Miracle-dz/UniFAR-FTUs huggingface dataset:
Adhesive, Transparent Tannic Acid@ Sulfonated Lignin-PAM Ionic Conductive Hydrogel Electrode with Anti-UV, Antibacterial and Mild Antioxidant Function

Inspired by mussel adhesion chemistry and ion electronics, a novel Ca2+-tannic acid@ sulfonated lignin-polyacrylamide (TA@SL-PAM) hydrogel was prepared via Ca2+-TA@SL composites and the PAM system, where a Ca2+-TA@SL composite was fabricated via TA doping with SL and the subsequent adsorption of Ca2+. The properties of the hydrogel were thoroughly investigated and the hydrogel was presented as multifunctional. The introduction of Ca2+-TA@SL composites endowed the hydrogel with excellent conductivity, adhesion and ultraviolet (UV) resistance, and improved antioxidant and antibacterial properties. More importantly, the Ca2+-TA@SL-PAM hydrogel electrode could accurately detect physiological signals of human (e.g., electrocardiogram (ECG), electromyography (EMG).
English-Chinese Cross Language Word Embedding Similarity Calculation

Differences in languages among various countries, regions, and nationalities have created huge obstacles in communication. Cross-language word similarity (CLWS) calculation is the most practical way to solve this problem. Selection of corpus is one of the factors that influence the calculate result. This paper compares the similarity in word embeddings of bilingual parallel and non-parallel corpus on traditional models. Firstly, this paper uses the fastText method to calculate the monolingual word embeddings of Chinese and English, and computes the semantic similarity between the two embeddings. Then it maps the word embeddings into an implicit shared space using Multilingual Unsupervised and Supervised Embedding (MUSE), and compares the effect of unsupervised and supervised machine learning methods in parallel and non-parallel corpus. Finally, the experimental results prove that MUSE model could be better align monolingual word embeddings space, non-parallel corpus have the same effect compares with parallel corpus in calculating the CLWS.
Research on Fine-grained Proposed Region Extraction Method Based on Weighted Channel Network

Proposed region extraction is an important step in target recognition and has important influence on subsequent results. While fine-grained images are more difficult to extract proposed regions due to the intra-class diversity and inter-class similarity. In order to solve the problem of fine-grained target proposed area extraction, This paper proposes a novel coarse-to-fine Weighted channel network(WCN)-based fine-grained image suggestion region extraction method, which firstly initializes parameters on the coarse-grained big data set, then the fine-grained data set is fine-tuned for specific problems to reduce model dependence on large-scale coarse-grained images, and finally the invalid features are suppressed while improving the effective features according to the response graph of the extracted features and the correlation of the feature channels to get the proposed region. The model was validated in the publicly available fine-grained image library CUB200_2011 and Stanford Dog, and achieved an accuracy of 80.1% and 82.8%, respectively, which just proves the validity and accuracy of the model.
Molecular Phylogeography and Ecological Niche Modeling of Sibbaldia procumbens s.l. (Rosaceae)

The phylogeographical analysis and ecological niche modeling (ENM) of the widely distributed Northern Hemisphere Sibbaldia procumbens s.l. can help evaluate how tectonic motion and climate change helped shape the current distribution patterns of this species. Three chloroplast regions (the atpI-atpH and trnL-trnF intergenic spacers and the trnL intron) were obtained from 332 (156 from present study and 176 from the previous study) individuals of S. procumbens s.l. An unrooted haplotype network was constructed using the software NETWORK, while BEAST was used to estimate the divergence times among haplotypes. ENM was performed by MAXENT to explore the historical dynamic distribution of S. procumbens s.l. The haplotype distribution demonstrates significant phylogeographical structure (NST > GST; P < 0.01). The best partitioning of genetic diversity by SAMOVA produced three groups, while the time to the most recent common ancestor of all haplotypes was estimated to originate during the Miocene, with most of the haplotype diversity having occurred during the Quaternary. The MAXENT analysis showed S. procumbens s.l. had a wider distribution range during the last glacial maximum and a narrower distribution range during the last interglacial, with predictions into the future showing the distribution range of S. procumbens s.l. shrinking.
KMT1 family methyltransferases regulate heterochromatin–nuclear periphery tethering via histone and non‐histone protein methylation

Euchromatic histone methyltransferases (EHMTs), members of the KMT1 family, methylate histone and non‐histone proteins. Here, we uncover a novel role for EHMTs in regulating heterochromatin anchorage to the nuclear periphery (NP) via non‐histone methylation. We show that EHMTs methylate and stabilize LaminB1 (LMNB1), which associates with the H3K9me2‐marked peripheral heterochromatin. Loss of LMNB1 methylation or EHMTs abrogates heterochromatin anchorage at the NP. We further demonstrate that the loss of EHMTs induces many hallmarks of aging including global reduction of H3K27methyl marks and altered nuclear morphology. Consistent with this, we observe a gradual depletion of EHMTs, which correlates with loss of methylated LMNB1 and peripheral heterochromatin in aging human fibroblasts. Restoration of EHMT expression reverts peripheral heterochromatin defects in aged cells. Collectively, our work elucidates a new mechanism by which EHMTs regulate heterochromatin domain organization and reveals their impact on fundamental changes associated with the intrinsic aging process.
Leptin Prevents Lipopolysaccharide-Induced Depressive-Like Behaviors in Mice: Involvement of Dopamine Receptors

Depression is a chronic and recurrent disorder, associated with high morbidity and risk of suicide. Leptin was firstly described as an anti-obesity hormone, but several actions of leptin in CNS have been reported. In fact, leptin regulates dopaminergic neurotransmission in mesolimbic areas and has antidepressant-like properties in stress-based models. In the present study, we investigated, for the first time, putative antidepressant-like effects of leptin in an animal model of depressive-like behaviors induced by lipopolysaccharide (LPS), and the potential involvement of dopamine receptors as mediators of those behavioral effects. Mice were injected leptin (1.5 mg/kg, IP) or imipramine prior to LPS administration. To evaluate the involvement of dopamine receptors, different experimental groups were pretreated with either the dopaminergic antagonist SCH23390, for D1 receptors or raclopride, for D2/D3 receptors, prior to leptin injection. Twenty-four hours post-LPS, mice were submitted to the forced swimming and sucrose preference tests. In addition, IL-1β levels were determined in the prefrontal cortex (PFC), hippocampus and striatum. BDNF levels were measured in the hippocampus. Our results showed that leptin, similarly to imipramine, prevented the core behavioral alterations induced by LPS (despair-like behavior and anhedonia), without altering locomotion. In neurochemical analysis, leptin restored LPS-induced changes in IL-1β levels in the PFC and striatum, and increased BDNF levels in the hippocampus. The blockade of dopamine D1 and D2/D3 receptors inhibited leptin's antidepressant-like effects, whilst only the blockade of D1-like receptors blunted leptin-induced increments in prefrontal IL-1β levels. Our results indicate that leptin has antidepressant-like effects in an inflammatory model of depression with the contribution, at least partial, of dopamine receptors.
Cluster-Based Destination Prediction in Bike Sharing System

Destination prediction not only helps to understand users' behavior, but also provides basic information for destination-related customized service. This paper studies the destination prediction in the public bike sharing system, which is now blooming in many cities as an environment friendly short-distance transportation solution. Due to the large number of bike stations (e.g. more than 800 stations of Citi Bike in New York City), the accuracy and effectiveness of destination prediction becomes a problem, where clustering algorithm is often used to reduce the number of destinations. However, grouping bike stations according to their location is not effective enough. The contribution of the paper lies in two aspects: 1) Proposes a Compound Stations Clustering method that considers not only the geographic location but also the usage pattern; 2) Provide a framework that uses feature models and corresponding labels for machine learning algorithms to predict destination for on-going trips. Experiments are conducted on real-world data sets of Citi Bike in New York City through the year of 2017 and results show that our method outperforms baselines in accuracy.
Scalping Anomaly Detection Based on Mobile Internet Traffic Data

With the development of Internet and E-commerce, scalping phenomenon becomes more common and severe. Because of the giant profit, some software means such as scripts and batching-operation are utilized by the profiteers, to scalp tickets or discount coupons from retailers and sell to the buyers. So, an effective way to detect scalping on Internet is important and necessary for online retailers. In this paper, we propose a new integrated method called SADM (Scalping Anomaly Detection Method) to detect the special abnormal behavior, scalping. We collect mobile traffic data from ISPs (Internet Service Providers) and process them to the NFP data (Network Fingerprint data), which records detailed activities on mobile Internet created by users' cell phones. After that, SADM constructs the powerful feature set by the means of feature engineering consisting of feature extraction and feature selection. We combine statistic method and subspace method RPCA-ADMM (Robust PCA-Alternating Direction Method of Multipliers) for a more robust feature set. For anomaly detection models, clustering methods are used to produce the final scalping blacklist. The experiment results show that our SADM method is useful for detecting the special scalping behavior generated by malware.
Deep Feature Fusion over Multi-field Categorical Data for Rating Prediction

Many predictive tasks in recommender systems model from categorical variables. Different from continuous features extracted from images and videos, categorical data is discrete and of multi-field while their dependencies are little known, which brings the problem of heavy computation on a large-scale sparse feature space. Deep learning methods have strong feature extraction capabilities and now have been more and more widely applied to recommender systems, but they do not perform well on discrete data. To tackle these two problems, in this paper we propose Deep Feature Fusion Model(DFFM) over sparse multi-field categorical data. DFFM utilizes categorical features as inputs and applies the Stacked Denoising AutoEncoder to obtain a dense representation. We construct a full feature connection layer and adopt a multi-layer convolution neural network to further extract deeper features and convert rating prediction to a classification problem. The extensive experiments on real world datasets show that our proposed method outperforms other state-of-the-art approaches.
The Intersection of Human Disturbance and Diel Activity, with Potential Consequences on Trophic Interactions

Direct effects of human disturbance on animal populations are well documented across habitats, biomes, and species, but indirect effects of diel have received less attention. An emerging field in applied ecology involves behavioral avoidance of or attraction to humans and their trappings. We posit trophic consequences, in terms of relative risk, for four species of mammals, each of which strongly avoids human activity, in urban reserves of coastal southern California. Two species, one predator and one prey, avoid human activity via a temporal shift to become “more nocturnal”—the species’ activity is centered near dawn on days without human activity but nearer to midnight on days with human activity. Diel shifts have brought the species into greater overlap, respectively, with a key prey and a key predator, overlap that may increase encounter rate and thus increase relative risk of predation, with potential consequences for trophic dynamics and cascades: increased risk of predation may depress prey population, either directly (e.g., mortality) or indirectly (e.g., “landscape of fear”). Human use of reserves, especially in high population density regions, needs to be reconsidered either to reduce access or to restrict access entirely to areas that may provide refuge to both predators and prey.
The power‐integrated discriminant improvement: An accurate measure of the incremental predictive value of additional biomarkers

The predictive performance of biomarkers is a central concern in biomedical research. This is often evaluated by comparing two statistical models: a “new” model incorporating additional biomarkers and an “old” model without them. In 2008, the integrated discrimination improvement (IDI) was proposed for cases when the response variable is binary, and it is now widely applied as a promising alternative to conventional measures, such as the difference of the area under the receiver operating characteristic curve. However, the IDI can erroneously identify a significant improvement in the new model even if no additional information has been provided by new biomarkers. In order to overcome problems with existing measures, in this study, we propose the power‐IDI as a measure of incremental predictive value. Our study explains why the IDI cannot avoid false detection of apparent improvements in a new model and we show that our proposed measure is better able to capture improvements in prediction. Numerical simulations and examples using real empirical data reveal that the power‐IDI is not only more powerful but also incurs fewer false detections of improvement.
Future Trends for In Situ Monitoring of Polycyclic Aromatic Hydrocarbons in Water Sources: The Role of Immunosensing Techniques

Polycyclic aromatic hydrocarbons (PAHs) are hazardous environmental pollutants found in water, soil, and air. Exposure to this family of chemicals presents a danger to human health, and as a result, it is imperative to design methods that are able to detect PAHs in the environment, thus improving the quality of drinking water and agricultural soils. This review presents emerging immunoassay techniques used for in situ detection of PAH in water samples and how they compare to common-place techniques. It will discuss their advantages and disadvantages and why it is required to find new solutions to analyze water samples. These techniques are effective in reducing detection times and complexity of measurements. Immunoassay methods presented here are able to provide in situ analysis of PAH concentrations in a water sample, which can be a great complement to existing laboratory techniques due to their real-time screening and portability for immunoassay techniques. The discussion shows in detail the most relevant state-of-the-art surface functionalization techniques used in the field of immunosensors, with the aim to improve PAH detection capabilities. Specifically, three surface functionalization techniques are key approaches to improve the detection of PAHs, namely, substrate surface reaction, layer-by-layer technique, and redox-active probes. These techniques have shown promising improvements in the detection of PAHs in water samples, since they show a wider linear range and high level of sensitivity compared to traditional PAH detection techniques. This review explores the various methods used in the detection of PAH in water environments. It provides extra knowledge to scientists on the possible solutions that can be used to save time and resources. The combination of the solutions presented here shows great promise in the development of portable solutions that will be able to analyze a sample in a matter of minutes on the field.
Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks

Biological networks catalog the complex web of interactions happening between different molecules, typically proteins, within a cell. These networks are known to be highly modular, with groups of proteins associated with specific biological functions. Human diseases often arise from the dysfunction of one or more such proteins of the biological functional group. The ability, to identify and automatically extract these modules has implications for understanding the etiology of different diseases as well as the functional roles of different protein modules in disease. The recent DREAM challenge posed the problem of identifying disease modules from six heterogeneous networks of proteins/genes. There exist many community detection algorithms, but all of them are not adaptable to the biological context, as these networks are densely connected and the size of biologically relevant modules is quite small. The contribution of this study is 3-fold: first, we present a comprehensive assessment of many classic community detection algorithms for biological networks to identify non-overlapping communities, and propose heuristics to identify small and structurally well-defined communities—core modules. We evaluated our performance over 180 GWAS datasets. In comparison to traditional approaches, with our proposed approach we could identify 50% more number of disease-relevant modules. Thus, we show that it is important to identify more compact modules for better performance. Next, we sought to understand the peculiar characteristics of disease-enriched modules and what causes standard community detection algorithms to detect so few of them. We performed a comprehensive analysis of the interaction patterns of known disease genes to understand the structure of disease modules and show that merely considering the known disease genes set as a module does not give good quality clusters, as measured by typical metrics such as modularity and conductance. We go on to present a methodology leveraging these known disease genes, to also include the neighboring nodes of these genes into a module, to form good quality clusters and subsequently extract a “gold-standard set” of disease modules. Lastly, we demonstrate, with justification, that “overlapping” community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.
Functional organization of vestibulospinal inputs on thoracic motoneurons responsible for trunk postural control in Xenopus

Vestibulospinal reflexes participate in postural control. How this is achieved has not been investigated fully. We combined electrophysiological, neuroanatomical and imaging techniques to decipher the vestibulospinal network controlling the activation of back and limb muscles responsible for postural adjustments. We describe two distinct pathways activating either thoracic postural motoneurons alone or thoracic and lumbar motoneurons together, with the latter co‐ordinating specifically hindlimb extensors and postural back muscles.
Artificial Marker and MEMS IMU-Based Pose Estimation Method to Meet Multirotor UAV Landing Requirements

The fully autonomous operation of multirotor unmanned air vehicles (UAVs) in many applications requires support of precision landing. Onboard camera and fiducial marker have been widely used for this critical phase due to its low cost and high effectiveness. This paper proposes a six-degrees-of-freedom (DoF) pose estimation solution for UAV landing based on an artificial marker and a micro-electromechanical system (MEMS) inertial measurement unit (IMU). The position and orientation of the landing maker are measured in advance. The absolute position and heading of the UAV are estimated by detecting the marker and extracting corner points with the onboard monocular camera. To achieve continuous and reliable positioning when the marker is occasionally shadowed, IMU data is fused by an extended Kalman filter (EKF). The error terms of the IMU sensor are modeled and estimated. Field experiments show that the positioning accuracy of the proposed system is at centimeter level, and the heading error is less than 0.1 degrees. Comparing to the marker-based approach, the roll and pitch angle errors decreased by 33% and 54% on average. Within five seconds of vision outage, the average drifts of the horizontal and vertical position were 0.41 and 0.09 m, respectively.
Infection-induced signals generated at the plasma membrane epigenetically regulate Wnt signaling in vitro and in vivo

Wnt signaling regulates immunomodulatory functions during infection and inflammation. Employing NCCIT and HCT116 cells, having high endogenous Wnt signaling, we observed elevated levels of low-density lipoprotein receptor–related protein 5/6 (LRP5/6) and Frizzled class receptor 10 (FZD10) and increases in β-catenin, doublecortin-like kinase 1 (DCLK1), CD44 molecule (CD44), and aldehyde dehydrogenase 1 family member A1 (ALDH1A1). siRNA-induced knockdown of these receptors antagonized TOPflash reporter activity and spheroid growth in vitro and elevated Wnt-inhibitory factor 1 (WIF1) activity. Elevated mRNA and protein levels of LRP5/6 and FZD10 paralleled expression of WNT2b and WNT4 in colonic crypts at days 6 and 12 post-infection with Citrobacter rodentium (CR) and tended to decline at days 20–34. The CR mutant escV or the tankyrase inhibitor XAV939 attenuated these responses. A three-dimensional organoid assay in colonic crypts isolated from CR-infected mice revealed elevated levels of LRP5/6 and FZD10 and β-catenin co-localization with enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). Co-immunoprecipitation in the membrane fraction revealed that axin associates with LRP5/6 in CR-infected crypts, and this association was correlated with increased β-catenin. Colon tumors from either CR-infected ApcPMin/+ or azoxymethane/dextran sodium sulfate (AOM/DSS)-treated mice had high LRP5/6 or FZD10 levels, and chronic Notch blockade through the γ-secretase inhibitor dibenzazepine down-regulated LRP5/6 and FZD10 expression. In CR-responsive CT-26 cells, siRNA-induced LRP5/6 or FZD10 knockdown antagonized TOPflash reporter activity. Elevated miR-153-3p levels correlated with LRP5/6 and FZD10, and miR-153-3p sequestration via a plasmid-based miR inhibitor system attenuated Wnt signaling. We conclude that infection-induced signals from the plasma membrane epigenetically regulate Wnt signaling.