Miracle-dz/UniFAR-FTUs huggingface dataset:
Rare case of bilateral anterolateral and symmetrical bowing of tibia successfully treated with a distal tibial opening wedge osteotomy

Abstract The anterolateral bowing of the tibia is closely associated with the development of its pseudarthrosis. Roughly, all deformities are unilateral so the shortening and angulation are easy to identify. We present a 6-year-old boy with an exuberant bilateral anterolateral bowing of tíbia. He has short stature, disturbed gait and callosity at the lateral border of the foot. Deformity was successfully treated by opening wedge tibia osteotomy and filled the remaining gap with structural fibular graft. It was fixed with two crossed K-wires and cast immobilization for 6 weeks. We decided to correct it before skeletal maturity due to the significant disturbance of the gait and esthetic impairment. It was obtained a satisfactory morphological and functional result with a simple and fast technique.
Silene sunhangii (Caryophyllaceae), a new species from China

Abstract Silene sunhangii, a new species of Caryophyllaceae known from only three populations in Hubei and Hunan provinces of central China, is described. Both morphological and molecular data were used to assess the taxonomic status and relationships of this species. Morphologically, S. sunhangii is most similar to S. platyphylla Franch. from which it differs most readily in having 3-veined elliptical leaves without pubescence, tasseled catacorolla, pale purple to red petals without a linear lobe or narrow tooth and lanceolate, bifid to one third. A phylogenetic analysis based on nuclear ITS region identified the new species as a well-supported, independent lineage. Our new species is nested within a grade that encompasses species representing a polyphyletic Silene sect. Physolychnis (Benth.) Bocquet. Both the genetic and morphological data support the recognition of Silene sunhangii as a distinct species, although there is inconsistency between these two datasets as to the relationships of the new species.
Correlation of Upper Extremity Function to Quality of Life of Primary Caregivers of Ambulatory Stroke Survivors Living in the Community

Objective:To investigate the correlation between function of the involved upper extremity and hand of the ambulatory stroke survivors living in the community independently and quality of life of their primary caregivers. Design:This was a case-control study comparing the Brunnstrom grading of the patients’ involved upper extremity and hand with SF-36 score of the caregivers. Setting:Physical Medicine & Rehabilitation Departments of a University Hospital and a Teaching Rehabilitation Center. Participants:The study included 51 stroke patients and their 51 primary caregivers. Interventions:Not applicable. Main Outcome Measures:Brunnstrom grade of the involved upper extremity and hand of the stroke survivors and SF-36 subscale scores of the primary caregivers. Results:Quality of life of the primary caregivers determined by the SF-36 scoring revealed-high correlation with the Brunnstrom grade of the patient’s upper extremity and hand. Conclusions:The independently walking stroke patients’ upper extremity and hand function is highly correlated with the quality of life of their primary caregivers. Independence in walking may not be an enhancing factor for the quality of life of the primary caregiver.
The Characteristics and Survival of Patients with Mesorectum Metastatic Lymph Nodes from Cervical Cancer

Purpose To analyze the characteristics and survival of patients with mesorectum metastatic lymph nodes (MLNs) from cervical cancer. Patients and methods We retrospectively reviewed 1194 consecutive patients with FIGO stage IA–IVA cervical cancer who were treated with definitive radiotherapy between January 2011 and December 2015 in our institute. Patients were treated with intensity-modulated radiation therapy (IMRT) and brachytherapy, combined with concurrent chemotherapy. Mesorectum, pelvic and para-aortic MLNs were boosted 59–61Gy with IMRT. We calculated the clinical characteristics and survival of the patients with and without mesorectum MLNs and made comparisons between them. Results The incidence rate of mesorectum MLNs was 0.8% (10/1194). The incidence rates for patients with FIGO stages I, II, III, and IVA disease were 0%, 0.4%, 2.0%, and 33.3%, respectively. Mesorectum MLNs were associated with more advanced stage (p <0.001); larger tumor size (p = 0.002), para-aortic MLNs (p <0.001), common iliac MLNs (p <0.001), and bilateral pelvic MLNs (p <0.001). All patients with mesorectum MLNs experienced treatment failure and died during follow-up. The median overall survival was 10.43 months. The 2-year overall survival (OS), disease-free survival (DFS), and local control (LC) rates were 10%, 0%, and 30%, respectively. Multivariate analysis showed that that mesorectum MLNs (hazard ratio, HR 4.0, 95% CI 1.8–9.1, p=0.001), para-aortic MLNs (HR 1.8, 95% CI 1.1–2.8, p=0.017) and pelvic MLNs (HR 2.5, 95% CI 1.7–3.7, p<0.001) were independent prognostic factors of OS. Conclusion Mesorectum MLNs from cervical cancer are rare. And, the survival of patients with mesorectum MLNs was poor after dose-escalated concurrent chemoradiotherapy.
Heterozygous Disruption of Beclin 1 Alleviates Zinc Oxide Nanoparticles-Induced Disturbance of Cholesterol Biosynthesis in Mouse Liver

Purpose Liver is regarded as one of the primary target organs for zinc oxide nanoparticles (ZnONPs) toxicity. Since liver represents the leading site for de novo cholesterol biosynthesis in mammals, the injuries of liver could result in the disruption of cholesterol biosynthesis. In this study, we aimed to investigate whether pulmonary ZnONPs exposure induces disturbance of cholesterol biosynthesis in mouse liver. Methods and results Our data demonstrated intratracheally instilled with a single dose of 3, 6, and 12 μg/animal ZnONPs could induce histopathological deterioration in mouse liver in a dose-related manner at 3 days, but remission was observed at 7 days after treatment. Moreover, ZnONPs caused the disturbance of cholesterol biosynthesis by increasing both 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase and sterol regulatory element-binding protein 2 (SREBP2) protein expressions. To further reveal the underlying toxic mechanisms, we detected the biomarkers of autophagy and found that pulmonary ZnONPs exposure led to the elevation of LC3B-II and Beclin 1, suggesting ZnONPs might trigger autophagy in liver tissues. By using both beclin 1+/+ and beclin 1+/- mice, we demonstrated that inhibition of autophagy by heterozygous disruption of beclin 1 attenuated the disturbance of cholesterol biosynthesis induced by ZnONPs in liver. Conclusion Pulmonary exposure of ZnONPs would induce the cholesterol biosynthesis disturbance in mouse liver through Beclin-1-dependent autophagy activation, suggesting that inhibition of autophagy may contribute to preventing the cholesterol biosynthesis disturbance and its associated pathologies induced by ZnONPs in liver.
PRKD2 Promotes Progression and Chemoresistance of AML via Regulating Notch1 Pathway

Introduction Progression and chemoresistance of acute myeloid leukemia (AML) contribute to most of the treatment failure. Notch pathway has been proven to be involved in many biological processes and diseases, especially AML. In this study, we aimed to explore genes correlated with Notch1 pathway in AML and determine their roles in the regulation of AML progression and chemoresistance. Methods TCGA database was used to explore Notch1 associated genes. Kaplan–Meier survival analysis was performed to evaluate the prognostic significance of genes. Quantitative RT-PCR (qRT-PCR) and Western blot were performed to examine the expression of genes. The expression of PRKD2 was up-regulated or knocked down in AML cell lines by lentivirus or siRNAs. CCK-8 and flow cytometry were used to analyze the effect of PRKD2 on cell proliferation and chemoresistance. Results Based on TCGA database, PRKD2 was found to be positively correlated with Notch1 expression, cytogenetic risk status and poorer prognosis in AML. Moreover, the expression level of PRKD2 was higher in AML chemo-resistant cells than in chemo-sensitive cells. Functionally, knockdown of PRKD2-induced apoptosis and increased chemosensitivity of AML cells. PRKD2 overexpression promoted proliferation and chemoresistance of AML cells. Furthermore, we found PRKD2 could regulate Notch1 pathway. Besides, high PRKD2 expression was correlated with higher risk group of AML patients which indicated that PRKD2 was an independent prognostic marker for AML. Conclusion Taken together, our results showed that PRKD2 could promote the proliferation and chemoresistance of AML cells by regulating Notch1 pathway. The study broadened our insights into the underlying mechanisms in chemoresistance and proliferation of AML, and provided a new prognostic marker and treatment target for AML.
Comparison of four variable selection methods to determine the important variables in predicting the prognosis of traumatic brain injury patients by support vector machine

Background: Large amounts of information have called for increased computational complexity. Data dimension reduction is therefore critical to preliminary analysis. In this research, four variable selection (VS) methods are compared to obtain the important variables in predicting the prognosis of traumatic brain injury (TBI) patients. Materials and Methods: In a retrospective follow-up study, 741 TBI patients who were hospitalized for at least 2 days and had a Glasgow Coma Scale score of at least one were followed. Their clinical data recorded during intensive care unit (ICU) admission and eight-category extended GOS conditions 6 months after discharge were utilized here. Two filter- and two wrapper-based VS methods were applied for comparison. A support vector machine (SVM) classifier was then used, and the sensitivity, specificity, accuracy, and the area under the receiver characteristic curve (AUC) values were calculated. Results: Theoretically, the variables selected by sequential forward selection (SFS) method would better predict the prognosis (AUC = 0.737, 95% confidence interval [0.701, 0.772], specificity = 89.2%, sensitivity = 58.9% and accuracy = 79.1%) than the others. Genetic algorithm (GA), minimum redundancy maximum relevance (MRMR), and mutual information method were in the next orders, respectively. Conclusion: The use of an SVM classifier on optimal subsets given by GA and SFS reveals that wrapper-based methods perform better than filter-based methods in our data set, although all selected subsets, except for the MRMR, were clinically accepted. In addition, for prognosis prediction of TBI patients, a small subset of clinical records during ICU admission is enough to achieve an accepted accuracy.
Designing a community participation management model to control the epidemic of heart coronary artery diseases for Tehran province

Background: Coronary artery disease (CAD) is the most important disease in the cardiovascular diseases and is the most important cause of death in developed and developing countries. Today, the participation of communities in government programs is considered as an important indicator of the success rate and development process of societies. This study was conducted with the aim of designing a community participation management model for control of CAD. Materials and Methods: This study was carried out practically, quantitatively, and qualitatively in seven steps. The sample consisted of 400 people. The instrument for measuring this research is a questionnaire of 35 questions that is gathered through referring to the centers and observing and interviewing and reviewing the findings of previous research. The data were analyzed using “exploratory and confirmatory factor analysis” and “Amos 24” and “SPSS 20” software. Results: A total of five factors have been identified as effective in managing people's participation in controlling the epidemic of CAD, including policy, planning, organizing, coordinating, and financing. Of these factors, policy-making and coordination have the most (0.96) and least (0.43) impact, respectively, on managing people's participation in controlling the epidemic of CADs. Conclusion: Results suggest that community-based CAD programs should be implemented and evaluated in accordance with clear rules and principles. All of the community should participate and establish close relationships with the national authorities.
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, because depending on users, it differs what they are interested in, so it is necessary to develop a method to generate various summaries according to users’ interests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation.
Generating Quantified Descriptions of Abstract Visual Scenes

Quantified expressions have always taken up a central position in formal theories of meaning and language use. Yet quantified expressions have so far attracted far less attention from the Natural Language Generation community than, for example, referring expressions. In an attempt to start redressing the balance, we investigate a recently developed corpus in which quantified expressions play a crucial role; the corpus is the result of a carefully controlled elicitation experiment, in which human participants were asked to describe visually presented scenes. Informed by an analysis of this corpus, we propose algorithms that produce computer-generated descriptions of a wider class of visual scenes, and we evaluate the descriptions generated by these algorithms in terms of their correctness, completeness, and human-likeness. We discuss what this exercise can teach us about the nature of quantification and about the challenges posed by the generation of quantified expressions.
Generating Paraphrases with Lean Vocabulary

In this work, we examine whether it is possible to achieve the state of the art performance in paraphrase generation with reduced vocabulary. Our approach consists of building a convolution to sequence model (Conv2Seq) partially guided by the reinforcement learning, and training it on the subword representation of the input. The experiment on the Quora dataset, which contains over 140,000 pairs of sentences and corresponding paraphrases, found that with less than 1,000 token types, we were able to achieve performance which exceeded that of the current state of the art.
Towards Generating Math Word Problems from Equations and Topics

A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein. In this paper, we focus on the task of generating math word problems. Previous works are mainly template-based with pre-defined rules. We propose a novel neural network model to generate math word problems from the given equations and topics. First, we design a fusion mechanism to incorporate the information of both equations and topics. Second, an entity-enforced loss is introduced to ensure the relevance between the generated math problem and the equation. Automatic evaluation results show that the proposed model significantly outperforms the baseline models. In human evaluations, the math word problems generated by our model are rated as being more relevant (in terms of solvability of the given equations and relevance to topics) and natural (i.e., grammaticality, fluency) than the baseline models.
Generating Text from Anonymised Structures

Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model’s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.
Generating Abstractive Summaries with Finetuned Language Models

Neural abstractive document summarization is commonly approached by models that exhibit a mostly extractive behavior. This behavior is facilitated by a copy-attention which allows models to copy words from a source document. While models in the mostly extractive news summarization domain benefit from this inductive bias, they commonly fail to paraphrase or compress information from the source document. Recent advances in transfer-learning from large pretrained language models give rise to alternative approaches that do not rely on copy-attention and instead learn to generate concise and abstractive summaries. In this paper, as part of the TL;DR challenge, we compare the abstractiveness of summaries from different summarization approaches and show that transfer-learning can be efficiently utilized without any changes to the model architecture. We demonstrate that the approach leads to a higher level of abstraction for a similar performance on the TL;DR challenge tasks, enabling true natural language compression.
Choosing between Long and Short Word Forms in Mandarin

Between 80% and 90% of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013). Consequently, the choice between long and short forms is a key problem for lexical choice across NLP and NLG. Following an earlier work on abbreviations in English (Mahowald et al, 2013), we bring a probabilistic perspective to these questions, using both a behavioral and a corpus-based approach. We hypothesized that there is a higher probability of choosing short form in supportive context than in neutral context in Mandarin. Consistent with our prediction, our findings revealed that predictability of contexts makes effect on speakers’ long and short form choice.
Generation of Hip-Hop Lyrics with Hierarchical Modeling and Conditional Templates

This paper addresses Hip-Hop lyric generation with conditional Neural Language Models. We develop a simple yet effective mechanism to extract and apply conditional templates from text snippets, and show—on the basis of a large-scale crowd-sourced manual evaluation—that these templates significantly improve the quality and realism of the generated snippets. Importantly, the proposed approach enables end-to-end training, targeting formal properties of text such as rhythm and rhyme, which are central characteristics of rap texts. Additionally, we explore how generating text at different scales (e.g. character-level or word-level) affects the quality of the output. We find that a hybrid form—a hierarchical model that aims to integrate Language Modeling at both word and character-level scales—yields significant improvements in text quality, yet surprisingly, cannot exploit conditional templates to their fullest extent. Our findings highlight that text generation models based on Recurrent Neural Networks (RNN) are sensitive to the modeling scale and call for further research on the observed differences in effectiveness of the conditioning mechanism at different scales.
Performance Evaluation of eICIC Scheme for Small Cell Data Offloading in Heterogeneous Cellular Network

—The global mobile data traffic is expected to reach 69 exabytes per month by 2022 at a compound annual growth rate of 45 percent. A massive number of small cells are needed to be deployed to cope with ever increase capacity demands in future wireless networks. Advanced interference mitigation techniques should be adopted with dense deployment of small cells to improve much needed capacity and enhance spectral efficiency. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. In this context Enhanced Inter Cell Interference Coordination (eICIC) factor determines the amount of radio resources that macro cells should offer to small cells. In this paper, a user’s location based, a framework to study the impact of eICIC factor in various network performance metrics is provided. We differentiate between core small cell and edge small cell in eICIC factor analysis. In addition, we investigate the impact of eICIC factor in offloaded users and centric small cell users separately. Finally numerical results using the standard 3GPP KPIs (5%- and 50%-ile users throughput, average per user throughput and overall cell throughput) are provided to confirm theoretical analysis and measure the network performance with eICIC scheme.
Real World Voice Assistant System for Cooking

This study presents a voice assistant system to support cooking by utilizing smart speakers in Japan. This system not only speaks the procedures written in recipes point by point but also answers the common questions from users for the specified recipes. The system applies machine comprehension techniques to millions of recipes for answering the common questions in cooking such as “人参はどうしたらよいですか (How should I cook carrots? )”. Furthermore, numerous machine-learning techniques are applied to generate better responses to users.
Toward a Better Story End: Collecting Human Evaluation with Reasons

Creativity is an essential element of human nature used for many activities, such as telling a story. Based on human creativity, researchers have attempted to teach a computer to generate stories automatically or support this creative process. In this study, we undertake the task of story ending generation. This is a relatively new task, in which the last sentence of a given incomplete story is automatically generated. This is challenging because, in order to predict an appropriate ending, the generation method should comprehend the context of events. Despite the importance of this task, no clear evaluation metric has been established thus far; hence, it has remained an open problem. Therefore, we study the various elements involved in evaluating an automatic method for generating story endings. First, we introduce a baseline hierarchical sequence-to-sequence method for story ending generation. Then, we conduct a pairwise comparison against human-written endings, in which annotators choose the preferable ending. In addition to a quantitative evaluation, we conduct a qualitative evaluation by asking annotators to specify the reason for their choice. From the collected reasons, we discuss what elements the evaluation should focus on, to thereby propose effective metrics for the task.