Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Characteristics and Source of Dechloranes in Soil and Lichen of the Fildes Peninsula (Antarctica)

Dechloranes (Decs) have been widely found in the environment, even in the Tibetan Plateau and remote polar regions. However, the understanding of their regional distribution characteristics in polar regions is limited. To study the long-range atmospheric transport and fates of these emerging contaminants, Decs were analyzed in soil and lichen from the Fildes Peninsula in Antarctica. The concentrations of five Decs in soil and lichen ranged from 141.46 to 838.47 pg/g dw and 237.04 to 3599.18 pg/g dw, respectively. The mean fractions of anti-Dechlorane Plus (DP) (fanti) values estimated in the current soils (0.37) and lichen (0.24) were lower than those of commercial products (fanti = 0.64–0.80), which confirms that long-range atmospheric transport is a main source of DP, and the DP burdens could be driven by the accumulation of syn-DP. The average ΣDP concentration in soil in the coastal area was higher than that in the inland area and Ardley Island, while in lichen, the average DP concentration at the Ardley Island site was approximately three-fold higher than that in the coastal area and inland areas. This indicates that the distribution of DP was influenced by anthropogenic interference and animal activities in the Fildes Peninsula. The spatial variation of fanti of the three regions was clearer in soil than that in lichen. The fanti values were negatively correlated with DP concentrations in soil, suggesting that DP concentration levels play an important role in determining the isomeric composition of DP in the soil.
The use of demonstratives and personal pronouns in fragile X syndrome and autism spectrum disorder

ABSTRACT Demonstratives (e.g. here, that, these) and personal pronouns are early developing components of language, which are often impaired in young children with autism spectrum disorder (ASD). Additionally, demonstrative and personal pronoun use are linked to joint attention and language ability early in life for individuals with ASD. Fragile X syndrome (FXS) is a genetic disorder with a significant amount of overlap in its behavioural phenotype with ASD. The present study examined demonstrative and personal pronoun production during a conversation sample in adolescent boys with ASD and adolescent boys with FXS with a co-diagnosis of ASD (FXS+ASD). Findings indicated that grammatical complexity was related to both qualitative and quantitative aspects of demonstrative and personal pronoun production in boys with ASD, while grammatical complexity was related to the total number of demonstratives and personal pronouns produced in the boys with FXS+ASD. ASD severity was not related to demonstrative or personal pronoun production in ASD, although it was negatively correlated with the total number of personal pronouns produced by the boys with FXS. Additionally, groups did not differ significantly in production of personal pronouns, but they did differ significantly in multiple aspects of demonstrative use. Findings suggest that these groups produce similar rates of personal pronouns in the school-age years, while production of demonstratives differentiates these groups. This study contributes to the knowledge of the language phenotypes of idiopathic ASD and FXS+ASD, and provides implications for intervention targets for school-age children with these disorders.
Default Mode Network Complexity and Cognitive Decline in Mild Alzheimer’s Disease

The human resting-state is characterized by spatially coherent brain activity at a low temporal frequency. The default mode network (DMN), one of so-called resting-state networks, has been associated with cognitive processes that are directed toward the self, such as introspection and autobiographic memory. The DMN’s integrity appears to be crucial for mental health. For example, patients with Alzheimer’s disease or other psychiatric conditions show disruptions of functional connectivity within the brain regions of the DMN. However, in prodromal or early stages of Alzheimer’s disease, physiological alterations are sometimes elusive, despite manifested cognitive impairment. While functional connectivity assesses the signal correlation between brain areas, multi-scale entropy (MSE) measures the complexity of the blood-oxygen level dependent signal within an area and thus might show local changes before connectivity is affected. Hence, we investigated alterations of functional connectivity and MSE within the DMN in fifteen mild Alzheimer’s disease patients as compared to fourteen controls. Potential associations of MSE with functional connectivity and cognitive abilities [i.e., mini-mental state examination (MMSE)] were assessed. A moderate decrease of DMN functional connectivity between posterior cingulate cortex and right hippocampus in Alzheimer’s disease was found, whereas no differences were evident for whole-network functional connectivity. In contrast, the Alzheimer’s disease group yielded lower global DMN-MSE than the control group. The most pronounced regional effects were localized in left and right hippocampi, and this was true for most scales. Moreover, MSE significantly correlated with functional connectivity, and DMN-MSE correlated positively with the MMSE in Alzheimer’s disease. Most interestingly, the right hippocampal MSE was positively associated with semantic memory performance. Thus, our results suggested that cognitive decline in Alzheimer’s disease is reflected by decreased signal complexity in DMN nodes, which might further lead to disrupted DMN functional connectivity. Additionally, altered entropy in Alzheimer’s disease found in the majority of the scales indicated a disturbance of both local information processing and information transfer between distal areas. Conclusively, a loss of nodal signal complexity potentially impairs synchronization across nodes and thus preempts functional connectivity changes. MSE presents a putative functional marker for cognitive decline that might be more sensitive than functional connectivity alone.
Physicochemical and nutritional properties of extrudates from food grade distiller's dried grains, garbanzo flour, and corn grits

Abstract Distiller's dried grains and garbanzo flour were blended with corn grits for the development of extruded snacks using a single screw extruder. Distiller's dried grains were processed for food application and termed as food grade distiller's dried grains or FDDG. Effects of different level of FDDG addition (0%–20%) and extrusion process parameters such as barrel and die temperature (100–140°C), screw speed (100–200 rpm), and feed moisture content (14%–20% wet basis) on the physical properties (expansion ratio, bulk density, color parameters), functional properties (water absorption and solubility indices), and nutritional properties (total dietary fiber, soluble and insoluble dietary fiber and protein content) of the extrudates were investigated and optimized using response surface methodology. FDDG incorporation had a significant effect (p < 0.05) on the total dietary fiber, color parameters, water solubility, and water absorption indices of the extruded snacks. Desirable expanded extrudates with a high level of total dietary fiber and protein were obtained with blends containing 20% FDDG extruded at 140°C extrusion temperature, 167 rpm screw speed, and 19% feed moisture content. Results indicate garbanzo flour, and FDDG can be successfully blended with corn grits to produce nutritious gluten‐free extruded snacks which are high in protein and dietary fiber.
Impact of Domain and User's Learning Phase on Task and Session Identification in Smart Speaker Intelligent Assistants

Task and session identification is a key element of system evaluation and user behavior modeling in Intelligent Assistant (IA) systems. However, identifying task and sessions for IAs is challenging due to the multi-task nature of IAs and the differences in the ways they are used on different platforms, such as smart-phones, cars, and smart speakers. Furthermore, usage behavior may differ among users depending on their expertise with the system and the tasks they are interested in performing. In this study, we investigate how to identify tasks and sessions in IAs given these differences. To do this, we analyze data based on the interaction logs of two IAs integrated with smart-speakers. We fit Gaussian Mixture Models to estimate task and session boundaries and show how a model with 3 components models user interactivity time better than a model with 2 components. We then show how session boundaries differ for users depending on whether they are in a learning-phase or not. Finally, we study how user inter-activity times differs depending on the task that the user is trying to perform. Our findings show that there is no single task or session boundary that can be used for IA evaluation. Instead, these boundaries are influenced by the experience of the user and the task they are trying to perform. Our findings have implications for the study and evaluation of Intelligent Agent Systems.
Facial Expression Recognition in the Wild: A Cycle-Consistent Adversarial Attention Transfer Approach

Facial expression recognition (FER) is a very challenging problem due to different expressions under arbitrary poses. Most conventional approaches mainly perform FER under laboratory controlled environment. Different from existing methods, in this paper, we formulate the FER in the wild as a domain adaptation problem, and propose a novel auxiliary domain guided Cycle-consistent adversarial Attention Transfer model (CycleAT) for simultaneous facial image synthesis and facial expression recognition in the wild. The proposed model utilizes large-scale unlabeled web facial images as an auxiliary domain to reduce the gap between source domain and target domain based on generative adversarial networks (GAN) embedded with an effective attention transfer module, which enjoys several merits. First, the GAN-based method can automatically generate labeled facial images in the wild through harnessing information from labeled facial images in source domain and unlabeled web facial images in auxiliary domain. Second, the class-discriminative spatial attention maps from the classifier in source domain are leveraged to boost the performance of the classifier in target domain. Third, it can effectively preserve the structural consistency of local pixels and global attributes in the synthesized facial images through pixel cycle-consistency and discriminative loss. Quantitative and qualitative evaluations on two challenging in-the-wild datasets demonstrate that the proposed model performs favorably against state-of-the-art methods.
Person Re-identification with Hierarchical Deep Learning Feature and efficient XQDA Metric

Feature learning and metric learning are two important components in person re-identification (re-id). In this paper, we utilize both aspects to refresh the current State-Of-The-Arts (SOTA). Our solution is based on a classification network with label smoothing regularization (LSR) and multi-branch tree structure. The insight is that some middle network layers are found surprisingly better than the last layers on the re-id task. A Hierarchical Deep Learning Feature (HDLF) is thus proposed by combining such useful middle layers. To learn the best metric for the high-dimensional HDLF, an efficient eXQDA metric is proposed to deal with the large-scale big-data scenarios. The proposed HDLF and eXQDA are evaluated with current SOTA methods on five benchmark datasets. Our methods achieve very high re-id results, which are far beyond state-of-the-art solutions. For example, our approach reaches 81.6%, 96.1% and 95.6% Rank-1 accuracies on the ILIDS-VID, PRID2011 and Market-1501 datasets. Besides, the code and related materials (lists of over 1800 re-id papers and 170 top conference re-id papers) are released for research purposes.
Improving Stock Movement Prediction with Adversarial Training

This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a recurrent neural network model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with stationary price-based features (e.g. the closing price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of continuous price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11% relative improvements on average w.r. t. accuracy, verifying the usefulness of adversarial training for stock prediction task. Codes will be made available upon acceptance.
Anakinra for the treatment of adult-onset Still’s disease

ABSTRACT Introduction: Adult onset Still’s disease (AOSD) is an uncommon systemic inflammatory disease on the clinical spectrum of autoinflammatory disorders. Its presentation and clinical course may result in several well-differentiated phenotypes: from a systemic and highly symptomatic pattern to a chronic articular pattern. Overproduction of numerous pro-inflammatory cytokines is observed in AOSD. Anakinra (ANK), a human interleukin (IL)-1R antagonist, has recently been approved in the EU for the treatment of AOSD. Areas covered: In this review, we discuss the main studies on the efficacy and safety on ANK for the treatment of AOSD. The vast majority of them are retrospective studies and case series. Expert commentary: Overall, ANK is an effective biologic agent for the treatment of AOSD, especially for the systemic pattern and also for those patients who have life-threatening complications, which frequently occur over the course of the disease. The initial dose usually indicated of ANK in adults is 100 mg/day subcutaneously, although dose reduction can be performed in some cases once the disease is under control. The safety profile of ANK is favorable and similar to that described in other rheumatic diseases. In conclusion, ANK is an effective and safe agent for the treatment of AOSD.
The Finnish You: an Interactive Storytelling Application for an Airport Environment

Traveling should be full of excitement and new experiences. However, a chaotic airport environment and constant waiting often halt these pleasurable feelings. Although passengers can spend their time shopping, they are unlikely to connect personally to the products. Furthermore, airport services seldom highlight the local culture that passengers miss by being confined to the airport. To address these shortcomings, we present a mobile web-application, called "The Finnish You". Utilizing the elements of interactive storytelling and gamification, the application guides users through shops and brands in the airport while teaching about the local culture in a personalized way. The application was tested in a user study with nine participants in a controlled office environment and was seen as a satisfactory way to spend time waiting in the airport. Our findings show how a personalized storytelling approach may convert ordinary shopping activity into a culture-learning adventure. We further suggest implications for the design of storytelling applications regarding the airport context of use.
Users’ Intrinsic Goals Linked to Alcohol Dependence Risk Level and Engagement With a Health Promotion Website (Hello Sunday Morning): Observational Study

Background Hello Sunday Morning (HSM) is a self-guided health promotion website with the aim to improve drinking culture. Members are encouraged to sign up for a 3-month period of alcohol abstention and record and track their progress and goals. Objective This study used self-determination theory (SDT) to examine the nature of goals subscribed by HSM users to test the extent to which intrinsic goal pursuit was linked to lower alcohol dependency risk and higher engagement with the HSM website. Methods HSM users (N=2216; 59.75%, 1324/2216, females; aged 18-79 years) completed the World Health Organization’s Alcohol Use Disorders Identification Test (WHO-AUDIT, which measures alcohol dependence risk level) at sign-up and at 4 and 6 months after sign-up. In addition, the website had a goals-subscription feature that allowed participants to share their goals. Two independent raters classified the goals according to a coding system we devised based on SDT, which proposes that intrinsic goals (eg, growth, relationships, community, and health) better promote positive outcomes than extrinsic goals (eg, wealth, fame, and image). Results Although there was substantial (1016/2216, 45.84%) attrition of HSM users from sign-up to 6 months, the attrition rate could not be attributed to alcohol dependency risk because people in different WHO-AUDIT risk zones were equally likely to be missing at 4 and 6 months after sign-up. The SDT-driven coding of goals yielded the following categories: wealth and image (extrinsic goals); relationships, personal growth, community engagement, and physical health (intrinsic goals); and alcohol use-related goals (which were hard to classify as either extrinsic or intrinsic). Alcohol dependence risk level correlated positively with goals related to money (r=.16), personal growth (r=.17), relationships (r=.10), and alcohol use (r=.25). Website engagement correlated negatively with alcohol dependence risk level (r=.10) and positively with relationship (r=.10) and community goals (r=.12). Conclusions HSM users with higher alcohol dependence risk tended to engage with the website less, but to the extent that they did, they tended to subscribe to goals related to alcohol use and improving their personal growth, relationships, and finances. In line with SDT, engagement with goals—particularly the intrinsic goals of connecting with close-others and the broader community—related to increased website engagement. Web-based tools intended to promote healthy behaviors in users may be effective in engaging their users if the users’ experience on the website supports the pursuit of intrinsic goals.
On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach

Destination prediction is known as an important problem for many location based services (LBSs). Existing solutions generally apply probabilistic models to predict destinations over a sub-trajectory, but their accuracies in fine-granularity prediction are always not satisfactory due to the data sparsity problem. This paper presents a carefully designed deep learning model called TALL model for destination prediction. It not only takes advantage of the bidirectional Long Short-Term Memory (LSTM) network for sequence modeling, but also gives more attention to meaningful locations that have strong correlations w.r. t. destination by adopting attention mechanism. Furthermore, a hierarchical model that explores the fusion of multi-granularity learning capability is further proposed to improve the accuracy of prediction. Extensive experiments on Beijing and Chengdu real datasets finally demonstrate that our proposed models outperform existing methods without considering external features.
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)

Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling. However, learning the parameters from observed data is generally very difficult because their likelihood function is typically intractable. Likelihood-free Bayesian inference methods have been proposed which include the frameworks of approximate Bayesian computation (ABC), synthetic likelihood, and its recent generalization that performs likelihood-free inference by ratio estimation (LFIRE). A major difficulty in all these methods is choosing summary statistics that reduce the dimensionality of the data to facilitate inference. While several methods for choosing summary statistics have been proposed for ABC, the literature for synthetic likelihood and LFIRE is very thin to date. We here address this gap in the literature, focusing on the important special case of time-series models. We show that convolutional neural networks trained to predict the input parameters from the data provide suitable summary statistics for LFIRE. On a wide range of time-series models, a single neural network architecture produced equally or more accurate posteriors than alternative methods.
MTBP promotes migration and invasion by regulation of ZEB2-mediated epithelial–mesenchymal transition in lung cancer cells

Background It is clearly necessary to discover prognostic biomarkers to identify stage I patients at risk of recurrence and give them timely postoperative treatment. Materials and methods Data of stage I lung adenocarcinoma were retrieved from four gene series in Gene Expression Omnibus (GEO) database (GSE50081, GSE30219, GSE37745, and GSE13213). Partek Genomics Suite software was used to identify survival-related genes for finding candidate indicators for early-stage patients at risk of recurrence. Differential expression of MTBP (MDM2 binding protein) in early-stage lung adenocarcinoma tissues was determined by immunohistochemical staining. The effects of MTBP interference expression and overexpression on viability, migration, and invasion capacity of lung cells were evaluated using Cell Counting Kit-8, wound healing, and Transwell assays. The tumor growth and lung metastasis in vivo were observed in chick embryo chorioallantoic membrane model. Human Exon 2.0 ST Array was used to analyze downstream regulation genes of MTBP in lung cancer cells. Involvement of ZEB2 and epithelial–mesenchymal transition (EMT) markers was investigated by Western blot. Results By mining GEO database, we identified MTBP as a poor prognostic indicator of stage I lung adenocarcinomas. In addition, increased expression of MTBP was also associated with poor survival in our early-stage lung adenocarcinoma cohort. Further experiment suggested that knockdown of MTBP suppressed the migration and invasion of A549 and H1975 cells in vitro and in vivo, whereas overexpression of MTBP in HCC827 and PC9 cells promoted the migration and invasion in vitro and in vivo. Furthermore, ZEB2 upregulation directly activated EMT to mediate the downstream effects of MTBP involved in lung cancer cells metastasis. Conclusion MTBP is an independent indicator for poor prognosis in stage I lung adenocarcinomas and might promote the aggressive phenotype of non-small-cell lung cancer by inducing the EMT process through upregulating ZEB2 expression.