Miracle-dz/UniFAR-FTUs huggingface dataset:
IRMA: the 335-million-word Italian coRpus for studying MisinformAtion

The dissemination of false information on the internet has received considerable attention over the last decade. Misinformation often spreads faster than mainstream news, thus making manual fact checking inefficient or, at best, labor-intensive. Therefore, there is an increasing need to develop methods for automatic detection of misinformation. Although resources for creating such methods are available in English, other languages are often under-represented in this effort.With this contribution, we present IRMA, a corpus containing over 600,000 Italian news articles (335+ million tokens) collected from 56 websites classified as ‘untrustworthy’ by professional fact-checkers. The corpus is freely available and comprises a rich set of text- and website-level data, representing a turnkey resource to test hypotheses and develop automatic detection algorithms. It contains texts, titles, and dates (from 2004 to 2022), along with three types of semantic measures (i.e., keywords, topics at three different resolutions, and LIWC lexical features). IRMA also includes domain-specific information such as source type (e.g., political, health, conspiracy, etc.), credibility, and higher-level metadata, including several metrics of website incoming traffic that allow to investigate user online behavior. IRMA constitutes the largest corpus of misinformation available today in Italian, making it a valid tool for advancing quantitative research on untrustworthy news detection and ultimately helping limit the spread of misinformation.
Prompt Tuning with Contradictory Intentions for Sarcasm Recognition

Recently, prompt tuning has achieved promising results in a variety of natural language processing (NLP) tasks. The typical approach is to insert text pieces (i.e. templates) into the input and transform downstream tasks into the same form as pre-training. In essence, a high-quality template is the foundation of prompt tuning to support the performance of the converted cloze-style task. However, for sarcasm recognition, it is time-consuming and requires increasingly sophisticated domain knowledge to determine the appropriate templates and label words due to its highly figurative nature. In this work, we propose SarcPrompt, to incorporate the prior knowledge about contradictory intentions into prompt tuning for sarcasm recognition. SarcPrompt is inspired by that the speaker usually says the opposite of what they actually mean in the sarcastic text. Based on this idea, we explicitly mimic the actual intention by prompt construction and indicate whether the actual intention is contradictory to the literal content by verbalizer engineering. Experiments on three public datasets with standard and low-resource settings demonstrate the effectiveness of our SarcPrompt for sarcasm recognition.
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples

NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image _i_ and text _t_), but motivated intensionally through capabilities invoked in verbal descriptions of the task (e.g., “_t_ is a description of _i_, for which the content of _i_ needs to be recognised and understood”). We present Pento-DIARef, a diagnostic dataset in a visual domain of puzzle pieces where referring expressions are generated by a well-known symbolic algorithm (the “Incremental Algorithm”),which itself is motivated by appeal to a hypothesised capability (eliminating distractors through application of Gricean maxims). Our question then is whether the extensional description (the dataset) is sufficient for a neural model to pick up the underlying regularity and exhibit this capability given the simple task definition of producing expressions from visual inputs. We find that a model supported by a vision detection step and a targeted data generation scheme achieves an almost perfect BLEU@1 score and sentence accuracy, whereas simpler baselines do not.
Lesion-specific suppression of YAP/TAZ by biomimetic nanodrug ameliorates atherosclerosis development

Atherosclerosis, characterized by the buildup of lipid-rich plaque on the vessel wall, is the primary cause of myocardial infarction and ischemic stroke. Recent studies have demonstrated that dysregulation of yes-associated protein 1 (YAP) and transcriptional coactivator with PDZ-binding domain (TAZ) contributes to plaque development, making YAP/TAZ potential therapeutic targets. However, systemic modulation of YAP/TAZ expression or activities risks serious off-target effects, limiting clinical applicability. To address the challenge, this study develops monocyte membrane-coated nanoparticles (MoNP) as a drug delivery vehicle targeting activated endothelium lining the plaque surface and utilizes MoNP to deliver verteporfin (VP), a potent YAP/TAZ inhibitor, for lesion-specific treatment of atherosclerosis. The results reveal that MoNP significantly enhance payload delivery to inflamed endothelial cells (EC) while avoiding phagocytic cells, and preferentially accumulate in atherosclerotic regions. MoNP-mediated delivery of VP substantially reduces YAP/TAZ expression, suppressing inflammatory gene expression and macrophage infiltration in cultured EC and mouse arteries exposed to atherogenic stimuli. Importantly, this lesion-targeted VP nanodrug effectively decreases plaque development in mice without causing noticeable histopathological changes in major organs. Collectively, these findings demonstrate a plaque-targeted and pathway-specific biomimetic nanodrug, potentially leading to safer and more effective treatments for atherosclerosis.
Ts65Dn synaptic proteome partially recapitulates AD biochemical signature

Down syndrome (DS), caused by human chromosome 21 trisomy, is one of the most common genetic causes of intellectual disabilities. Moreover, approximately 60-80% of individuals with Down Syndrome develop early onset Alzheimer’s disease symptoms and neuropathology characterized by the deposition of senile plaques and neurofibrillary tangles. Spine loss and synaptic deficits are the main alterations present in both pathologies. The synaptic proteins that are altered in both pathologies have also been studied. However, a proteomic study comparing synaptic fractions from DS and AD patients is lacking. To address this aim, we optimized a biochemical subfractioning method to isolate postsynaptic fractions from the cortex of Ts65Dn Down syndrome murine model and the entorhinal cortex from AD subjects’ samples. These samples were used for further proteomic analysis to observe potential alterations in postsynaptic protein expression and protein-protein interaction networks. This study compared the Ts65Dn animal model and entorhinal cortex of AD subjects and revealed a common altered postsynaptic composition of the adult Ts65Dn mouse cortex and entorhinal cortex of AD subjects. These postsynaptic protein alterations might functionally underlie Ts65Dn-associated and AD-associated dendritic deterioration, microtubule organization, glutamatergic transmission, and phosphorylation alterations. They provide insight into the possible common pathological synaptic mechanisms of both diseases and the earliest stages of AD disease progression. Moreover, our proteomic data suggest potential drug targets for modulating the commonly altered postsynaptic mechanisms in DS and AD.
Boosting N-terminally anchored yeast surface display via structural insights into S. cerevisiae Pir proteins

Surface display co-opts yeast’s innate ability to embellish its cell wall with mannoproteins, thus converting the yeast’s outer surface into a growing and self-sustaining catalyst. However, the efficient toolbox for converting the enzyme of interest into its surface-displayed isoform is currently lacking, especially if the isoform needs to be anchored to the cell wall near the isoform’s N-terminus. Aiming to advance such N-terminally anchored surface display, we employed in silico and machine-learning strategies to study the 3D structure, function, genomic organisation, and evolution of the Pir protein family, whose members evolved to covalently attach themselves near their N-terminus to the β-1,3-glucan of the cell wall. Through the newly-gained insights, we rationally engineered 14 S. cerevisiae Hsp150 (Pir2)-based fusion proteins. We quantified their performance, uncovering guidelines for efficient yeast surface display while developing a construct that promoted a 2.5-fold more efficient display than the full-length Hsp150 and a Pir-tag, i.e., a peptide spanning only 4.5 kDa but promoting as efficient surface display as the full-length Hsp150. These constructs fortify the existing surface display toolbox, allowing for a prompt and routine refitting of any protein into its N-terminally anchored isoform. Graphical abstract
Induced pluripotent stem cell model revealed impaired neurovascular interaction in genetic small vessel disease CADASIL

Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is the most common genetic small vessel disease caused by variants in the NOTCH3 gene. Patients with CADASIL experience recurrent strokes, developing into cognitive defect and vascular dementia. CADASIL is a late-onset vascular condition, but migraine and brain MRI lesions appear in CADASIL patients as early as their teens and twenties, suggesting an abnormal neurovascular interaction at the neurovascular unit (NVU) where microvessels meet the brain parenchyma. To understand the molecular mechanisms of CADASIL, we established induced pluripotent stem cell (iPSC) models from CADASIL patients and differentiated the iPSCs into the major NVU cell types including brain microvascular endothelial-like cells (BMECs), vascular mural cells (MCs), astrocytes and cortical projection neurons. We then built an in vitro NVU model by co-culturing different neurovascular cell types in Transwells and evaluated the blood brain barrier (BBB) function by measuring transendothelial electrical resistance (TEER). Results showed that, while the wild-type MCs, astrocytes and neurons could all independently and significantly enhance TEER values of the iPSC-BMECs, such capability of MCs from iPSCs of CADASIL patients was significantly impeded. Additionally, the barrier function of the BMECs from CADASIL iPSCs was significantly impaired, accompanied with disorganised tight junctions in iPSC-BMECs, which could not be effectively rescued by the wild-type MCs, astrocytes and neurons. Our findings provide new insight into early disease pathologies on the neurovascular interaction and BBB function at the molecular and cellular levels for CADASIL, which helps inform future therapeutic development.
Water resource systems' variable fuzzy sets evaluation new method based on the opposites unity and quantity-quality exchange theorem

According to the theory of opposites unity and law of quantity-quality exchange derived from the variable fuzzy sets’ theory. A new variable fuzzy evaluation method for water resource systems was proposed. Two examples of application for illustrating the principle and calculation steps were given. Comparing with fuzzy comprehensive evaluation method and set pair analysis method on the evaluation, the proposed method possesses the merits of restrict theoretical basis, simple in form and easy to be used.
Legionella pneumophila macrophage infectivity potentiator protein appendage domains modulate protein dynamics and inhibitor binding

Macrophage infectivity potentiator (MIP) proteins are widespread in human pathogens including Legionella pneumophila, the causative agent of Legionnaires’ disease and protozoans such as Trypanosoma cruzi. All MIP proteins contain a FKBP (FK506 binding protein)-like prolyl-cis/trans- isomerase domain that hence presents an attractive drug target. Some MIPs such as the Legionella pneumophila protein (LpMIP) have additional appendage domains of mostly unknown function. In full- length, homodimeric LpMIP, the N-terminal dimerization domain is linked to the FKBP-like domain via a long, free-standing stalk helix. Combining X-ray crystallography, NMR and EPR spectroscopy and SAXS, we elucidated the importance of the stalk helix for protein dynamics and inhibitor binding to the FKBP-like domain and bidirectional crosstalk between the different protein regions. The first comparison of a microbial MIP and a human FKBP in complex with the same synthetic inhibitor was made possible by high-resolution structures of LpMIP with a [4.3. 1]-aza-bicyclic sulfonamide and provides a basis for designing pathogen-selective inhibitors. Through stereospecific methylation, the affinity of inhibitors to L. pneumophila and T. cruzi MIP was greatly improved. The resulting X-ray inhibitor-complex structures of LpMIP and TcMIP at 1.49 and 1.34 Å, respectively, provide a starting point for developing potent inhibitors against MIPs from multiple pathogenic microorganisms.
Dip-coating of ultra-thin liquid lubricant and its control for thin-film magnetic hard disks

The conventional viscous-flow mechanism associated with the dip-coating technique for lubricant thickness control becomes ineffective for deposition of ultra-thin films (</spl sim/2 nm) required for hard magnetic disks. We have demonstrated that at least two important microscopic mechanisms dominate the control of lubricant thickness in this thickness range. They are surface adsorption and diffusion of lubricant molecules at the solid-liquid interface. Furthermore, it was also observed that too fast a pulling-up speed resulted in a more nonuniform lubricant film onto the disk surface. This is due to disturbance of the meniscus profile by viscous drag. The uniformity and/or the microstructure of lubricant layer deposited onto the disk surface will then affect tribological performance.
Assembly and Cation-selectivity Mechanisms of Neuronal Gap Junction Channel Connexin 36 Elucidated by Cryo-EM

Electrical synapses are essential components of neural circuits. Dysfunction of electrical synapses can lead to obstacles in learning and memory. Neural signal transduction across electrical synapses is primarily mediated by a gap junction channel, Connexin 36 (Cx36), the lack of which causes impaired electrical couplings in cortical interneurons and thalamic reticular nucleus (TRN) neurons. Unique characteristics of Cx36 gap junction channel include its insensitivity in transjunctional membrane potential, as well as its preference for homomeric assembly, prone to exclude other paralogous connexins from co-assembly. However, the structural basis underlying Cx36 function and assembly remains elusive. Here, we report the cryo-EM structure of human Cx36 at 2.67 Å resolution and identified critical residues underpinning its obligatory homomeric assembly. In particular, we found non-canonical electrostatic interactions between protomers from opposing hemichannels and a steric complementary site between adjacent protomers within a hemichannel, which together offer a structural explanation for the assembly specificity in homomeric and heteromeric gap junction channels. Moreover, the narrowest restriction along the channel axis overlaps with an acidic patch, where Glu43, Asp47 and Asp48 may contribute to cation-selectivity. Also, the amino-terminal helix reported to be responsible for sensing membrane potential in other connexins was disordered in our Cx36 structure, in line with its channel activity independent of membrane potential. Together, this work elucidated the assembly mechanisms of the electrical synaptic gap junction channel, and offered possible explanations for sustained Cx36 activity upon membrane depolarization, which allows efficient action potential propagation across electrical synapses.
Symptoms of depression, anxiety, and thoughts of suicide/self-injury in adolescents and young adults living with HIV in Botswana

Globally, mental health problems have been reported to be more common in youth living with HIV (YLWH) than in the general population, but routine mental health screening is rarely done in high-volume HIV clinics. In 2019, YLWH in a large HIV clinic in Botswana were screened using the Generalized Anxiety Scale-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) in a pilot standard-of-care screening programme. Two-way ANOVA was used to describe the effects of age group (12–<16, 16–<20 and 20–25 years old) and sex on GAD-7 and PHQ-9 scores. Chi-square statistics were used to compare characteristics of YLWH with and without potential suicidality/self-harm symptoms based on question 9 in the PHQ-9. Among 1 469 YLWH, 33.1%, 44.3% and 15.0% had anxiety, depression and potential suicidality/self-harm symptoms respectively. YLWH of 20–25 years old and 16–<20 years old had higher GAD-7 scores compared to 12–<16-year-olds (p = 0.014 and p = <0.001 respectively). Female YLWH of 20–25 years old had higher PHQ-9 scores compared to 12–<16-year-olds (p = 0.002). There were no other sex–age dynamics that were statistically significant. Female YLWH endorsed more thoughts of suicidality/self-harm than males (17% versus 13%, p = 0.03 respectively). Given the proportion of YLWH with mental health symptoms, Botswana should enhance investments in mental health services for YLWH, especially for young female adults who bear a disproportionate burden.
Two-step methods for image zooming using duality strategies

In this paper we propose two two-step methods for image zooming using duality strategies. In the first method, instead of smoothing the normal vector directly as did in the first step of the classical LOT model, we reconstruct the unit normal vector by means of Chambolle's dual formulation. Then, we adopt the split Bregman iteration to obtain the zoomed image in the second step. The second method is based on the TV-Stokes model. By smoothing the tangential vector and imposing the divergence free condition, we propose an image zooming method based on the TV-Stokes model using the dual formulation. Furthermore, we give the convergence analysis of the proposed algorithms. Numerical experiments show the efficiency of the proposed methods.
The rise of metrics and fall of academic autonomy: the digital future of academic freedom

As citation-based metrics are increasingly used for measuring research performance, the formation of academics’ research choices and conceptions of academic freedom are affected. Critically examining the behaviours of academics in today’s competitive publishing landscape, we argue that creeping managerial practices are shaping research choices and perceived opportunities. In addition, observation indicates that the way in which academic freedom is conceptualised is changing as a result of considering academic performance through metrics. This raises questions of whether and to what extent professional and academic autonomy remains in the hands of authors or are instead being outsourced to metrics. Based on 21 semi-structured interviews with academics across the humanities and social sciences, we found metrics relating to assessment, rankings and funding all direct research choices. This research-in-progress paper identifies key issues and outlines future research to understand the wider influence of metrification.
A novel method of improving energy efficient QoS in FANET using Correlation And Confidence Based Gradient Clustering Metric approach

Flying Ad‐hoc Networks (FANETs) being the most popular wing of the research in recent time had many evolutions to cope up with the growing demands in defense and other civil applications. Limited number of nodes, limited source of energy and increased mobility of the drones were the major problems to be addressed while building an effective FANET. Drones were aerial‐nodes that are driver‐less; their actions were prescribed by means of algorithms devoid of some human relations and could be easily deployed in the system. Hence this article aims to propose a novel framework to improvise the energy efficiency of FANET through Correlation and Confidence based Gradient Clustering Metric (CC‐GCM) approach in the clustering algorithm. The results obtained through the proposed model are proven to be outstanding by producing an improvised performance efficiency and augmented energy efficiency. This article also involves a comparative study of the performance attributes with the existing models with tabulated values and graphs in terms of energy consumption, average end‐to‐end delay, cluster building time, average cluster life time, and Packet Delivery Ratio (PDR).
Photonic Crystal Fiber-Based Surface Plasmon Resonance Sensor for Sensing Broad Range of Refractive Indices

A twin core photonic crystal fiber-based surface plasmon resonance sensor has been designed in this work to detect a wide range of refractive indices with high sensitivity. The dual core fiber is polished on both sides and the metal layer is deposited on the polished surfaces as the plasmonic materials. Two microchannels are employed on each side of the fiber and thus the sensor forms four separate plasmonic layers on the external surface of the fiber to achieve a wide detection range. Plasmonic material silver is used here due to its availability and low cost. A thin layer of (TiO2) is used on top to prevent oxidation of silver. The proposed design is easy to fabricate and exhibits a good fabrication tolerance. The sensing application is also convenient as the analyte is placed outside the fiber. The analyte refractive index detection range of this sensor is found 1.23 – 1.44. The maximum spectral sensitivity of the sensor is 28200 nm/RIU and the corresponding resolution is 3.55 × 10−6 RIU. The maximum amplitude sensitivity of the sensor is 286 RIU−1. Due to its high sensitivity and wide detection range this sensor can be used to detect various low refractive index (1.23 – 1.29) chemicals (like halogenated anesthetic agents, refrigerants, different type of aerogels) to various bio materials (biological tissues, cancerous cells, hemoglobin) which has refractive indices usually in the range of 1.3 – 1.39, to various high index (1.4 – 1.44) analytes (like high concentration of electrolytes, proteins).
Dataset Conversion for Spiking Neural Networks

Spiking Neural Networks (SNN) are still a relatively new research area, and there are only a few publicly available datasets suitable for SNN training and testing. Datasets designed for traditional neural networks are not suitable as an input to SNNs because they rely on the timing of spikes, thus, requiring data to be preprocessed in a specific way. This work presents a pipeline for converting existing conventional datasets into rate-encoded spikes matching the requirements for SNN processing. The implementation is based on the Python snntorch library. The functionality of the proposed encoder pipeline is evaluated on a music genre classification task using the GTZAN dataset. However, the proposed procedure has general applicability and may be used for data of any form.