Miracle-dz/UniFAR-FTUs huggingface dataset:
Up-Regulation of p53/miR-628-3p Pathway, a Novel Mechanism of Shikonin on Inhibiting Proliferation and Inducing Apoptosis of A549 and PC-9 Non–Small Cell Lung Cancer Cell Lines

Shikonin (SHK) is a pleiotropic agent with remarkable cell growth inhibition activity against various cancer types, especially non–small cell lung cancer (NSCLC), but its molecular mechanism is still unclear. Our previous study found that miR-628-3p could inhibit the growth of A549 cells and induce its apoptosis. Bioinformatics analysis predicted that miR-628-3p promoter sequence contained p53 binding sites. Considering the regulatory effect of SHK on p53, we speculate that SHK may inhibit the growth and induce apoptosis of NSCLC cells by up-regulating miR-628-3p. CCK-8 and EdU assay confirmed the inhibitory effect of SHK on A549 and PC-9 cells. Meanwhile, quantitative reverse transcription–polymerase chain reaction and Western blot showed that SHK could promote the expression of p53 and miR-628-3p in a dose-dependent manner. Overexpression of p53 or miR-628-3p can inhibit the growth and promote apoptosis of A549 and PC-9 cells, while silencing p53 or miR-628-3p has the opposite effect. Dual luciferase reporting assay and ChIP (chromatin immunoprecipitation) assay further verified the direct interaction between p53 and the promoter of miR-628-3p. Gene knockdown for p53 or miR-628-3p confirmed that SHK inhibits the growth and induces apoptosis of A549 and PC-9 cells at least partly by up-regulating p53/miR-628-3p signaling pathway. Therefore, these novel findings provide an alternative approach to target p53/miR-628-3p axis and could be used for the development of new treatment strategies for NSCLC.
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction

Emotion cause extraction (ECE) aims to extract the causes behind the certain emotion in text. Some works related to the ECE task have been published and attracted lots of attention in recent years. However, these meth-ods neglect two major issues: 1) pay few at-tentions to the effect of document-level context information on ECE, and 2) lack of suf-ﬁcient exploration for how to effectively use the annotated emotion clause. For the ﬁrst issue, we propose a bidirectional hierarchical attention network (BHA) corresponding to the speciﬁed candidate cause clause to capture the document-level context in a structured and dynamic manner. For the second issue, we design an emotional ﬁltering module (EF) for each layer of the graph attention network, which calculates a gate score based on the emotion clause to ﬁlter the irrelevant information. Combining the BHA and EF, the EF-BHA can dynamically aggregate the contextual information from two directions and ﬁlters irrelevant information. The experimental results demonstrate that EF-BHA achieves the competitive performances on two public datasets in different languages (Chinese and English). More-over, we quantify the effect of context on emotion cause extraction and provide the visualization of the interactions between candidate cause clauses and contexts.
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation

Multi-hop relation detection in Knowledge Base Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may comprise multiple relations. Most of the existing methods treat it as a single-label learning problem while ignoring the fact that for some complex questions, there exist multiple correct relation paths in knowledge bases. Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem. However, performing multi-label multi-hop relation detection is challenging since the numbers of both the labels and the hops are un-known. To tackle this challenge, multi-label multi-hop relation detection is formulated as a sequence generation task. A relation-aware sequence relation generation model is proposed to solve the problem in an end-to-end manner. Experimental results show the effectiveness of the proposed method for relation detection and KBQA.
Reversion Mosaicism in Primary Immunodeficiency Diseases

Reversion mosaicism has been reported in an increasing number of genetic disorders including primary immunodeficiency diseases. Several mechanisms can mediate somatic reversion of inherited mutations. Back mutations restore wild-type sequences, whereas second-site mutations result in compensatory changes. In addition, intragenic recombination, chromosomal deletions, and copy-neutral loss of heterozygosity have been demonstrated in mosaic individuals. Revertant cells that have regained wild-type function may be associated with milder disease phenotypes in some immunodeficient patients with reversion mosaicism. Revertant cells can also be responsible for immune dysregulation. Studies identifying a large variety of genetic changes in the same individual further support a frequent occurrence of reversion mosaicism in primary immunodeficiency diseases. This phenomenon also provides unique opportunities to evaluate the biological effects of restored gene expression in different cell lineages. In this paper, we review the recent findings of reversion mosaicism in primary immunodeficiency diseases and discuss its clinical implications.
Impaired Functional T-Cell Response to SARS-CoV-2 After Two Doses of BNT162b2 mRNA Vaccine in Older People

Long-term care facility (LTCF) older residents display physiological alterations of cellular and humoral immunity that affect vaccine responses. Preliminary reports suggested a low early postvaccination antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aim of this study was to focus on the specific T-cell response. We quantified S1-specific IgG, neutralizing antibody titers, total specific IFNγ-secreting T cells by ELISpot, and functionality of CD4+- and CD8+-specific T cells by flow cytometry, after two doses of the BNT162b2 vaccine in younger and older people, with and without previous COVID-19 infection (hereafter referred to as COVID-19-recovered and COVID-19-naive subjects, respectively). Frailty, nutritional, and immunosenescence parameters were collected at baseline in COVID-19-naive older people. We analyzed the immune response in 129 young adults (median age 44.0 years) and 105 older residents living in a LCTF (median age 86.5 years), 3 months after the first injection. Humoral and cellular memory responses were dramatically impaired in the COVID-19-naive older (n = 54) compared with the COVID-19-naive younger adults (n = 121). Notably, older participants’ neutralizing antibodies were 10 times lower than the younger’s antibody titers (p < 0.0001) and LCTF residents also had an impaired functional T-cell response: the frequencies of IFNγ+ and IFNγ+IL-2+TNFα+ cells among specific CD4+ T cells, and the frequency of specific CD8+ T cells were lower in COVID-19-naive older participants than in COVID-19-naive young adults (p < 0.0001 and p = 0.0018, respectively). However, COVID-19-recovered older participants (n = 51) had greater antibody and T-cell responses, including IFNγ+ and IFNγ+IL-2+TNFα+-specific CD4+ T cells (p < 0.0001), as well as TNFα+-specific CD8+ T cells (p < 0.001), than COVID-19-naive older adults. We also observed that “inflammageing” and particularly high plasma levels of TNFα was associated to poor antibody response in the older participants. In conclusion, our results show that the COVID-19-naive older people had low counts and impaired specific CD4+ and CD8+ T cells, in addition to impaired antibody response, and that specific studies are warranted to assess the efficiency of SARS-CoV-2 mRNA-based vaccines, as in other immunocompromised subjects. Our study also shows that, despite their physiological alterations of immunity, vaccination is highly efficient in boosting the prior natural memory response in COVID-19-recovered older people.
Improving Embedding-based Large-scale Retrieval via Label Enhancement

Current embedding-based large-scale retrieval models are trained with 0-1 hard label that indicates whether a query is relevant to a document, ignoring rich information of the relevance degree. This paper proposes to improve embedding-based retrieval from the perspective of better characterizing the querydocument relevance degree by introducing label enhancement (LE) for the first time. To generate label distribution in the retrieval scenario, we design a novel and effective supervised LE method that incorporates prior knowledge from dynamic term weighting methods into contextual embeddings. Our method significantly outperforms four competitive existing retrieval models and its counterparts equipped with two alternative LE techniques by training models with the generated label distribution as auxiliary supervision information. The superiority can be easily observed on English and Chinese large-scale retrieval tasks under both standard and cold-start settings.
Pain and Other Neurological Symptoms Are Present at 3 Months After Hospitalization in COVID-19 Patients

COVID-19 is an ongoing pandemic with a devastating impact on public health. Acute neurological symptoms have been reported after a COVID-19 diagnosis, however, the long-term neurological symptoms including pain is not well established. Using a prospective registry of hospitalized COVID-19 patients, we assessed pain and neurological function (including functional, cognitive and psychiatric assessments) of several hospitalized patients at 3 months. Our main finding is that 60% of the patients report pain symptoms. 71% of the patients still experienced neurological symptoms at 3 months and the most common symptoms being fatigue (42%) and PTSD (25%). Cognitive symptoms were found in 12%. Our preliminary findings suggests the importance of investigating long-term outcomes and rationalizes the need for further studies investigating the neurologic outcomes and symptoms of pain after COVID-19.
Predictors of Parental Accommodation and Response Treatment in Young Children With Obsessive-Compulsive Disorder

Background: Cognitive–behavioral family-based treatment (CBFT) is the treatment standard in very young children with obsessive–compulsive disorder (OCD), which includes the same core components of cognitive–behavioral therapy (CBT) with significant family involvement. Although the latter reports high rates of remission, some children do not improve with treatments. Therefore, it is necessary to identify possible moderating variables such as comorbidity, severity of disorder, years of onset, parental anxiety, and parental accommodation. This study has two main aims: (1) to propose a predictive model on family accommodation (father and mother), taking into account variables related to the children (severity of obsessive–compulsive responses, internalizing and externalizing symptoms, and comorbidity) and with the parents before intervention (worry, accommodation of one parental member over the other) and (2) to examine the mediating role of externalizing symptoms and mother's accommodation in the relation between initial severity and improvement of severity of obsessive–compulsive responses in children aged 5–8 years. Methods: Participants comprised 56 children with OCD [mean = 6.61 (SD = 0.76)] and their parents; 79% of the sample was men. Treatment was implemented by two clinicians specialized in OCD (>15 years of experience). Clinicians were trained to administer CBT protocol in the same way. They were doctors of clinical psychology and researchers at the OCD. Results: Mother's accommodation was associated with child variables (Child Behavior Checklist–Externalizing and Initial Severity, Children's Yale–Brown Obsessive–Compulsive Scale). Father's accommodation could be explained by parent variables (mother's accommodation and worry). Simple mediation model tested using the SPSS macro PROCESS supported the relation of the initial severity of symptoms with that following intervention, through the simple indirect effect of externalizing symptoms of the child. Conclusions: Comorbidities with externalizing symptoms, father's worry, and mother's accommodation were variables that should be controlled in treatment of pediatric OCD.
Activity of PD-1 Inhibitor Combined With Anti-Angiogenic Therapy in Advanced Sarcoma: A Single-Center Retrospective Analysis

Background: Immune checkpoint inhibitors (ICIs) are employed to treat various cancers, including soft tissue sarcomas (STSs), and less than 20% of patients benefit from this treatment. Vascular endothelial growth factor (VEGF) promotes the immunosuppressive tumor microenvironment and contributes to ICI-resistant therapy. Anti-VEGF receptor tyrosine-kinase inhibitors (TKIs) combined with ICIs have shown antitumor activity in patients with alveolar soft-part sarcoma (ASPS). However, they have not been extensively studied to treat other STS subtypes, such as leiomyosarcoma (LMS), dedifferentiated liposarcoma (DDLPS), undifferentiated pleomorphic sarcoma (UPS), myxofibrosarcoma (MFS), and angiosarcoma (AS). Methods: In this retrospective study, we collected data from 61 patients who were diagnosed with advanced STS based on imaging and histology, including LMS, DDLPS, and UPS. Among them, 41 patients were treated with ICIs combined with TKIs and 20 patients received ICI therapy. The endpoints of progression-free survival (PFS) and overall response rate (ORR) were analyzed in the two groups, and the overall response [partial response (PR), stable disease (SD), and progressive disease (PD)] of each patient was determined using RECIST 1.1 evaluation criteria. Results: In total, 61 STS patients had the following subtypes: LMS (n = 20), DDLPS (n = 17), UPS (n = 8), ASPS (n = 7), MFS (n = 7), and AS (n = 2). The median PFS (mPFS) was significantly prolonged after ICI treatment in combination with TKIs (11.74 months, 95% CI 4.41–14.00) compared to ICI treatment alone (6.81 months, 95% CI 5.43–NA) (HR 0.5464, p = 0.043). The 12-month PFS rates of patients who received ICI–TKI treatment were increased from 20.26% (95% CI 0.08–0.53) to 42.90% (95% CI 0.27–0.68). In the combination therapy group, 12 patients (30%) achieved PR, 25 patients (62.5%) achieved SD, and 3 patients (7.5%) achieved PD for 3 months or longer. In the non-TKI-combination group, 2 patients (9.5%) achieved PR, 14 patients (66.7%) achieved SD, and 5 patients (23.8%) achieved PD within 3 months. The ORRs in the two groups were 30.0% (ICI–TKI combination) and 9.5% (ICI only), respectively. A notable ORR was observed in the ICI–TKI combination group, especially for subtypes ASPS (66.7%), MFS (42.9%), and UPS (33.3%). The PD-L1 expression (n = 33) and tumor mutation burden (TMB, n = 27) were determined for each patient. However, our results showed no significant difference in PFS or response rates between the two groups. Conclusion: This study suggests that ICI–TKI treatment has antitumor activity in patients with STS, particularly the ASPS and MFS subtypes. Moreover, effective biomarkers to predict clinical outcomes are urgently needed after combination therapy in the STS subtypes.
Age-Based Disparities in Metastatic Melanoma Patients Treated in the Immune Checkpoint Inhibitors (ICI) Versus Non-ICI Era: A Population-Based Study

Immune checkpoint inhibitors (ICIs) have revolutionized metastatic melanoma treatment, but our knowledge of ICI activity across age groups is insufficient. Patients in different age groups with advanced melanoma were selected based on the ICI approval time in this study. Patients with melanoma were identified in the Surveillance, Epidemiology, and End Result (SEER) database program 2004–2016. The results showed that 4,040 patients had advanced melanoma before the advent of ICI (referred to as the “non-ICI era”), whereas there were 6,188 cases after ICI approval (referred to as the “ICI era”). In all age groups, the cases were dominated by men. The differences between the first (20–59 years) and second (60–74 years) age groups in both eras were significant in terms of surgery performance and holding of insurance policies (p = 0.05). The first and second groups (20–59 and 60–70 years old, respectively) showed no difference in survival (median = 8 months) during the non-ICI era, but the difference was evident in the first, second, and third age groups in the ICI era, with the younger group (20–59 years) having significantly better survival (median = 18, 14, and 10 months, respectively, p = 0.0001). Multivariate analysis of the first group (the youngest) in the ICI era revealed that surgery was significantly associated with an increase in survival among patients compared with those who did not undergo surgery (p < 0.0001). Furthermore, having an insurance policy among all age groups in the ICI era was associated with favorable survival in the first (20–59 years) and second (60–74 years) age groups (p = 0.0001), while there were no survival differences in the older ICI group (>74 years). Although there were differences in survival between the ICI era and the non-ICI era, these results demonstrate that ICI positively affected the survival of younger patients with advanced melanoma (first age group) than it had beneficial effects on older patients. Moreover, having had cancer surgery and holding an insurance policy were positive predictors for patient survival. This study emphasizes that adequate clinical and preclinical studies are important to enhance ICI outcomes across age groups.
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task

The paradigm of leveraging large pretrained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-toend TOD modeling by adopting span prediction as an auxiliary task. In end-toend setting, our model achieves new stateof-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.
Case Report: Detection of Symptomatic Treatment-Related Changes in a Patient With Anaplastic Oligodendroglioma Using FET PET

Following local and systemic treatment of gliomas, the differentiation between glioma relapse and treatment-related changes such as pseudoprogression or radiation necrosis using conventional MRI is limited. To overcome this limitation, various amino acid PET tracers such as O-[2-(18F)-fluoroethyl]-L-tyrosine (FET) are increasingly used and provide valuable additional clinical information. We here report neuroimaging findings in a clincally symptomatic 53-year-old woman with a recurrent anaplastic oligodendroglioma with MRI findings highly suspicious for tumor progression. In contrast, FET PET imaging suggested treatment-related changes considerably earlier than the regression of contrast enhancement on MRI. In patients with oligodendroglioma, the phenomenon of symptomatic treatment-related changes is not well described, making these imaging findings unique and important for clinical decision-making.
Weighted Gene Co-Expression Network Analysis and Treatment Strategies of Tumor Recurrence-Associated Hub Genes in Lung Adenocarcinoma

Despite the recent progress of lung adenocarcinoma (LUAD) therapy, tumor recurrence remained to be a challenging factor that impedes the effectiveness of treatment. The objective of the present study was to predict the hub genes affecting LUAD recurrence via weighted gene co-expression network analysis (WGCNA). Microarray samples from LUAD dataset of GSE32863 were analyzed, and the modules with the highest correlation to tumor recurrence were selected. Functional enrichment analysis was conducted, followed by establishment of a protein–protein interaction (PPI) network. Subsequently, hub genes were identified by overall survival analyses and further validated by evaluation of expression in both myeloid populations and tissue samples of LUAD. Gene set enrichment analysis (GSEA) was then carried out, and construction of transcription factors (TF)–hub gene and drug–hub gene interaction network was also achieved. A total of eight hub genes (ACTR3, ARPC5, RAB13, HNRNPK, PA2G4, WDR12, SRSF1, and NOP58) were finally identified to be closely correlated with LUAD recurrence. In addition, TFs that regulate hub genes have been predicted, including MYC, PML, and YY1. Finally, drugs including arsenic trioxide, cisplatin, Jinfukang, and sunitinib were mined for the treatment of the eight hub genes. In conclusion, our study may facilitate the invention of targeted therapeutic drugs and shed light on the understanding of the mechanism for LUAD recurrence.
On the Effects of Transformer Size on In- and Out-of-Domain Calibration

Large, pre-trained transformer language models, which are pervasive in natural language processing tasks, are notoriously expensive to train. To reduce the cost of training such large models, prior work has developed smaller, more compact models which achieves a significant speedup in training time while maintaining competitive accuracy to the original model on downstream tasks. Though these smaller pre-trained models have been widely adopted by the community, it is not known how well are they calibrated compared to their larger counterparts. In this paper, focusing on a wide range of tasks, we thoroughly investigate the calibration properties of pre-trained transformers, as a function of their size. We demonstrate that when evaluated in-domain, smaller models are able to achieve competitive, and often better, calibration compared to larger models, while achieving signiﬁcant speedup in training time. Post-hoc calibration techniques further reduce calibration error for all models in-domain. However, when evaluated out-of-domain, larger models tend to be better calibrated, and label-smoothing instead is an effective strategy to calibrate models in this setting.
AutoEQA: Auto-Encoding Questions for Extractive Question Answering

There has been a signiﬁcant progress in the ﬁeld of extractive question answering (EQA) in the recent years. However, most of them rely on annotations of answer-spans in the corresponding passages. In this work, we address the problem of EQA when no annotations are present for the answer span, i.e., when the dataset contains only questions and corresponding passages. Our method is based on auto-encoding of the question that performs a question answering (QA) task during encoding and a question generation (QG) task during decoding. Our method performs well in a zero-shot setting and can provide an additional loss to boost performance for EQA.