Miracle-dz/UniFAR-FTUs huggingface dataset:
Huawei AARC’s Submissions to the WMT21 Biomedical Translation Task: Domain Adaption from a Practical Perspective

This paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation systems and submissions to the WMT21 biomedical translation shared task. Four of the submissions achieve state-of-the-art BLEU scores based on the official-released automatic evaluation results (EN->FR, EN<->IT and ZH->EN). We perform experiments to unveil the practical insights of the involved domain adaptation techniques, including finetuning order, terminology dictionaries, and ensemble decoding. Issues associated with overfitting and under-translation are also discussed.
Nest Volume and Distance between Nests Do Not Affect Population Size or Species Richness of the Termitophilous Corotocini Fauna

Termite nests act as shelters for a wide variety of invertebrates. We studied the Corotocini termitophile fauna (Staphylinidae, Aleocharinae) in nine Nasutitermes corniger nests and nine N. ephratae nests to test the resource size (larger nests should host more social-parasite species than smaller ones) and resource concentration (nests occurring closer to each other should host a greater richness and variety of social parasites) hypotheses. In total, we found 358 Corotocini individuals. Regression analysis did not reveal a relationship between species richness or population size of termitophiles and nest volume. Thus, we rejected the resource size hypotheses as a driver of termitophile diversity. Neither did distance between nests explain the richness of their fauna; thus, the resource concentration hypothesis was also rejected. In conclusion, we suggest that biological aspects, such as dispersal dynamics and species traits likely explain the diversity of termitophiles.
PROMT Systems for WMT21 Terminology Translation Task

This paper describes the PROMT submissions for the WMT21 Terminology Translation Task. We participate in two directions: English to French and English to Russian. Our final submissions are MarianNMT-based neural systems. We present two technologies for terminology translation: a modification of the Dinu et al. (2019) soft-constrained approach and our own approach called PROMT Smart Neural Dictionary (SmartND). We achieve good results in both directions.
Remote Interpreting: Platform Testing in a University Setting

This work is based on the testing of a remote interpreting (RI) delivery platform conducted a year before the disruptive COVID-19 pandemic outbreak, and aimed at assessing the use and experience of such systems in a university setting. A survey was administered to the different groups of users (interpreters, audience, and speakers) involved in two tests to collect their responses and remarks, and assess trends and perceptions in their experience. According to emerging findings of the research project, RI was already considered to be an indisputable yet burgeoning resource for conference settings with potential convenience and benefits for each group of users. However, participants’ remarks early suggested that all the parties involved in the industry need to collaborate to effectively improve and enhance such services. Specific training on RI modalities would also appear to be increasingly necessary for interpreters to adapt to new raising working conditions and meet a thriving demand—and training institutions would ever more have to offer adequate solutions, while this technological shift also requires receptiveness and adaptability to an abruptly diversifying and evolving profession.
Illinois Japanese \leftrightarrow English News Translation for WMT 2021

This system paper describes an end-to-end NMT pipeline for the Japanese \leftrightarrow English news translation task as submitted to WMT 2021, where we explore the efficacy of techniques such as tokenizing with language-independent and language-dependent tokenizers, normalizing by orthographic conversion, creating a politeness-and-formality-aware model by implementing a tagger, back-translation, model ensembling, and n-best reranking. We use parallel corpora provided by WMT 2021 organizers for training, and development and test data from WMT 2020 for evaluation of different experiment models. The preprocessed corpora are trained with a Transformer neural network model. We found that combining various techniques described herein, such as language-independent BPE tokenization, incorporating politeness and formality tags, model ensembling, n-best reranking, and back-translation produced the best translation models relative to other experiment systems.
Speech Activity Detection from Stereotactic EEG

Recent studies have shown promise for designing Brain-Computer Interfaces (BCIs) to restore speech communication for those suffering from neurological injury or disease. Numerous BCIs have been developed to reconstruct different aspects of speech, such as phonemes and words, from brain activity. However, many challenges remain toward the successful reconstruction of continuous speech from brain activity during speech imagery. Here, we investigate the potential of differentiating speech and non-speech using intracranial brain activity in different frequency bands acquired by stereotactic EEG. The results reveal statistically significant information in the alpha and theta bands for detecting voice activity, and that using a combination of multiple frequency bands further improves performance with over 92% accuracy. Furthermore, the model is causal and can be implemented with low-latency for future closed-loop experiments. These preliminary findings show the potential of cross-frequency brain signal features for detecting speech activity to enhance speech decoding and synthesis models.
Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task

In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics Shared Task. With this year’s focus on Multidimensional Quality Metric (MQM) as the ground-truth human assessment, our aim was to steer COMET towards higher correlations with MQM. We do so by first pre-training on Direct Assessments and then fine-tuning on z-normalized MQM scores. In our experiments we also show that reference-free COMET models are becoming competitive with reference-based models, even outperforming the best COMET model from 2020 on this year’s development data. Additionally, we present COMETinho, a lightweight COMET model that is 19x faster on CPU than the original model, while also achieving state-of-the-art correlations with MQM. Finally, in the “QE as a metric” track, we also participated with a QE model trained using the OpenKiwi framework leveraging MQM scores and word-level annotations.
Novel Driver’s Drowsiness Detection System and its Evaluation in a Driving Simulator Environment

Driver’s drowsiness is one of the leading causes of traffic accidents. Drowsiness can be detected using brain waves (EEG) because it reflects the cognitive state of the brain. However, using EEG for drowsiness detection in vehicles is still impractical. The non-contact brain signal sensor NBM (Neuro-Bio Monitor), introduced by Freer Logic, has a potential to detect the driver’s drowsiness while driving due to its unobtrusive nature of brain signal sensing. In this study, a novel driver drowsiness detection algorithm was introduced using NBM signals. In addition, the feasibility of the NBM-based drowsiness detection algorithm in a driving simulator environment was evaluated by comparison with conventional methods including the eyelid closure ratio (PERCLOS) and the self-reported Karolinska Sleepiness Scale (KSS). The study recruited 14 healthy adults and asked them to drive about 70 minutes on a night drive mode. NBM signals were recorded simultaneously with PERCLOS and KSS. The algorithm shows an accuracy of 78.79% and a detection rate of 95% by comparison with KSS based drowsiness. It also shows a correlation of more than 70% with PERCLOS. This study demonstrated that NBM can be used in vehicle to detect driver drowsiness during driving.
Stress Assessment and Mitigation using fNIRS and Binaural Beat Stimulation

This paper investigates binaural beat stimulation (BBs) on mitigating mental stress levels at the workplace. We developed an experimental protocol to induce stress levels by performing Stroop Color-Word Task (SCWT) under time pressure and negative feedback. Then, we mitigated the levels of stress using 16 Hz BBs. The level of stress was assessed by utilizing Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, and behavioral responses. We quantified the level of stress using statistical analysis, functional connectivity based on Phase Locking Value (PLV), and support vector machines (SVM) classifier. We found that BBs has significantly improved the accuracy of target detection by 27.35 %, (p<0.005) and reduced the cortisol level. The classification results showed that the SVM technique with PLV features differentiates between three levels of mental states (control, stress and mitigation) with an average accuracy of 65.22%, and sensitivity of 81.79% and specificity of 80.10%.
Data analysis methods for neuroimaging data pre-processing to decode cognitive tasks using logistic regression for BCI applications

Brain-Computer Interfaces (BCI) permit neural activity to be directly interpreted and used for applications, like therapeutic replacement of lost function (e.g. stroke) or to supplement existing function (e.g. handsfree applications). Two major challenges for BCI are accurate interpretation of neural activity and signal processing speed for real-time applications i.e. correctly decode a user’s intent and the timely execution of that intent. Magnetoencephalography (MEG) has advantages over Electroencephalography (EEG) with respect to spatial and temporal resolution which could potentially allow better decoding of brain activity. High spatial and temporal resolution using MEG generates a large volume of data which must be rapidly preprocessed and classified correctly for practical realtime BCI. This paper presents a simple data processing technique to clean, normalise and reduce data dimensionality, for optimal class label decoding using a simple Logistic Regression classifier. Good decoding performance was achieved using an off-line MEG dataset, with or without data dimensionality reduction, comparable to more complex data pre-processing methods and classifiers already studied.
Proposal of a New Test Problem for Large-Scale Multi- and Many-Objective Optimization

The research on large-scale multi- and many-objective optimization has received increasing attention in the evolutionary multi-objective optimization (EMO) community. A number of large-scale EMO algorithms based on different strategies (e.g., divide-and-conquer, coevolution, and dimensionality reduction) have been proposed over the last decade. The performance of the large-scale EMO algorithms was empirically evaluated using several benchmark test suites, including the ZDT, DTLZ, WFG, MaF, UF and LSMOP test suites. Even though these test suites are theoretically scalable to any number of decision variables, they are not necessarily appropriate for examining the performance of large-scale EMO algorithms. In fact, among these benchmark test suites, only the LSMOP test suite is specifically designed to test the performance of large-scale EMO algorithms. In this paper, we propose a new scalable multi- and many-objective test problem for examining large-scale EMO algorithms. The proposed test problem has the following features: 1) the number of objectives and decision variables can be arbitrarily specified; 2) the interaction strength among the objectives can be adjusted by a correlation parameter. The performance of six EMO algorithms is examined on the new test problem. Our experimental results show that the proposed new test problem poses difficulties to some state-of-the-art large-scale EMO algorithms.
Spectrum Analysis of Inherited Metabolic Disorders for Expanded Newborn Screening in a Central Chinese Population

Neonatal inherited metabolic disorders (IMDs) are closely associated with early neonatal death and abnormal growth and development. Increasing attention has been paid to IMDs because of their high incidence and diversity. However, there are no reports about the incidence of IMDs in Changsha, China. Therefore, we retrospectively analyzed the screening results of neonates to evaluate the characteristics of IMDs in the area. From January 2016 to December 2020, 300,849 neonates were enrolled for expanded newborn screening by tandem mass spectrometry in the Neonatal Disease Screening Center of the Changsha Hospital for Maternal & Child Health Care. Newborns with mild initial results were recalled for repeated tests; if the second test was still positive, the patient was referred for confirmatory tests. A total of 71 confirmed cases were identified in our study, with an incidence rate of 1:4,237. There were 28 cases of amino acid metabolic disorders, representing 39.44% of the IMDs diagnosed, with an incidence rate of 1:10,745. Twelve newborns were diagnosed with organic acid metabolic disorders, accounting for 16.66% of IMDs, with an incidence rate of 1:25,071. There were 31 cases of fatty acid oxidation disorders, representing 43.05% of IMDs, with an incidence rate of 1:9,705. Overall, 14 types of IMDs were found in Changsha. The most common disorders in the region were primary carnitine deficiency, hyperphenylalaninemia and short-chain acyl-CoA dehydrogenase deficiency. Their incidence rate is respectively 1:13,675, 1:16,714 and 1:42,978. The mutations in PAH, SLC22A5, and ACADS are the leading causes of IMDs in this area. This study demonstrates the importance of utilizing MS/MS in IMD screening for early diagnosis and treatment. This strategy may be used for prenatal genetic counseling to avoid irreversible growth and intellectual development disorders in children.
Humanized CD30-Targeted Chimeric Antigen Receptor T Cells Exhibit Potent Preclinical Activity Against Hodgkin’s Lymphoma Cells

CD30-directed chimeric antigen receptors (CARs) with single chain antibody fragment (scFv)-binding domains from murine HRS3 show strong cytotoxicity to Hodgkin’s Lymphoma cells and have been used in clinical trials. However, murine scFv in CAR might induce specific rejective immune responses in patients, which compromises the therapeutic effects. The use of human or humanized antibody fragments for CAR construction, rather than those derived from mouse antibodies, can reduce the immunogenicity of the CAR. Importantly, this strategy might simultaneously decrease the risk of cytokine-mediated toxicities and improve CAR T cell persistence. Murine HRS3 antibody has been successfully humanized by grafting the complementarity-determining regions (CDRs) from the mouse antibody framework onto human immunoglobulin consensus sequences, followed by an in vitro evolutionary strategy to select functional Fab fragments with the same affinity as murine sources. In this study, humanized scFvs were utilized to construct a CD30-directed CAR (hHRS3-CAR), and its effectiveness was compared with that of HRS3-CAR. The hHRS3-CAR-T cells specifically kill CD30-positive tumor cell lines in vitro and eliminate lymphoma xenografts in immunodeficient mice with comparable efficiency to HRS3-CAR. The hHRS-CAR-T could be used in clinical trials based on the previously reported advantages of humanized CARs, such as the reduction of immune rejection and better persistence of cells.
Differences in Demographic, Risk, and Protective Factors in a Clinical Sample of Children who Experienced Sexual Abuse Only vs. Poly-victimization

Children exposed to child sexual abuse (CSA) vary considerably with regards to their presenting concerns and treatment needs. One factor creating heterogeneity amongst children experiencing CSA is their history of experiencing other victimizations (i.e., poly-victimized or not). However, little is known about risk factors for poly-victimization as well as differences in protective factors among these two groups. Additionally, there is currently limited understanding of whether poly-victimization is associated with greater trauma symptoms in children exposed to CSA and being seen for trauma treatment. Using a clinical sample of 117 children who were sexually abused (64 CSA only and 53 poly-victimized) ranging from age 3–18 years, the current study examined demographic characteristics, abuse characteristics, trauma symptoms, and protective factors using casefile review methodology. After accounting for other risk factors, parental abuse history and protective factors were significantly associated with child poly-victimization status. Children exposed to poly-victimization were more likely to have financial concerns χ(1,115)2 = 4.16, p = 0.04, parents with abuse histories χ(1,117)2 = 8.93, p = 0.003, and parents with histories of mental health or substance use difficulties χ(1,117)2 = 4.02, p = 0.045. Although cumulative trauma symptoms scores were higher for children who were poly-victimized compared to CSA only, t(115) = −2.24, p = 0.03, multiple regression analyses showed that poly-victimization status was not significantly associated with child trauma symptoms after accounting for other demographic and abuse characteristics. Assessing and understanding the extent to which children exposed to CSA have experienced other forms of maltreatment is critical for identifying children who may be most at risk of poor outcomes.
Probiotic Properties of Bifidobacterium longum KABP042 and Pediococcus pentosaceus KABP041 Show Potential to Counteract Functional Gastrointestinal Disorders in an Observational Pilot Trial in Infants

Functional gastrointestinal disorders (FGIDs) are a common concern during the first year of life. Recognized as gut-brain axis disorders by Rome IV criteria, FGIDs etiology is linked to altered gut-brain interaction, intestinal physiology, and microbiota. In this regard, probiotics have emerged as a promising therapy for infant FGIDs. In this study, we have investigated the probiotic potential of the strains Bifidobacterium longum KABP042 and Pediococcus pentosaceus KABP041—isolated from healthy children’s feces—in the treatment of FGIDs. To this scope, genome sequences of both strains were obtained and subjected to in silico analyses. No virulence factors were detected for any strain and only the non-transferable erm(49) gene, which confers resistance to erythromycin and clindamycin, was identified in the genome of B. longum KABP042. Safety of both strains was confirmed by acute oral toxicity in rats. In vitro characterization revealed that the strains tolerate gastric and bile challenges and display a great adhesion capacity to human intestinal cells. The two strains mediate adhesion by different mechanisms and, when combined, synergically induce the expression of Caco-2 tight junction proteins. Moreover, growth inhibition experiments demonstrated the ability of the two strains alone and in combination to antagonize diverse Gram-negative and Gram-positive bacterial pathogens during sessile and planktonic growth. Pathogens’ inhibition was mostly mediated by the production of organic acids, but neutralization experiments strongly suggested the presence of additional antimicrobial compounds in probiotic culture supernatants such as the bacteriocin Lantibiotic B, whose gene was detected in the genome of B. longum KABP042. Finally, an exploratory, observational, pilot study involving 36 infants diagnosed with at least one FGID (infant colic and/or functional constipation) showed the probiotic formula was well tolerated and FGID severity was significantly reduced after 14 days of treatment with the 2 strains. Overall, this work provides evidence of the probiotic and synergic properties of strains B. longum KABP042 and P. pentosaceus KABP041, and of their potential to treat pediatric FGIDs. Clinical Trial Registration: [www.ClinicalTrials. gov], [identifier NCT04944628].
Type 2 Inflammation in Eosinophilic Esophagitis: From Pathophysiology to Therapeutic Targets

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease of the esophagus characterized clinically by symptoms related to esophageal dysfunction and histologically by eosinophil-predominant inflammation, whose incidence is rising. It significantly affects patients’ quality of life and, if left untreated, results in fibrotic complications. Although broad consensus has been achieved on first-line therapy, a subset of patients remains non-responder to standard therapy. The pathogenesis of EoE is multifactorial and results from the complex, still mostly undefined, interaction between genetics and intrinsic factors, environment, and antigenic stimuli. A deep understanding of the pathophysiology of this disease is pivotal for the development of new therapies. This review provides a comprehensive description of the pathophysiology of EoE, starting from major pathogenic mechanisms (genetics, type 2 inflammation, epithelial barrier dysfunction, gastroesophageal reflux, allergens, infections and microbiota) and subsequently focusing on the single protagonists of type 2 inflammation (involved cells, cytokines, soluble effectors, surface proteins and transcription factors) that could represent present and future therapeutic targets, while summarizing previous therapeutic approaches in literature.
Impaired Topographic Organization in Patients With Idiopathic Blepharospasm

Background: Idiopathic blepharospasm (BSP) is a common adult-onset focal dystonia. Neuroimaging technology can be used to visualize functional and microstructural changes of the whole brain. Method: We used resting-state functional MRI (rs-fMRI) and graph theoretical analysis to explore the functional connectome in patients with BSP. Altogether 20 patients with BSP and 20 age- and gender-matched healthy controls (HCs) were included in the study. Measures of network topology were calculated, such as small-world parameters (clustering coefficient [Cp], the shortest path length [Lp]), network efficiency parameters (global efficiency [Eglob], local efficiency [Eloc]), and the nodal parameter (nodal efficiency [Enod]). In addition, the least absolute shrinkage and selection operator (LASSO) regression was adopted to determine the most critical imaging features, and the classification model using critical imaging features was constructed. Results: Compared with HCs, the BSP group showed significantly decreased Eloc. Imaging features of nodal centrality (Enod) were entered into the LASSO method, and the classification model was constructed with nine imaging nodes. The area under the curve (AUC) was 0.995 (95% CI: 0.973–1.000), and the sensitivity and specificity were 95% and 100%, respectively. Specifically, four imaging nodes within the sensorimotor network (SMN), cerebellum, and default mode network (DMN) held the prominent information. Compared with HCs, the BSP group showed significantly increased Enod in the postcentral region within the SMN, decreased Enod in the precentral region within the SMN, increased Enod in the medial cerebellum, and increased Enod in the precuneus within the DMN. Conclusion: The network model in BSP showed reduced local connectivity. Baseline connectomic measures derived from rs-fMRI data may be capable of identifying patients with BSP, and regions from the SMN, cerebellum, and DMN may provide key insights into the underlying pathophysiology of BSP.