Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of beta- and gamma-band rhythmic stimulation on motor inhibition

Voluntary movements are accompanied by an increase in gamma-band oscillatory activity (60-100Hz) and a strong desynchronization of beta-band activity (13-30Hz) in the motor system at both the cortical and subcortical level. Conversely, successful motor inhibition is associated with increased beta power in a fronto-basal-ganglia network. Intriguingly, gamma activity also increases in response to a stop-signal. In this study, we used transcranial alternating current stimulation to drive beta and gamma oscillations to investigate whether these frequencies are causally related to motor inhibition. We found that 20Hz stimulation targeted at the pre-supplementary motor area enhanced inhibition and increased beta oscillatory activity around the time of the stop-signal in trials directly following stimulation. In contrast, 70Hz stimulation seemed to slow down the braking process, and predominantly affected go task performance. These results demonstrate that the effects of tACS are state-dependent and that especially fronto-central beta activity is a functional marker for successful motor inhibition.
Identification of High risk nsSNPs in Human TP53 Gene Associated with Li–Fraumeni Syndrome: An In Silico Analysis Approach

Background Li–Fraumeni syndrome (LFS) is a cancer–prone conditions caused by a germline mutation of the TP53 gene on chromosome 17p13.1. It has an autosomal dominant pattern of inheritance with high penetrance. Purpose The aim of this study is to identify the high-risk pathogenic nsSNPs in PT53 gene that could be involved in the pathogenesis of Li–Fraumeni syndrome. Methods The nsSNPs in the human PT53 gene retrieved from NCBI, were analyzed for their functional and structural consequences using various in silico tools to predict the pathogenicity of each SNP. SIFT, Polyphen, PROVEAN, SNAP2, SNPs&Go, PHD-SNP, and P-Mut were chosen to study the functional inference while I-Mutant 3.0, and MUPro tools were used to test the impact of amino acid substitutions on protein stability by calculating ΔΔG value. The effects of the mutations on 3D structure of the PT53 protein were predicted using RaptorX and visualized by UCSF Chimera. Results A total of 845 PT53 nsSNPs were analyzed. Out of 7 nsSNPs of PT53 three of them (T118L, C242S, and I251N) were found high-risk pathogenic. Conclusion In this study, out of 7 predicted high-risk pathogenic nsSNPs, three high-risk pathogenic nsSNPs of PT53 gene were identified, which could be used as diagnostic marker for this gene. The combination of sequence-based and structure-based approaches is highly effective for pointing pathogenic regions.
Observations Pertaining to the Origin and Ecology of Microorganisms Recovered from the Deep Subsurface of Taylorsville Basin, Virginia

To understand the conditions under which microorganisms exist in deep hydrocarbon reservoirs, sidewall cores were collected from a natural gas‐bearing formation, 2800 m below the surface in Taylorsville Basin, Virginia. Data from chemical and microbial tracers and controls indicate that the interiors of some sidewall cores contained microorganisms indigenous to the rock formation. The cultured microorganisms were composed primarily of saline‐tolerant, thermophilic fermenting, Fe(III)‐reducing, and sulfate‐reducing bacteria (1 to 104 cells/g). The physiological capabilities of the cultured microorganisms are compatible with the temperature (76°C), pressure (32 MPa), and salinity (≈0.8 wt.% NaCl equivalent) in the sampled interval. The petrological data indicated that the strata contain intercrystalline pores of micrometer size, that occur between late diagenetic cement in siltstone and within cross‐cutting, mineralized fractures in shale. These pores made up only 0.04% of the rock volume, were mostly gas‐f.
CYRI-A regulates macropinocytic cup maturation and mediates integrin uptake, limiting invasive migration

The Scar/WAVE complex is the major driver of actin nucleation at the plasma membrane, resulting in lamellipodia and membrane ruffles. While lamellipodia aid migration, membrane ruffles can generate macropinosomes - cup-like structures - important for nutrient uptake and regulation of cell surface receptor levels. How macropinosomes are formed and the role of the actin machinery in their formation and resolution is still not well understood. Mammalian CYRI-B is a recently described negative regulator of the Scar/WAVE complex by RAC1 sequestration, but its other paralogue, CYRI-A has not been characterised. Here we implicate CYRI-A as a key regulator of macropinosome maturation and integrin internalisation from the cell surface. We find that CYRI-A is recruited to nascent macropinosomes in a transient but distinct burst, downstream of PIP3-mediated RAC1 activation and the initial burst of actin assembly driving cup formation, but upstream of internalisation and RAB5 recruitment to the macropinosome. Together, our data place CYRI-A as a local suppressor of actin dynamics, enabling the resolution of the macropinocytic cup. The failure of CYRI-depleted cells to resolve their macropinocytic cups results in reduced integrin a5b1 internalisation, leading to enhanced spreading, invasive behaviour and anchorage-independent 3D growth. We thus describe a new role for CYRI-A as a highly dynamic regulator of RAC1 activity at macropinosomes, modulating homeostasis of integrin surface presentation, with important functional consequences.
An in vivo method for diversifying the functions of therapeutic antibodies

Significance The immune system can produce enormous varieties of antibodies to counter infections. In this study, we devised a method to harness the power of the immune system to produce therapeutic antibodies. We used this method to generate antibodies against human PD1, a negative regulator of T cell activity. Available anti-PD1 therapeutic antibodies inhibit the function of PD1, thereby boosting T cell activity against tumor cells. We diversified one such anti-PD1 antibody and obtained multiple anti-PD1 antibodies, some of which can stimulate, rather than inhibit, PD1 function in vitro. Such PD1-stimulatory antibodies could potentially be used to suppress pathogenic T cell activities in autoimmune diseases. This method could be used to diversify the functions of other therapeutic antibodies. V(D)J recombination generates mature B cells that express huge repertoires of primary antibodies as diverse immunoglobulin (Ig) heavy chain (IgH) and light chain (IgL) of their B cell antigen receptors (BCRs). Cognate antigen binding to BCR variable region domains activates B cells into the germinal center (GC) reaction in which somatic hypermutation (SHM) modifies primary variable region-encoding sequences, with subsequent selection for mutations that improve antigen-binding affinity, ultimately leading to antibody affinity maturation. Based on these principles, we developed a humanized mouse model approach to diversify an anti-PD1 therapeutic antibody and allow isolation of variants with novel properties. In this approach, component Ig gene segments of the anti-PD1 antibody underwent de novo V(D)J recombination to diversify the anti-PD1 antibody in the primary antibody repertoire in the mouse models. Immunization of these mouse models further modified the anti-PD1 antibodies through SHM. Known anti-PD1 antibodies block interaction of PD1 with its ligands to alleviate PD1-mediated T cell suppression, thereby boosting antitumor T cell responses. By diversifying one such anti-PD1 antibody, we derived many anti-PD1 antibodies, including anti-PD1 antibodies with the opposite activity of enhancing PD1/ligand interaction. Such antibodies theoretically might suppress deleterious T cell activities in autoimmune diseases. The approach we describe should be generally applicable for diversifying other therapeutic antibodies.
Topic Modelling Games

English. This paper presents a new topic modelling framework inspired by game theoretic principles. It is formulated as a normal form game in which words are represented as players and topics as strategies that the players select. The strategies of each player are modelled with a probability distribution guided by a utility function that the players try to maximize. This function induces players to select strategies similar to those selected by similar players and to choice strategies not shared with those selected by dissimilar players. The proposed framework is compared with state-of-the-art models demonstrating good performances on standard benchmarks.
AMI @ EVALITA2020: Automatic Misogyny Identification

English. Automatic Misogyny Identiﬁcation (AMI) is a shared task proposed at the Evalita 2020 evaluation campaign. The AMI challenge, based on Italian tweets, is organized into two subtasks: (1) Subtask A about misogyny and aggressiveness identiﬁcation and (2) Subtask B about the fairness of the model. At the end of the evaluation phase, we received a total of 20 runs for Subtask A and 11 runs for Subtask B, submitted by 8 teams. In this paper, we present an overview of the AMI shared task, the datasets, the evaluation method-ology, the results obtained by the participants and a discussion about the method-ology adopted by the teams. Finally, we draw some conclusions and discuss future work.
Effects of Malaysian strains of Toxoplasma gondii on behaviours and their possible risk in schizophrenia-like rat model

Toxoplasma gondii (T. gondii) is a protozoan parasite that reside majorly in the brain of its intermediate host. T. gondii infected rodent’s shows some degree of behaviour deficits, while T. gondii infection in humans is associated with psychiatric problems such as schizophrenia. The present study aimed to evaluate the effects of Malaysian strains of T. gondii on rats. Forty five, four weeks old, male Wistar rats were used. The rats were assigned into five groups: two control groups (CG1 and CG2) and three experimental groups (EG1, EG2, EG3). CG1 rats received phosphate buffered saline (PBS), CG2 received MK-801 (as a model for schizophrenia), EG1, EG2, EG3 received orally 5 × 103 single T. gondii oocysts strain of type I, type II and type III respectively. After infection, all the five groups of rats were tested for T. gondii antibodies at two weeks post-infection (PI). Behavioural tests of exploratory activity (open field) and spatial learning and memory retention (Morris water maze) were performed on the ninth and tenth weeks PI followed by histological staining of rat brain. T. gondii IgM antibodies were detected in EG1, EG2 and EG3, but not in CG1 and CG2. The behaviour results demonstrated that rats from CG2, EG1, EG2 and EG3 had increased in their locomotor activities and memory deficits compared to control, while learning remain intact. Moreover, tissue cysts were found widely distributed exclusively in the whole brain of EG1, EG2 and EG3 without tropism. These findings taken together, implies that Malaysian strains of T. gondii are implicated in some causes of behaviour changes that are responsible for schizophrenia-like conditions if humans were infected.
Quantitative assessment of grassland foliar moisture parameters as an inference on rangeland condition in the mesic rangelands of southern Africa

ABSTRACT Understanding Grass Water Content (GWC) is critical in assessing rangelands health as well as utilization rates and patterns. Spatial explicit information on grassland ecology, particularly in southern Africa remains rudimentary due to the lack of affordable data primary sources and robust user-friendly data processing algorithms. These are required to enable efficient and routine monitoring and assessment of GWC to inform policy and management. The Sentinel 2 Multispectral Instrument (MSI) in conjunction with environmental variables acquired at moderately high resolution, provide an attractive primary data source for determining grassland water content at landscape scale. This study, therefore, evaluated the utility of Sentinel 2 MSI data (e.g. Red-Edge (RE) and the Shortwave Infrared (SWIR) bands and environmental variables) in estimating GWC, herein referred to as Canopy Water Content (CWC), Dry Moisture Content (DMC), Canopy Storage Capacity (CSC), and Live Fuel Moisture Content (LFMC) in the Mesic rangelands of southern Africa. The results illustrated that both the RE and SWIR bands were critical in characterizing GWC variables in rangelands. Further, the best models for estimating CWC, DMC, CSC and LMFC yielded a relative root-mean-square error (rRMSE) of 20.8%, 19.5%, 9.3% and 18.5%, respectively. The most optimal variables for characterizing these plant selected variables included RE and SWIR bands centred at 740 nm (B6), 783 nm (B7), 1610 nm (B11) and 2190 nm (B12) as well as their derivatives in concert with environmental variables such as Topographic Wetness Index, maximum curvature and aspect. These findings are critical towards developing a robust, efficient and spatially explicit monitoring framework for southern African rangelands.
How Granularity of Orthography-Phonology Mappings Affect Reading Development: Evidence from a Computational Model of English Word Reading and Spelling

It is widely held that children implicitly learn the structure of their writing system through statistical learning of spelling-to-sound mappings. Yet an unresolved question is how to sequence reading experience so that children can ‘pick up’ the structure optimally. We tackle this question here using a computational model of encoding and decoding. The order of presentation of words was manipulated so that they exhibited two distinct progressions of granularity of spelling-to-sound mappings. We found that under a training regime that introduced written words progressively from small-to-large granularity, the network exhibited an early advantage in reading acquisition as compared to a regime introducing written words from large-to-small granularity. Our results thus provide support for the grain size theory (Ziegler and Goswami, 2005) and demonstrate that the order of learning can inﬂuence learning trajectories of literacy skills.
UNITOR @ Sardistance2020: Combining Transformer-based Architectures and Transfer Learning for Robust Stance Detection

English. This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (Sardistance) task within the context of EVALITA 2020. UNITOR implements a transformer-based architecture whose accuracy is improved by adopting a Transfer Learning technique. In particular, this work investigates the possible contribution of three auxiliary tasks related to Stance Detection, i.e., Sentiment Detection, Hate Speech Detection and Irony Detection. Moreover, UNITOR relies on an additional dataset automatically downloaded and labeled through distant supervision. The UNITOR system ranked first in Task A within the competition. This confirms the effectiveness of Transformer-based architectures and the beneficial impact of the adopted strategies. Italiano. Questo lavoro descrive UNITOR, uno dei sistemi partecipanti allo Stance Detection in Italian tweet (SardiStance) task. UNITOR implementa un’architettura neurale basata su Transformer, la cui accuratezza viene migliorata applicando un metodo di Transfer Learning, che sfrutta le informazioni di tre task ausiliari, ovvero Sentiment Detection, Hate Speech Detection e Irony Detection. Inoltre, l’addestramento di UNITOR puó contare su un insieme di dati scaricati ed etichettati automaticamente applicando un semplice metodo di Distant Supervision. Il sistema si é classificato al primo posto nella competizione, confermando l’efficacia delle architetture basate su Transformer e il contributo delle strategie
Depression of substantia nigra dopamine transmission is driven by retrograde neurotensin release and is enhanced by methamphetamine self-administration

Midbrain dopamine neurons play central roles in reward learning and motivated behavior, and inhibition at somatodendritic dopamine D2 receptor (D2R) synapses blunts psychostimulant reinforcement. Release of the neuropeptide neurotensin in the midbrain increases following methamphetamine exposure and induces long-term depression of D2R synaptic currents (LTDDA), however the source of neurotensin that drives LTDDA is not known. Here we show that LTDDA is driven by neurotensin released by dopamine neurons. Optogenetic stimulation of dopamine neurons was sufficient to induce LTDDA in the substantia nigra, but not the ventral tegmental area, and was dependent on neurotensin receptors, postsynaptic calcium, and vacuolar-type H+-ATPase activity in the postsynaptic cell. Further, LTDDA was enhanced in mice that had self-administered methamphetamine. These findings reveal a novel form of signaling between dopamine neurons involving release of the peptide neurotensin, which may act as a feed forward mechanism to increase dopamine neuron excitability and methamphetamine self-administration.
SentNA @ ATE_ABSITA: Sentiment Analysis of Customer Reviews Using Boosted Trees with Lexical and Lexicon-based Features (short paper)

English. This paper describes our submission to the tasks on Sentiment Analysis of ATE ABSITA (Aspect Term Extraction and Aspect-Based Sentiment Analysis). In particular, we focused on Task 3 using an approach based on combining frequency of words with lexicon-based polarities and uses Boosted Trees to predict the sentiment score. This approach achieved a competitive error and, thanks to the interpretability of the building blocks, allows us to show the what elements are considered when making the prediction. We also joined Task 1 proposing a hybrid model that joins rule-based and machine learning methodologies in order to combine the advantages of both. The model proposed for Task 1 is only preliminary. Italiano. Questo articolo descrive la nostra sottomissione ai tasks sulla Sentiment Analysis ATE ABSITA (Aspect Term Extraction and Aspect-Based Sentiment Analysis). I nostri sforzi si sono concentrati sul Task 3 per il quale abbiamo adottato gli alberi di predizione (Boosted Trees) utilizzando come features di ingresso una combinazione basata sulla frequenza delle parole con la polarità derivate da un lessico. L’approccio raggiunge un errore competitivo e, grazie all’interpretabilità dei moduli intermedi, ci consente di analizzare in dettaglio gli elementi che caratterizzano maggiormente la fase di predizione. Una proposta è stata realizzata anche per il Task 1, dove abbiamo sviluppato un modello ibrido che Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). combina un approcio basato su regole con tecniche Machine Learning. Il modello sviluppato per il Task 1 è solo in fase pre-
The connectome predicts resting-state functional connectivity across the Drosophila brain

Connectomic datasets have emerged as invaluable tools for understanding neural circuits in many systems. What constraints does the connectome place on information processing and routing in a large scale neural circuit? For mesoscale brain networks, the relationship between cell and synaptic level connectivity and brain function is not well understood. Here, we use data from the Drosophila connectome in conjunction with whole-brain in vivo imaging to relate structural and functional connectivity in the central brain. We find that functional connectivity is strongly associated with the strength of both direct and indirect anatomical pathways. We also show that some brain regions, including the mushroom body and central complex, show considerably higher functional connectivity to other brain regions than is predicted based on their direct anatomical connections. We find several key topological similarities between mesoscale brain networks in flies and mammals, revealing conserved principles relating brain structure and function.
Overprotective Training Environments Fall Short at Testing Time: Let Models Contribute to Their Own Training

Despite important progress, conversational systems often generate dialogues that sound unnatural to humans. We conjecture that the reason lies in their different training and testing conditions: agents are trained in a controlled "lab" setting but tested in the "wild". During training, they learn to generate an utterance given the human dialogue history. On the other hand, during testing, they must interact with each other, and hence deal with noisy data. We propose to fill this gap by training the model with mixed batches containing both samples of human and machine-generated dialogues. We assess the validity of the proposed method on GuessWhat? , a visual referential game.
Properties of organo‐clay/natural rubber nanocomposites: Effects of organophilic modifiers

The effect of various modifiers on the structure and properties of clay/natural rubber nanocomposites are investigated with the aim to evaluate the effect of size and structure of the modifier. Nanocomposites are prepared by melt intercalation method. Mechanical properties of the cured rubber containing nanoclay are compared with the reference compound without the filler. No improvement of mechanical properties is observed for small organic cations; however, stress and strain at break of clay/rubber nanocomposites increase with rising number of octyl chains in the interlayer spaces of organo-clays. Concerning organo-cations with the same number of carbon atoms, more effective are the modifiers with several shorter carbon chains compared to those with one long chain. The composites exhibit hybrid structure of nanocomposite and microcomposite as revealed by X-ray powder diffraction (XRD), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). The details of the structure are supported by DMTA and hysteresis measurements. V C 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 127: 3447-3455, 2013
DIACR-Ita @ EVALITA2020: Overview of the EVALITA2020 Diachronic Lexical Semantics (DIACR-Ita) Task

This paper describes the first edition of the “Diachronic Lexical Seman-tics” (DIACR-Ita) task at the EVALITA2020 campaign. The task challenges participants to develop systems that can automatically detect if a given word has changed its meaning over time, given con-textual information from corpora.The task, at its first edition, attracted 9 participant teams and collected a total of 36 sub-mission runs
YNU_OXZ @ HaSpeeDe 2 and AMI : XLM-RoBERTa with Ordered Neurons LSTM for Classification Task at EVALITA 2020

English. This paper describes the system that team YNU OXZ submitted for EVALITA 2020. We participate in the shared task on Automatic Misogyny Identification (AMI) and Hate Speech Detection (HaSpeeDe 2) at the 7th evaluation campaign EVALITA 2020. For HaSpeeDe 2, we participate in Task A Hate Speech Detection and submitted two-run results for the news headline test and tweets headline test, respectively. Our submitted run is based on the pre-trained multilanguage model XLM-RoBERTa, and input into Convolution Neural Network and K-max Pooling (CNN + K-max Pooling). Then, an Ordered Neurons LSTM (ONLSTM) is added to the previous representation and submitted to a linear decision function. Regarding the AMI shared task for the automatic identification of misogynous content in the Italian language. We participate in subtask A about Misogyny & Aggressive Behaviour Identification. Our system is similar to the one defined for HaSpeeDe and is based on the pre-trained multi-language model XLMRoBERTa, an Ordered Neurons LSTM (ON-LSTM), a Capsule Network, and a final classifier.