many directions, from more traditional studies on language variation aimed at characterizing this genre with respect to more traditional ones, to emerging topics in computational social science such as the identiﬁcation of factors involved in the success of a story. The latter is the focus of the presented case-study, in which a wide set of multi-level linguistic features has been automatically extracted from a subset of the corpus and analysed in order to detect the ones which signiﬁcantly discriminate successful from unsuccessful stories
Domain Adaptation for Text Classification with Weird Embeddings

Pre-trained word embeddings are often used to initialize deep learning models for text classiﬁcation, as a way to inject pre-computed lexical knowledge and boost the learning process. However, such embed-dings are usually trained on generic corpora, while text classiﬁcation tasks are often domain-speciﬁc. We propose a fully automated method to adapt pre-trained word embeddings to any given classiﬁcation task, that needs no additional resource other than the original training set. The method is based on the concept of word weirdness , extended to score the words in the training set according to how characteristic they are with respect to the labels of a text classiﬁcation dataset. The polarized weirdness scores are then used to up-date the word embeddings to reﬂect task-speciﬁc semantic shifts. Our experiments show that this method is beneﬁcial to the performance of several text classiﬁcation tasks in different languages.
TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization (short paper)

In this contribution we describe the system (i.e. a statistical model) used to participate in Evalita conference 2020, SardiStance (Tasks A and B) and Haspeede2 (Tasks A and B). We first developed a classifier by extracting features from the texts and the social network of users. Then, we fit the data through an extreme gradient boosting, with cross-validation tuning of the hyper-parameters. A key factor for a good performance in SardiStance Task B was the features extraction by using Multidimensional Scaling of the distance matrix (minimum path, undirected graph) applied on each network. The second system exploits the same features above, but it trains and performs predictions in twosteps. The performances proved to be lower than those of the single-step model.
Is Skin in the Game a Game Changer? Evidence from Mandatory Changes to D&O Insurance Policies

Abstract This paper examines the incentive effects of a mandatory personal deductible in liability insurance contracts for directors and officers (D&Os). Exploiting a novel German law that mandates personal deductibles for executives, we document positive returns for affected firms around the first announcement of the plan to impose a personal deductible. We also find evidence of long-run effects: affected firms decrease risk taking in operational activities and financial reporting, and improve the quality of takeover decisions. Our study shows that the structure of D&O insurance contracts matters because mandating that D&Os have “skin in the game” appears to lead to real effects on firm value.