Miracle-dz/UniFAR-FTUs huggingface dataset:
Estrogen-Related Receptor γ Agonist DY131 Ameliorates Lipopolysaccharide-Induced Acute Liver Injury

Sepsis-associated liver dysfunction remains a challenge in clinical practice with high mortality and limited specific therapies. DY131 is a pharmacological agonist of the orphan receptor estrogen-related receptor (ERR) γ which plays a crucial role in regulating energy generation, oxidative metabolism, cell apoptosis, inflammatory responses, etc. However, its role in acute liver injury is unknown. In this study, we evaluated the effect of DY131 on lipopolysaccharide (LPS)-induced liver injury. Mice were pretreated with DY131 through intraperitoneal injection at a dose of 5 mg/kg/day for 3 days prior to LPS challenge (10 mg/kg). 24 h later, they were anesthetized and sacrificed. Blood and liver tissues were collected for further studies. In a separate experiment, mice were treated with saline (vehicle) or DY131 for 3 days to evaluate the toxicity of DY131. We found that ERRγ was downregulated in the liver tissues from LPS-treated mice. Pretreatment with DY131 ameliorated LPS-induced liver injury as demonstrated by reduced liver enzyme release (ALT, AST, and LDH), improved liver morphological damage, and attenuated oxidative stress, inflammation and apoptosis. Meanwhile, DY131 had no significant side effects on hepatic and renal functions in mice. Finally, transcriptomics analysis revealed that the dysregulated pathways associated with inflammation and metabolism were significantly reversed by DY131 in LPS-treated mice, providing more evidence in favor of the protective effect of DY131 against LPS-induced liver injury. Altogether, these findings highlighted the protective effect of DY131 on LPS-induced hepatotoxicity possibly via suppressing oxidative stress, inflammation, and apoptosis.
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?

In machine reading comprehension tasks, a model must extract an answer from the available context given a question and a passage. Recently, transformer-based pre-trained language models have achieved state-of-the-art performance in several natural language processing tasks. However, it is unclear whether such performance reflects true language understanding. In this paper, we propose adversarial examples to probe an Arabic pre-trained language model (AraBERT), leading to a significant performance drop over four Arabic machine reading comprehension datasets. We present a layer-wise analysis for the transformer’s hidden states to offer insights into how AraBERT reasons to derive an answer. The experiments indicate that AraBERT relies on superficial cues and keyword matching rather than text understanding. Furthermore, hidden state visualization demonstrates that prediction errors can be recognized from vector representations in earlier layers.
Ritual, Labor Mobilization, and Monumental Construction in Small-Scale Societies

Issues of labor mobilization for monumental construction are central to inquiries based in anthropological archaeology in particular as well as collective action theory in general. Societies at all scales built monumental constructions, yet the mechanisms of labor mobilization in small-scale societies remain poorly understood. Many scholars point to kin networks as sources of labor and ritual as a means of attracting laborers. These explanations fail to point to specific mechanisms that encourage cooperative labor among large groups. In this paper, I address this issue by outlining the importance of ritualized monument construction for creating and maintaining cooperative labor parties in small-scale societies. Theoretical and ethnographic evidence illustrates the importance of the four Rs of reciprocity, reputation, retribution, and reward in ritual cooperation. This in turn provides the backdrop for interpreting the scale of ritual and monumental construction during the Woodland period (500 BCE–600 CE) in the Middle Ohio River Valley to illustrate the importance of ritualized monumental construction in small-scale societies.
CHEMICAL STABILITY STUDIES OF BIOADHESIVE TOPICAL GEL

The main purpose of this work was to study the chemical stability of optimized bioadhesive topical gel. For that Bioadhesive topical gel formulation was optimized on the basis of bioadhesive strength and in­vitro drug permeation study of prepared bioadhesive topical gel. To achieve the objectives of present study, BNTGs were prepared using natural bioadhesive polymer Aegel marmelos (plant Bale) and hydroxyethyl cellulose (HEC). Bioadhesive strength of BNTG3 was found to be 1.72±0.023 gm/cm2. BNTG3 permeates only 89.00%±0.59% over 24 h. The shelf life of BNTG3 was found to be 0.439 years and 0.302 years at 25±2°C and 40±2°C, respectively. Degradation half‐life of BNTG3 was found to be 2.902 years and 1.996 years at 25±2°C and 40±2°C, respectively. Activation energy was found to be 4631.7003 cal/mol. All the data was found to be extremely significant by studying one way ANOVA at p<0.05 level. In last it was concluded that, BNTG3 will be more effective and stable if it is stored at or below 25±2°C. So, BNTG3 was successfully developed and standardized for improved topical therapeutics.
EEG-fMRI Signal Coupling Is Modulated in Subjects With Mild Cognitive Impairment and Amyloid Deposition

Cognitive impairment indicates disturbed brain physiology which can be due to various mechanisms including Alzheimer's pathology. Combined functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings (EEG-fMRI) can assess the interplay between complementary measures of brain activity and EEG changes to be localized to specific brain regions. We used a two-step approach, where we first examined changes related to a syndrome of mild cognitive impairment irrespective of pathology and then studied the specific impact of amyloid pathology. After detailed clinical and neuropsychological characterization as well as a positron emission tomography (PET) scans with the tracer 11-[C]-Pittsburgh Compound B to estimate cerebral amyloid deposition, 14 subjects with mild cognitive impairment (MCI) (mean age 75.6 SD: 8.9) according to standard criteria and 21 cognitively healthy controls (HCS) (mean age 71.8 SD: 4.2) were assessed with EEG-fMRI. Thalamo-cortical alpha-fMRI signal coupling was only observed in HCS. Additional EEG-fMRI signal coupling differences between HCS and MCI were observed in parts of the default mode network, salience network, fronto-parietal network, and thalamus. Individuals with significant cerebral amyloid deposition (amyloid-positive MCI and HCS combined compared to amyloid-negative HCS) displayed abnormal EEG-fMRI signal coupling in visual, fronto-parietal regions but also in the parahippocampus, brain stem, and cerebellum. This finding was paralleled by stronger absolute fMRI signal in the parahippocampus and weaker absolute fMRI signal in the inferior frontal gyrus in amyloid-positive subjects. We conclude that the thalamocortical coupling in the alpha band in HCS more closely reflects previous findings observed in younger adults, while in MCI there is a clearly aberrant coupling in several networks dominated by an anticorrelation in the posterior cingulate cortex. While these findings may broadly indicate physiological changes in MCI, amyloid pathology was specifically associated with abnormal fMRI signal responses and disrupted coupling between brain oscillations and fMRI signal responses, which especially involve core regions of memory: the hippocampus, para-hippocampus, and lateral prefrontal cortex.
Parsing Argumentative Structure in English-as-Foreign-Language Essays

This paper presents a study on parsing the argumentative structure in English-as-foreign-language (EFL) essays, which are inherently noisy. The parsing process consists of two steps, linking related sentences and then labelling their relations. We experiment with several deep learning architectures to address each task independently. In the sentence linking task, a biaffine model performed the best. In the relation labelling task, a fine-tuned BERT model performed the best. Two sentence encoders are employed, and we observed that non-fine-tuning models generally performed better when using Sentence-BERT as opposed to BERT encoder. We trained our models using two types of parallel texts: original noisy EFL essays and those improved by annotators, then evaluate them on the original essays. The experiment shows that an end-to-end in-domain system achieved an accuracy of . 341. On the other hand, the cross-domain system achieved 94% performance of the in-domain system. This signals that well-written texts can also be useful to train argument mining system for noisy texts.
FuzzyBIO: A Proposal for Fuzzy Representation of Discontinuous Entities

Discontinuous entities pose a challenge to named entity recognition (NER). These phenomena occur commonly in the biomedical domain. As a solution, expansions of the BIO representation scheme that can handle these entity types are commonly used (i.e. BIOHD). However, the extra tag types make the NER task more difficult to learn. In this paper we propose an alternative; a fuzzy continuous BIO scheme (FuzzyBIO). We focus on the task of Adverse Drug Response extraction and normalization to compare FuzzyBIO to BIOHD. We find that FuzzyBIO improves recall of NER for two of three data sets and results in a higher percentage of correctly identified disjoint and composite entities for all data sets. Using FuzzyBIO also improves end-to-end performance for continuous and composite entities in two of three data sets. Since FuzzyBIO improves performance for some data sets and the conversion from BIOHD to FuzzyBIO is straightforward, we recommend investigating which is more effective for any data set containing discontinuous entities.
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool

Few shot learning with large language models has the potential to give individuals without formal machine learning training the access to a wide range of text to text models. We consider how this applies to creative writers and present Story Centaur, a user interface for prototyping few shot models and a set of recombinable web components that deploy them. Story Centaur’s goal is to expose creative writers to few shot learning with a simple but powerful interface that lets them compose their own co-creation tools that further their own unique artistic directions. We build out several examples of such tools, and in the process probe the boundaries and issues surrounding generation with large language models.
InterpreT: An Interactive Visualization Tool for Interpreting Transformers

With the increasingly widespread use of Transformer-based models for NLU/NLP tasks, there is growing interest in understanding the inner workings of these models, why they are so effective at a wide range of tasks, and how they can be further tuned and improved. To contribute towards this goal of enhanced explainability and comprehension, we present InterpreT, an interactive visualization tool for interpreting Transformer-based models. In addition to providing various mechanisms for investigating general model behaviours, novel contributions made in InterpreT include the ability to track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeddings through illustrative plots, and identify task-related functions of attention heads by using new metrics. InterpreT is a task agnostic tool, and its functionalities are demonstrated through the analysis of model behaviours for two disparate tasks: Aspect Based Sentiment Analysis (ABSA) and the Winograd Schema Challenge (WSC).
Paladin: an annotation tool based on active and proactive learning

In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets. By integrating active learning and proactive learning to the annotation task, Paladin makes the task less time-consuming and requiring less human effort. Although Paladin is designed for multi-label settings, the system is flexible and can be adapted to other tasks in single-label settings.
SLTEV: Comprehensive Evaluation of Spoken Language Translation

Automatic evaluation of Machine Translation (MT) quality has been investigated over several decades. Spoken Language Translation (SLT), esp. when simultaneous, needs to consider additional criteria and does not have a standard evaluation procedure and a widely used toolkit. To fill the gap, we develop SLTev, an open-source tool for assessing SLT in a comprehensive way. SLTev reports the quality, latency, and stability of an SLT candidate output based on the time-stamped transcript and reference translation into a target language. For quality, we rely on sacreBLEU which provides MT evaluation measures such as chrF or BLEU. For latency, we propose two new scoring techniques. For stability, we extend the previously defined measures with a normalized Flicker in our work. We also propose a new averaging of older measures. A preliminary version of SLTev was used in the IWSLT 2020 shared task. Moreover, a growing collection of test datasets directly accessible by SLTev are provided for system evaluation comparable across papers.
Unsupervised Approach to Multilingual User Comments Summarization

User commenting is a valuable feature of many news outlets, enabling them a contact with readers and enabling readers to express their opinion, provide different viewpoints, and even complementary information. Yet, large volumes of user comments are hard to filter, let alone read and extract relevant information. The research on the summarization of user comments is still in its infancy, and human-created summarization datasets are scarce, especially for less-resourced languages. To address this issue, we propose an unsupervised approach to user comments summarization, which uses a modern multilingual representation of sentences together with standard extractive summarization techniques. Our comparison of different sentence representation approaches coupled with different summarization approaches shows that the most successful combinations are the same in news and comment summarization. The empirical results and presented visualisation show usefulness of the proposed methodology for several languages.
Contribution of Matrix Metalloproteinase-1 Genotypes to Colorectal Cancer in Taiwan

Background/Aim: Matrix metalloproteinase-1 is responsible for extracellular matrix regulation, and its genetic role in colorectal cancer (CRC) is unclear. The aim of the study was to investigate the contribution of Matrix metalloproteinase-1 genotypes to CRC risk in Taiwan. Materials and Methods: A total of 362 cases and 362 controls were included and their MMP-1 -1607 (rs1799705) genotypes were examined. The environmental factors and clinical-pathological records were also analyzed. Results: The genotypic frequency of MMP-1 rs1799750 were different between the CRC and control groups (p for trend=0.0083). 1G/2G and 1G/1G were associated with lower risk (p=0.0438 and 0.0030, adjusted OR=0.73 and 0.54, 95%CI=0.54-0.90 and 0.37-0.83). Among non-smokers, those with 1G/2G and 1G/1G genotypes were at 0.70- and 0.48-fold odds of having CRC. Among non-alcohol drinkers, people with 1G/2G and 1G/1G genotypes were at 0.71- and 0.54-fold odds. The 1G/1G genotype were statistically lower among CRC patients with lymph node metastasis (7.2%) than those without (19.0%). Conclusion: The genotypes at MMP-1 rs1799705 play a role in determining susceptibility to CRC risk in Taiwan.
Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models

Synthetic data generation is widely known to boost the accuracy of neural grammatical error correction (GEC) systems, but existing methods often lack diversity or are too simplistic to generate the broad range of grammatical errors made by human writers. In this work, we use error type tags from automatic annotation tools such as ERRANT to guide synthetic data generation. We compare several models that can produce an ungrammatical sentence given a clean sentence and an error type tag. We use these models to build a new, large synthetic pre-training data set with error tag frequency distributions matching a given development set. Our synthetic data set yields large and consistent gains, improving the state-of-the-art on the BEA-19 and CoNLL-14 test sets. We also show that our approach is particularly effective in adapting a GEC system, trained on mixed native and non-native English, to a native English test set, even surpassing real training data consisting of high-quality sentence pairs.
Diversité des fourmis (Hymenoptera, Formicidae) dans la forêt de Yakouren (Algérie) : Estimation de la richesse, biogéographie et taxonomie

Résumé: Nous avons échantillonné des fourmis dans la forêt de Yakouren en combinant trois méthodes: pots barber, chasse à vue et appâts alimentaires. Vingt-sept (27) taxons ont été répertoriés appartenant aux sous-familles suivantes: Myrmicinae, Formicinae et Dolichoderinae. Camponotus alii, Pheidole pallidula et Crematogaster auberti levithorax sont les espèces les plus abondantes. Le calcul de différents estimateurs de richesse (Chao 1, Jackknife 1 et ICE) montrent qu'il y a toujours une possibilité de rencontrer de nouvelles espèces en augmentant l'effort d'échantillonnage. La composition des peuplements de fourmis dans le massif de Yakouren révèle une faible dégradation du milieu par rapport à des relevés réalisés dans les années 1960. Le milieu souffre des actions anthropiques, dont des incendies, que subit la région depuis plusieurs décennies. Les statuts taxonomiques de Camponotus lateralis purius et d'Aphaenogaster sardoa ujhelyii sont discutés.
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet

In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCOEX takes into account the challenging characteristics of ConceptNet, namely that – unlike conventional knowledge graphs – nodes are represented as non-canonicalized, free-form text. This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled. A commonly used shortcut to circumvent these problems is to apply string matching. We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph.
Expression of P16 and KI-67 in cervical intraepithelial neoplasia and carcinoma: An institute based study

Background: Cervical cancer is one of the leading causes of cancer death in Indian women Cervical cancer screening programme worldwide are moving from primary cytology to human papilloma virus (HPV) DNA testing, which is the major risk factor for cervical carcinoma. Objectives: To study the expression of P16 and KI-67 in cervical intraepithelial lesions and carcinomas and to calculate diagnostic accuracy of these biomarkers as compared to routine histopathology findings on equivocal cases Material and Methods: Evaluation for p16 and ki-67 immunoexpression was done in 102 cervical tissue samples. Informed consent, details of the patient regarding age, occupation, religion, parity, symptoms, any prior abnormal pap test, HPV DNA test if any, etc. were taken before procuring the samples. Results: The benign lesions were negative for both p16 and ki-67 immunoexpression whereas the premalignant lesions showed increasing positivity with the higher histological grade. All the malignant lesions were positive for ki 67 and p16 except for one case The specificity was highest when both the markers were used together. Conclusion: Correlation of p16 and ki-67 biomarkers and histopathology findings increases the diagnostic accuracy of premalignant and malignant cervical lesions which may be useful in cases with equivocal histologic features Keywords: Cervical intra epithelial neoplasia, Pap smear, Histopathology.
Overcoming Charge Collection Limitation at Solid/Liquid Interface by a Controllable Crystal Deficient Overlayer

Bulk and surface charge recombination of photoelectrode are two key processes that significantly hinder solar‐to‐fuel conversion of photoelectrochemical cell (PEC). In this study, the function of a “crystal‐deficient” overlayer is unveiled, which outperforms a traditionally used amorphous or crystalline overlayer in PEC water splitting by exhibiting a high conductivity and large electron diffusion length to enable unlimited electron collection. The optimized ≈2.5 nm thickness of the “crystal‐deficient” shell results in a depletion layer with a width of 3 nm, which overcomes the flat band limitation of the photovoltage and increases the light absorptivity in the wavelength range from 300 to 420 nm. In addition, a 50‐fold increase in the conductivity yields a one‐order‐of‐magnitude increase in the diffusion length of an electron (Ln)(≈20 μm), allowing for unlimited electron collection in the 1.9 μm TiO2 nanowire array with the “crystal‐deficient” shell. The controllable “crystal‐deficient” overlayer in rutile TiO2 nanowires photoanode achieves a photocurrent density greater than 2.0 mA cm−2 at 1.23 V versus reversible hydrogen electrode (RHE), a 1.18% applied bias photon‐to‐current efficiency at 0.49 V versus RHE, a faradaic efficiency greater than 93.5% at 0.6 V versus Pt under air mass 1.5G simulated solar light illumination (100 mW cm−2).