Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of EEG Data Revealing Relationships With Cognitive and Motor Symptoms in Parkinson's Disease: A Systematic Review

Recent research regards the electroencephalogram (EEG) as a promising method to study real-time brain dynamic changes in patients with Parkinson's disease (PD), but a deeper understanding is needed to discern coincident pathophysiology, patterns of changes, and diagnosis. This review summarized recent research on EEG characterization related to the cognitive and motor functions in PD patients and discussed its potential to be used as diagnostic biomarkers. Thirty papers out of 220 published from 2010 to 2020 were reviewed. Movement abnormalities and cognitive decline are related to changes in EEG spectrum and event-related potentials (ERPs) during typical oddball paradigms and/or combined motor tasks. Abnormalities in β and δ frequency bands are, respectively the main manifestation of dyskinesia and cognitive decline in PD. The review showed that PD patients have noteworthy changes in specific EEG characterizations, however, the underlying mechanism of the interrelation between gait and cognitive is still unclear. Understanding the specific nature of the relationship is essential for development of novel invasive clinical diagnostic and therapeutic methods.
The Social Role of Vocal Complexity in Striped Dolphins

Many gregarious species require complex patterns of communication for maintaining coordinated behaviors, articulated social structure and group cohesion. In mammal species, social complexity has been considered the driving force for the development of advanced acoustic communication systems. Striped dolphins are highly social, showing large group size with females maintaining strong bonds with kin. Here, we tested the hypothesis that more complex acoustic pattern plays a key role in social activity in the striped dolphins. The production rate of clicks, whistles and burst pulses, and the acoustic features of whistles have been related to the activity context (feeding, traveling, resting, and socializing). Furthermore, complex calls, consisting of a combination of frequency-modulated, and/or pulsed components were detected. Higher whistles and burst pulses production rates were recorded during socializing. Also, the social activity can be discriminated basing on the modulation of the whistle contour. Biphonic calls were especially recorded during social interaction events, suggesting that these phenomena can encode information about individual or group identity to conspecifics. Outcomes indicate the pivotal role of vocal complexity during social context and elicit further investigations of the communication system of small odontocetes from local to wider spatial scale.
CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging

More than 13 million people suffer from ischemic cerebral stroke worldwide each year. Thrombolytic treatment can reduce brain damage but has a narrow treatment window. Computed Tomography Perfusion imaging is a commonly used primary assessment tool for stroke patients, and typically the radiologists will evaluate resulting parametric maps to estimate the affected areas, dead tissue (core), and the surrounding tissue at risk (penumbra), to decide further treatments. Different work has been reported, suggesting thresholds, and semi-automated methods, and in later years deep neural networks, for segmenting infarction areas based on the parametric maps. However, there is no consensus in terms of which thresholds to use, or how to combine the information from the parametric maps, and the presented methods all have limitations in terms of both accuracy and reproducibility. We propose a fully automated convolutional neural network based segmentation method that uses the full four-dimensional computed tomography perfusion dataset as input, rather than the pre-filtered parametric maps. The suggested network is tested on an available dataset as a proof-of-concept, with very encouraging results. Cross-validated results show averaged Dice score of 0.78 and 0.53, and an area under the receiver operating characteristic curve of 0.97 and 0.94 for penumbra and core respectively.
Prognostic and Predictive Value of an Immunoscore Signature in Glioblastoma Multiform

Background Although increasing evidence shows that immune infiltration plays an essential role in glioblastoma (GBM), current prognostic indicators do not accurately represent the risk of immune cells infiltration in patients. It is therefore critical to identify new prognostic markers for GBM. Here, we investigated the effectiveness of using immunoscore to improve risk stratification and prediction of prognosis in GBM patients receiving chemotherapy. Methods Using mRNA microarrays and CIBERSORT, we analyzed 22 types of immune cell fractions in 517 GBM samples and characterized an immunoscore using the least absolute shrinkage and selection operator (LASSO) Cox regression model based on the fraction of immune cell types and patients’ overall survival. The prognostic and predictive accuracy of immunoscore was verified in the validation cohort and the entire cohort. Results Using the LASSO model, an immunoscore was developed to classify patients into High and Low immunoscore groups in the training cohort (P < 0.0001) based on the fraction of eight immune cell types. The immunoscore performance was validated in the validation cohort (P < 0.0001) and the entire cohort (P < 0.0001). Furthermore, a nomogram comprising age, IDH1 status, and immunoscore was generated to predict one- and three-year survival rates in the training cohort. The predictive value of the immunoscore was also confirmed in the validation cohort and the entire cohort (C-index: 0.66, 0.67, and 0.68, respectively). In addition, we concluded that patients in the low-immunoscore group may benefit from adjuvant chemotherapy for GBM. Conclusion Immunoscore, an immune-infiltration-based signature, is a reliable prognostic and predictive tool for GBM.
Intrinsic Connectivity Networks in the Self- and Other-Referential Processing

Neuroimaging studies have revealed a multitude of brain regions associated with self- and other-referential processing, but the question how the distinction between self, close other, and distant other is processed in the brain still remains unanswered. The default mode network (DMN) is the primary network associated with the processing of self, whereas task-positive networks (TPN) are indispensable for the processing of external objects. We hypothesize that self- and close-other-processing would engage DMN more than TPN, whereas distant-other-processing would engage TPN to a greater extent. To test this hypothesis, we used functional magnetic resonance imaging (fMRI) functional connectivity data obtained in the course of a trait adjective judgment task while subjects evaluated themselves, the best friend, a neutral stranger, and an unpleasant person. A positive association between the degree of self-relatedness and the degree of DMN dominance was revealed in cortical midline structures (CMS) and the left lateral prefrontal cortex. Relative to TPN, DMN showed greater connectivity in me than in friend, in friend than in stranger, and in stranger than in unpleasant conditions. These results show that the less the evaluated person is perceived as self-related, the more the balance of activity in the brain shifts from the DMN to the TPN.
Non-invasive Brain Stimulation for the Treatment of Gilles de la Tourette Syndrome

Gilles de la Tourette Syndrome is a multifaceted neuropsychiatric disorder typically commencing in childhood and characterized by motor and phonic tics. Its pathophysiology is still incompletely understood. However, there is convincing evidence that structural and functional abnormalities in the basal ganglia, in cortico-striato-thalamo-cortical circuits, and some cortical areas including medial frontal regions and the prefrontal cortex as well as hyperactivity of the dopaminergic system are key findings. Conventional therapeutic approaches in addition to counseling comprise behavioral treatment, particularly habit reversal therapy, oral pharmacotherapy (antipsychotic medication, alpha-2-agonists) and botulinum toxin injections. In treatment-refractory Tourette syndrome, deep brain stimulation, particularly of the internal segment of the globus pallidus, is an option for a small minority of patients. Based on pathophysiological considerations, non-invasive brain stimulation might be a suitable alternative. Repetitive transcranial magnetic stimulation appears particularly attractive. It can lead to longer-lasting alterations of excitability and connectivity in cortical networks and inter-connected regions including the basal ganglia through the induction of neural plasticity. Stimulation of the primary motor and premotor cortex has so far not been shown to be clinically effective. Some studies, though, suggest that the supplementary motor area or the temporo-parietal junction might be more appropriate targets. In this manuscript, we will review the evidence for the usefulness of repetitive transcranial magnetic stimulation and transcranial electric stimulation as treatment options in Tourette syndrome. Based on pathophysiological considerations we will discuss the rational for other approaches of non-invasive brain stimulation including state informed repetitive transcranial magnetic stimulation.
PcsR2 Is a LuxR-Type Regulator That Is Upregulated on Wheat Roots and Is Unique to Pseudomonas chlororaphis

LuxR solos are common in plant-associated bacteria and increasingly recognized for playing important roles in plant-microbe interkingdom signaling. Unlike the LuxR-type transcriptional regulators of prototype LuxR/LuxI quorum sensing systems, luxR solos do not have a LuxI-type autoinducer synthase gene associated with them. LuxR solos in plant-pathogenic bacteria are important for virulence and in plant endosymbionts contribute to symbiosis. In the present study, we characterized an atypical LuxR solo, PcsR2, in the biological control species Pseudomonas chlororaphis 30–84 that is highly conserved among sequenced P. chlororaphis strains. Unlike most LuxR solos in the plant-associated bacteria characterized to date, pcsR2 is not associated with a proline iminopeptidase gene and the protein has an atypical N-terminal binding domain. We created a pcsR2 deletion mutant and used quantitative RT-PCR to show that the expression of pcsR2 and genes in the operon immediately downstream was upregulated ∼10-fold when the wild type strain was grown on wheat roots compared to planktonic culture. PcsR2 was involved in upregulation. Using a GFP transcriptional reporter, we found that expression of pcsR2 responded specifically to root-derived substrates as compared to leaf-derived substrates but not to endogenous AHLs. Compared to the wild type, the mutant was impaired in the ability to utilize root carbon and nitrogen sources in wheat root macerate and to colonize wheat roots. Phenazine production and most biofilm traits previously shown to be correlated with phenazine production also were diminished in the mutant. Gene expression of several of the proteins in the phenazine regulatory network including PhzR, Pip (phenazine inducing protein) and RpeA/RpeB were reduced in the mutant, and overexpression of these genes in trans restored phenazine production in the mutant to wild-type levels, indicating PcsR2 affects the activity of the these regulatory genes upstream of RpeA/RpeB via an undetermined mechanism. Our results indicate PcsR2 upregulates the expression of the adjacent operon in response to unknown wheat root-derived signals and belongs to a novel subfamily of LuxR-type transcriptional regulators found in sequenced P. chlororaphis strains.
Overcoming of Radioresistance in Non-small Cell Lung Cancer by microRNA-320a Through HIF1α-Suppression Mediated Methylation of PTEN

Background Radioresistance is a major challenge in the use of radiotherapy for the treatment of lung cancer while microRNAs (miRs) have been reported to participate in multiple essential cellular processes including radiosensitization. This study was conducted with the main objective of investigating the potential role of miR-320a in radioresistance of non-small cell lung cancer (NSCLC) via the possible mechanism related to HIF1α, KDM5B, and PTEN. Methods Firstly, NSCLC radiosensitivity-related microarray dataset GSE112374 was obtained. Then, the expression of miR-320a, HIF1α, KDM5B, and PTEN was detected in the collected clinical NSCLC samples, followed by Pearson’s correlation analysis. Subsequently, ChIP assay was conducted to determine the content of the PTEN promoter fragment enriched by the IgG antibody and H3K4me3 antibody. Finally, a series of in vitro and in vivo assays were performed in order to evaluate the effects of miR-320a on radioresistance of NSCLC with the involvement of HIF1α, KDM5B, and PTEN. Results The microarray dataset GSE112374 presented with a high expression of miR-320a in NSCLC radiosensitivity samples, which was further confirmed in our clinical samples with the use of reverse transcription-quantitative polymerase chain reaction. Moreover, miR-320a negatively targeted HIF1α, inhibiting radioresistance of NSCLC. Interestingly, miR-320a suppressed the expression of KDM5B, and KDM5B was found to enhance the radioresistance of NSCLC through the downregulation of PTEN expression. The inhibition of miR-320a in radioresistance of NSCLC was also reproduced by in vivo assay. Conclusion Taken together, our findings were suggestive of the inhibitory effect of miR-320a on radioresistance of NSCLC through HIF1α-suppression mediated methylation of PTEN.
The Secretive Life of Neutrophils Revealed by Intravital Microscopy

Neutrophils are the most abundant circulating leukocyte within the blood stream and for many years the dogma has been that these cells migrate rapidly into tissues in response to injury or infection, forming the first line of host defense. While it has previously been documented that neutrophils marginate within the vascular beds of the lung and liver and are present in large numbers within the parenchyma of tissues, such as spleen, lymph nodes, and bone marrow (BM), the function of these tissue resident neutrophils under homeostasis, in response to pathogen invasion or injury has only recently been explored, revealing the unexpected role of these cells as immunoregulators or immune helpers and also unraveling their heterogeneity and plasticity. Neutrophils are highly motile cells and the use of intravital microscopy (IVM) to image cells within their environment with little manipulation has dramatically increased our understanding of the function, migratory behavior, and interaction of these short-lived cells with other innate and adaptive immune cells. Contrary to previous dogma, these studies have shown that marginated and tissue resident neutrophils are the first responders to pathogens and injury, critical in limiting the spread of infection and contributing to the orchestration of the subsequent immune response. The interplay of neutrophils, with other neutrophils, leukocytes, and stroma cells can also modulate and tune their early and late response in order to eradicate pathogens, minimize tissue damage, and, in certain circumstances, contribute to tissue repair. In this review, we will follow the extraordinary journey of neutrophils from their origin in the BM to their death, exploring their role as tissue resident cells in the lung, spleen, lymph nodes, and skin and outlining the importance of neutrophil subsets, their functions under homeostasis, and in response to infection. Finally, we will comment on how understanding these processes in greater detail at a molecular level can lead to development of new therapeutics.
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating

Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance. Being able to collect personal experience related to pharmaceutical product use could help us gain insight into how the human body reacts to different medications. Twitter, a popular social media service, is being considered as an important alternative data source for collecting personal experience information with medications. Identifying personal experience tweets is a challenging classification task in natural language processing. In this study, we utilized three methods based on Facebook’s Robustly Optimized BERT Pretraining Approach (RoBERTa) to predict personal experience tweets related to medication use: the first one combines the pre-trained RoBERTa model with a classifier, the second combines the updated pre-trained RoBERTa model using a corpus of unlabeled tweets with a classifier, and the third combines the RoBERTa model that was trained with our unlabeled tweets from scratch with the classifier too. Our results show that all of these approaches outperform the published methods (Word Embedding + LSTM) in classification performance (p < 0.05), and updating the pre-trained language model with tweets related to medications could even improve the performance further.
Evaluation of Coreference Resolution Systems Under Adversarial Attacks

A substantial overlap of coreferent mentions in the CoNLL dataset magnifies the recent progress on coreference resolution. This is because the CoNLL benchmark fails to evaluate the ability of coreference resolvers that requires linking novel mentions unseen at train time. In this work, we create a new dataset based on CoNLL, which largely decreases mention overlaps in the entire dataset and exposes the limitations of published resolvers on two aspects—lexical inference ability and understanding of low-level orthographic noise. Our findings show (1) the requirements for embeddings, used in resolvers, and for coreference resolutions are, by design, in conflict and (2) adversarial approaches are sometimes not legitimate to mitigate the obstacles, as they may falsely introduce mention overlaps in adversarial training and test sets, thus giving an inflated impression for the improvements.
Identifying and Classifying Third-party Entities in Natural Language Privacy Policies

App developers often raise revenue by contracting with third party ad networks, which serve targeted ads to end-users. To this end, a free app may collect data about its users and share it with advertising companies for targeting purposes. Regulations such as General Data Protection Regulation (GDPR) require transparency with respect to the recipients (or categories of recipients) of user data. These regulations call for app developers to have privacy policies that disclose those third party recipients of user data. Privacy policies provide users transparency into what data an app will access, collect, shared, and retain. Given the size of app marketplaces, verifying compliance with such regulations is a tedious task. This paper aims to develop an automated approach to extract and categorize third party data recipients (i.e., entities) declared in privacy policies. We analyze 100 privacy policies associated with most downloaded apps in the Google Play Store. We crowdsource the collection and annotation of app privacy policies to establish the ground truth with respect to third party entities. From this, we train various models to extract third party entities automatically. Our best model achieves average F1 score of 66% when compared to crowdsourced annotations.
Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles

We propose the technique of quasi-multitask learning (Q-MTL), a simple and easy to implement modification of standard multitask learning, in which the tasks to be modeled are identical. With this easy modification of a standard neural classifier we can get benefits similar to an ensemble of classifiers with a fraction of the resources required.We illustrate it through a series of sequence labeling experiments over a diverse set of languages, that applying Q-MTL consistently increases the generalization ability of the applied models. The proposed architecture can be regarded as a new regularization technique that encourages the model to develop an internal representation of the problem at hand which is beneficial to multiple output units of the classifier at the same time. Our experiments corroborate that by relying on the proposed algorithm, we can approximate the quality of an ensemble of classifiers at a fraction of computational resources required. Additionally, our results suggest that Q-MTL handles the presence of noisy training labels better than ensembles.
Global Bootstrapping Neural Network for Entity Set Expansion

Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision. Recent end-to-end bootstrapping approaches have shown their advantages in information capturing and bootstrapping process modeling. However, due to the sparse supervision problem, previous end-to-end methods often only leverage information from near neighborhoods (local semantics) rather than those propagated from the co-occurrence structure of the whole corpus (global semantics). To address this issue, this paper proposes Global Bootstrapping Network (GBN) with the “pre-training and fine-tuning” strategies for effective learning. Specifically, it contains a global-sighted encoder to capture and encode both local and global semantics into entity embedding, and an attention-guided decoder to sequentially expand new entities based on these embeddings. The experimental results show that the GBN learned by “pre-training and fine-tuning” strategies achieves state-of-the-art performance on two bootstrapping datasets.
Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest

We present CUT, a dataset for studying Civil Unrest on Twitter. Our dataset includes 4,381 tweets related to civil unrest, hand-annotated with information related to the study of civil unrest discussion and events. Our dataset is drawn from 42 countries from 2014 to 2019. We present baseline systems trained on this data for the identification of tweets related to civil unrest. We include a discussion of ethical issues related to research on this topic.
Parsing Gapping Constructions Based on Grammatical and Semantic Roles

A gapping construction consists of a coordinated structure where redundant elements are elided from all but one conjuncts. This paper proposes a method of parsing sentences with gapping to recover elided elements. The proposed method is based on constituent trees annotated with grammatical and semantic roles that are useful for identifying elided elements. Our method outperforms the previous method in terms of F-measure and recall.
“A Little Birdie Told Me ... " - Social Media Rumor Detection

The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works, we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media fine-tuning of BERT allow for better language understanding, thus yielding an F1 score of 58.7 on the SemEval 2019 task on rumour stance detection.