Miracle-dz/UniFAR-FTUs huggingface dataset:
Dueling Chinese Couplets using Sequence to Sequence Model

—Chinese couplet is a special type of Chinese poems aiming to be both concise and meaningful. There are lots of constraints for couplets, such as couplet length, Chinese tone pattern, lexical category. Due to these strict rules, dueling Chinese couplets is considered as a difﬁcult pastime. In this paper, we aim to encode a machine that duels well Chinese couplets using Sequence to Sequence (Seq2Seq) model. More precisely, our model used bidirectional Gated Recurrent Units (GRU) for both encoder and decoder along with the attention mechanism. Divers constraints are incorporated into the model during either the training process or the evaluation process. Our model ﬁnally achieved the BLEU score 14.15. 1
A Statistical Approach to Using Remote Sensing Data to Discern Streamflow Variable Influence in the Snow Melt Dominated Upper Rio Grande Basin

Since the middle of the 20th century, the peak snowpack in the Upper Rio Grande (URG) basin of United States has been decreasing. Warming influences snowpack characteristics such as snow cover, snow depth, and Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmelt runoff-dominated river systems of the URG basin. The purpose of this research is to investigate which variables are most important in predicting naturalized streamflow and to explore variables’ relative importance for streamflow dynamics. We use long term remote sensing data for hydrologic analysis and deploy R algorithm for data processing and synthesizing. The data is analyzed on a monthly and baseflow/runoff basis for nineteen sub-watersheds in the URG. Variable importance and influence on naturalized streamflow is identified using linear standard regression with multi-model inference based on the second-order Akaike information criterion (AICc) coupled with the intercept only model. Five predictor variables: temperature, precipitation, soil moisture, sublimation, and SWE are identified in order of relative importance for streamflow prediction. The most influential variables for streamflow prediction vary temporally between baseflow and runoff conditions and spatially by watershed and mountain range. Despite the importance of temperature on streamflow, it is not consistently the most important factor in streamflow prediction across time and space. The dominance of precipitation over streamflow is more obvious during baseflow. The impact of precipitation, SWE, sublimation, and minimum temperature on streamflow is evident during the runoff season, but the results vary for different sub-watersheds. The association between sublimation and streamflow is positive in the runoff season, which may relate to temperature and requires further research. This research sheds light on the primary drivers and their spatial and temporal variability on streamflow generation. This work is critical for predicting how warming temperatures will impact water supplies serving society and ecosystems in a changing climate.
Proteomic analysis of spinal cord tissue in a rat model of cancer-induced bone pain

Background Cancer-induced bone pain (CIBP) is a moderate to severe pain and seriously affects patients’ quality of life. Spinal cord plays critical roles in pain generation and maintenance. Identifying differentially expressed proteins (DEPs) in spinal cord is essential to elucidate the mechanisms of cancer pain. Methods CIBP rat model was established by the intratibial inoculation of MRMT-1 cells. Positron emission tomography (PET) scan and transmission electron microscopy (TEM) were used to measure the stats of spinal cord in rats. Label free Liquid Chromatography with tandem mass spectrometry (LC-MS-MS) were used to analyze the whole proteins from the lumbar spinal cord. Differentially expressed proteins (DEPs) were performed using Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and verified using Western blot and immunofluorescence assay. Results In the current study, CIBP rats exhibited bone damage, spontaneous pain, mechanical hyperalgesia, and impaired motor ability. In spinal cord, an hypermetabolism and functional abnormality were revealed on CIBP rats. An increase of synaptic vesicles density in active zone and a disruption of mitochondrial structure in spinal cord of CIBP rats were observed. Meanwhile, 422 DEPs, consisting of 167 up-regulated and 255 down-regulated proteins, were identified among total 1539 proteins. GO enrichment analysis indicated that the DEPs were mainly involved in catabolic process, synaptic function, and enzymic activity. KEGG pathway enrichment analysis indicated a series of pathways, including nervous system disease, hormonal signaling pathways and amino acid metabolism, were involved. Expression change of synaptic and mitochondrial related protein, such as complexin 1 (CPLX1), synaptosomal-associated protein 25 (SNAP25), synaptotagmin 1 (SYT1), aldehyde dehydrogenase isoform 1B1 (ALDH1B1), Glycine amidinotransferase (GATM) and NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), were further validated using immunofluorescence and Western blot analysis. Conclusion This study provides valuable information for understanding the mechanisms of CIBP, and supplies potential therapeutic targets for cancer pain.
The mediating role of general academic emotions in burnout and procrastination among Chinese medical undergraduates during the COVID-19 pandemic: A cross-sectional study

Background Academic procrastination has become more prevalent during the COVID-19 pandemic due to teaching/learning changes. This phenomenon induces academic burnout, which is already serious among medical students. However, the academic emotion, which is the factor most vulnerable to changes in the academic environment, is still unknown. Therefore, the current study aimed to investigate the mediating role of general academic emotions in procrastination and burnout among Chinese medical students during the COVID-19 pandemic. Methods This cross-sectional study enrolled 995 medical students from China Medical University. We applied the Chinese version of the Maslach Burnout Inventory Student Survey (MBI-SS), the Aitken Procrastination Inventory (API) and the General Academic Emotion Questionnaire for College Students (GAEQ) to evaluate the variables of interest. We examined the mediation effects of GAEs by hierarchical linear regression analysis. Results Correlation analyses showed a significant positive correlation between procrastination and burnout. Procrastination and burnout positively and negatively correlated with negative academic emotions, respectively. Hierarchical linear regression analyses showed that procrastination had positive associations with negative academic emotions, while it had negative associations with positive academic emotions. The contributions (as mediators) of GAEs to burnout and procrastination were 21.16% (NAEs), 29.75% (PAEs), 54.25% (NDEs) and 23.69% (PDEs). Conclusions The results indicate that academic emotions had mediating effects on procrastination and burnout. Medical students' worries about the uncertainty of the learning environment may have exacerbated academic burnout. Targeted improvements in the teaching environment to communicate encouragement and reduce anxiety and helplessness among medical undergraduates for implementing medical education while preventing and controlling the infection.
Characterizing the interfacial zones of fly ash based alkali activated adhesives to various substrates

Alkali Activated Materials (AAMs) are important potential substitutes for cement in many materials. The AAMs presented here are based on industrial ground granulated blast furnace slag (GGBFS) and fly ash (FA). They are tested for their suitability as adhesives for joining concrete, ceramic tiles, a wood-based geopolymer (WGP) and a high density geopolymer (HDGP). After mixing multiple batches and performing preliminary tests of the resulting shrinkage, bending-, compressive- and pull-of-strengths to standard ceramic tiles, selected mixtures are tested for their open time and applied to bond intended substrates. The interfacial zones (ITZ) of an AAM containing additional KOH and the most comparable KOH-free AAM to concrete, ceramic tiles, WPG and HDGP are subsequently characterized in detail using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDXS). Both mechanical interlocking and areas of probable chemical bonding are identified.
Customer agility, market orientation, and brand image in the context of Chinese market

Introduction Customer agility (CA) and market orientation have been widely discussed in prior research. Also, the role of brand image in business making-decision has been emphasized. However, the current analysis lacks integrating the relationship among the three concepts. Thus, this study creatively creates a causal relationship model of CA, market orientation, and brand image. Methods Finally, 289 valid samples from the managers in Chinese market was collected for further analysis by partial least squares structural equation modeling (PLS-SEM). Results The results show that market orientation can positively affect CA (both customer-sensing capability and customer-responding capability), and CA will further have a positive influence on brand image. Also, market orientation has a direct impact on brand image. Additionally, CA (both customer-sensing capability and customer-responding capability) will mediate the impact of market orientation on brand image. Discussion The research has both theoretical and practical contributions. From the theoretical perspective, the results contribute to enriching the brand theory, strategy management, and marketing research. From the practical perspective, the investigation enlightens managers to pay attention to the roles of CA implementation and market orientation strategies in enhancing their brand image.
Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment

Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage. The same behavior happens when the M2-associated transcription factors are disturbed (STAT3 or STAT6); cycle attractors will prevent a hyper-regulation scenario implicated in providing a suitable environment for tumor growth. Therefore, here we propose that cyclic macrophage phenotypes can serve as a reservoir for balancing the phenotypes when a specific phenotype-based transcription factor is perturbed in the regulatory network of macrophages. We consider that cyclic attractors should not be simply ignored, but it is necessary to carefully evaluate their biological importance. In this work, we suggest one conjecture: the cyclic attractors can serve as a reservoir to balance the inflammatory/regulatory response of the network under external perturbations.
Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

Background Obstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension. Materials and methods We retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models. Results A total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension. Conclusion We established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society.
Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69–0.71 for MG region, 0.89–0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment.
Analysis of pre-service teachers’ argumentation-based academic writing process

The purpose of this research is to examine the participating students’ argumentation-based academic writing processes and the contributions of these processes to the students’ academic writing skills. The participants of the study, which was conducted through mixed method research design, were a group of 53 pre-service Turkish Teachers who are in their first year of Turkish education program at a state university in Turkey during the 2020–2021 academic year. In this research, the data were obtained through student products, rubrics, reflective participant diaries, and a semi-structured interview form. SPSS 23 was used in the analysis of the quantitative data, and NVivo 12 programs were used in the analysis of qualitative data. When the results of the analyses are considered in general, it can be deduced that academic writing practices based on argumentation contributed to the development of students in the dimensions of “subject and content,” “organization,” “language use,” “citation,” and “writing process. ” In the data obtained from the reflective diaries and interviews, although some difficulties were pointed out, the statements of the students regarding their development came to the fore. In addition, it was pointed out that argumentation contributes not only to students’ academic writing skills, but also their development of thinking, objectivity, research motivation, and critical thinking.
High-quality chromosome-level genome assembly of Pacific cod, Gadus macrocephalus

COPYRIGHT © 2022 Ma, Li, Jiang, Zheng, Liu and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Data Report PUBLISHED 05 December 2022 DOI 10.3389/fmars.2022. 1067526
Negative effects of abamectin on soil microbial communities in the short term

With the widespread use of abamectin in agriculture, there is increasing urgency to assess the effects of abamectin on soil microorganisms. Here, we treated plant–soil microcosms with abamectin at concentrations of 0.1 and 1.0 mg/kg and quantified the impacts of abamectin on bulk and rhizosphere soil microbial communities by shotgun metagenomics after 7 and 21 days of exposure. Although abamectin was reported to be easily degradable, it altered the composition of the soil microbial communities, disrupted microbial interactions, and decreased community complexity and stability after 7 days of exposure. After treatment with abamectin at a concentration of 1.0 mg/kg, some opportunistic human diseases, and soil-borne pathogens like Ralstonia were enriched in the soil. However, most ecological functions in soil, particularly the metabolic capacities of microorganisms, recovered within 21 days after abamectin treatment. The horizontal and vertical gene transfer under abamectin treatments increased the levels of antibiotic resistance genes dissemination. Overall, our findings demonstrated the negative effects of abamectin on soil ecosystems in the short-term and highlight a possible long-term risk to public and soil ecosystem health associated with antibiotic resistance genes dissemination.
Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson’s disease, and periodontitis using integrated bioinformatics analyses

Objective To identify the genetic linkage mechanisms underlying Parkinson’s disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases. Methods The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated. Results Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD. Conclusion Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.
Development of speech research software on the MIT lisp machine

Study of the acoustic‐phonetic properties of speech is limited by the considerable overhead involved in the recording, transcription, measurement, and statistical analysis of speech tokens. Conventional computer speech facilities ease this burden by providing storage, signal processing, and display functions. Spire, for Speech and Phonetics Interactive Research Environment, extends this basic functionality by providing a highly human‐engineered user interface. The goal is to dramatically decrease the time required to perform phonetic studies by increasing the ease and flexibility of user and program interaction. Based on the MIT Lisp Machine, the system utilizes a high‐resolution bit‐raster‐scan graphics display (100 pixels per linear inch) and hand‐held “mouse” pointer as its primary means of user interface. Spire's current capabilities include coordinated computation and display of spectrograms, spectral slices, LPC parameters, pitch, formants, transcriptions, and other data. Cursors on the various disp.