Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential neuronal reprogramming induced by NeuroD1 from astrocytes in grey matter versus white matter

A new technology called in vivo glia-to-neuron conversion has emerged in recent years as a promising next generation therapy for neural regeneration and repair. This is achieved through reprogramming endogenous glial cells into neurons in the central nervous system through ectopically expressing neural transcriptional factors in glial cells. Previous studies have been focusing on glial cells in the grey matter such as the cortex and striatum, but whether glial cells in the white matter can be reprogrammed or not is unknown. To address this fundamental question, we express NeuroD1 in the astrocytes of both grey matter (cortex and striatum) and white matter (corpus callosum) to investigate the conversion efficiency, neuronal subtypes, and electrophysiological features of the converted neurons. We discover that NeuroD1 can efficiently reprogram the astrocytes in the grey matter into functional neurons, but the astrocytes in the white matter are much resistant to neuronal reprogramming. The converted neurons from cortical and striatal astrocytes are composed of both glutamatergic and GABAergic neurons, capable of firing action potentials and having spontaneous synaptic activities. In contrast, the few astrocyte-converted neurons in the white matter are rather immature with rare synaptic events. These results provide novel insights into the differential reprogramming capability between the astrocytes in the grey matter versus the white matter, and highlight the impact of regional astrocytes as well as microenvironment on the outcome of glia-to-neuron conversion. Since human brain has large volume of white matter, this study will provide important guidance for future development of in vivo glia-to-neuron conversion technology into potential clinical therapies. Experimental protocols in this study were approved by the Laboratory Animal Ethics Committee of Jinan University (approval No. IACUC-20180321-03) on March 21, 2018.
Audiology for the school speech-language clinician

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Effect of nicotine on biofilm formation of Streptococcus mutans isolates from smoking and non-smoking subjects

ABSTRACT Objectives: To investigate effects of nicotine on biofilm formation of Streptococcus mutans isolates from oral washes of smoker and non-smoker human subjects. Materials and methods: This study was conducted using 60 S. mutans isolates with three S. mutans isolates collected from oral washes of ten smoking subjects and ten from non-smoking subjects. Biofilm was formed by culturing each S. mutans strain (10 μl) in 190 μl of TSB supplemented with 1% sucrose (TSBS) containing 0, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, and 32.0 mg/ml of nicotine for 24 h in 5% CO2 at 37°C in 96 well microtiter plates. The absorbance values of biofilm were measured at 490 nm in a microplate spectrophotometer. Results: There was a significant effect (p-value < 0.05) of nicotine concentrations and smoking on the growth of biofilm, planktonic cells, and total absorbance, for all strains of S. mutans. Isolates from smokers had significantly more biofilm at 0–16 mg/ml of nicotine compared to those from non-smokers (p-value < 0.0001). Conclusion: S. mutans smoker isolates are more affected by high nicotine concentrations than non-smoker isolates. Clinical Relevance: The use of nicotine products increases the growth of S. mutans and may place tobacco users at risk for dental decay.
Emotional Stress During Pregnancy – Associations With Maternal Anxiety Disorders, Infant Cortisol Reactivity, and Mother–Child Interaction at Pre-school Age

There is growing evidence that even milder forms of maternal stress or anxiety during pregnancy affect the fetus causing possible long-term consequences for infant and child development. The mechanisms through which prenatal maternal stress may affect the unborn are not yet entirely clarified. Due to limited self-regulatory skills after birth, infants depend on sensitive behavior of their parents to regulate affective states and physiological arousal. Dyadic affect regulation has been linked to various developmental patterns up to adolescence and thereby represents a key element of early social relationships. Aim of the study was to evaluate possible long-term consequences of emotional stress during pregnancy and postpartum anxiety disorders, as well as infant postpartum cortisol reactivity on mother–child-interaction at pre-school age. The sample comprised of N = 63 mother–infant dyads at study entry, n = 28 diagnosed with postpartum anxiety disorders according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV), n = 35 were healthy controls. Mothers were interviewed with the Structured Clinical Interview for DSM-IV Disorders at an average infant age of M = 4.1 months and filled out a questionnaire regarding emotional stress during pregnancy. Further, they were videotaped during the Face-to-Face-Still-Face paradigm (FFSF), a widely used mild socio-emotional stressor for infants. To determine infant stress-reactivity, infant salivary cortisol was collected before, immediately after and 20 min after the FFSF. Missing values were estimated by multiple imputations. At the age of M = 5.3 years, mother-child-interaction was re-assessed in a follow-up sample of n = 30 dyads via a free-play situation. Moreover, dimensional measures for anxiety were assessed. Mothers in the clinical group reported significantly higher stress scores than the control group. Infant stress reactivity in the early postpartum period and maternal anxiety symptoms at the 5-year follow-up assessment were significantly associated with dyadic interaction quality at pre-school age. Even though maternal stress during pregnancy did not directly predict mother–child interaction quality at pre-school age, it was significantly correlated with infant cortisol reactivity during postpartum period. Nevertheless, caution should be taken when interpreting the results considering the small sample size.
A Tracking-by-Detection System for Pedestrian Tracking Using Deep Learning Technique and Color Information

Pedestrian tracking is a particular object tracking problem and an important component in various vision-based applications, such as autonomous cars and surveillance systems. Following several years of development, pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding tracklets based on similarities between color distributions. The experimental results show that our system is able to overcome various difficulties to produce highly accurate tracking results.
Chronological Analysis of Source Code Reuse Impact on Android Application Security

Application developers consider open discussion forum on software development such as question and answer (Q&A) forums to be very important. There are cases where snippets which are partial source code on such forums contains vulnerabilities, and application developers divert snippets without knowing it. Previous works were focused on security-related codes such as a TLS connection, and not on actual vulnerable codes that are used widely. Thus, a time series investigation on the spread of such codes has not been conducted. In this paper, a method that enables the chronological analysis of source code reuse is proposed. By determining source code reuse in applications, we can investigate the context using time information such as the respective publication dates and time, and clarify how many cases are not source code reuse. An evaluation of the proposed method is achieved using large-scale data which includes 527,249 snippets of source code and 249,987 applications. The result shows that the appearance rate of applications having the same code as the snippet has increased after the release of the snippet. Furthermore, experiments on extracting vulnerable snippets from all snippets show that vulnerable snippets often have a greater impact than the overall snippet trend.
Kernel Fisher Discriminant Analysis for Natural Gait Cycle Based Gait Recognition

This paper studies a novel approach to natural gait cycles based gait recognition via kernel Fisher discriminant analysis (KFDA), which can effectively calculate the features from gait sequences and accelerate the recognition process. The proposed approach firstly extracts the gait silhouettes through moving object detection and segmentation from each gait videos. Secondly, gait energy images (GEIs) are calculated for each gait videos, and used as gait features. Thirdly, KFDA method is used to refine the extracted gait features, and low-dimensional feature vectors for each gait videos can be got. The last is the nearest neighbor classifier is applied to classify. The proposed method is evaluated on the CASIA and USF gait databases, and the results show that our proposed algorithm can get better recognition effect than other existing algorithms.
Information Dissemination Model of Microblogging with Internet Marketers

Microblogging services (such as Twitter) are the representative information communication networks during the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for information dissemination in online social networks due to its large number of users. In this study, a microblog information dissemination model is presented. Related concepts are introduced and analyzed based on the dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptible-infective-removal (SIR) information dissemination model. Correlation analysis is conducted on the existing information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method in reducing the rumor dissemination of microblogs.
Systematic Review of Bug Report Processing Techniques to Improve Software Management Performance

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.
A Multistakeholder Recommender Systems Algorithm for Allocating Sponsored Recommendations

Retailing and social media platforms recommend two types of items to their users: sponsored items that generate ad revenue and non-sponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through some form of programmatic auction, and non-sponsored items to maximize user utility with a recommender system (RS). We develop a multiobjective binary in-teger programming model to allocate sponsored recommendations considering a dual objective of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method can be applied as a form of post processing to an existing RS, making it widely applicable. We apply the model to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach, which unifies programmatic advertising and RS, opens a new frontier for advertising and RS research and we therefore provide an extended discussion of future research topics.
Survey of Temporal Information Extraction

Documents contain information that can be used for various applications, such as question answering (QA) system, information retrieval (IR) system, and recommendation system. To use the information, it is necessary to develop a method of extracting such information from the documents written in a form of natural language. There are several kinds of the information (e.g., temporal information, spatial information, semantic role information), where different kinds of information will be extracted with different methods. In this paper, the existing studies about the methods of extracting the temporal information are reported and several related issues are discussed. The issues are about the task boundary of the temporal information extraction, the history of the annotation languages and shared tasks, the research issues, the applications using the temporal information, and evaluation metrics. Although the history of the tasks of temporal information extraction is not long, there have been many studies that tried various methods. This paper gives which approach is known to be the better way of extracting a particular part of the temporal information, and also provides a future research direction.
Combining serological and contact data to derive target immunity levels for achieving and maintaining measles elimination

BackgroundVaccination has reduced the global incidence of measles to the lowest rates in history. However, local interruption of measles virus transmission requires sustained high levels of population immunity that can be challenging to achieve and maintain. The herd immunity threshold for measles is typically stipulated at 90–95%. This figure does not easily translate into age-specific immunity levels required to interrupt transmission. Previous estimates of such levels were based on speculative contact patterns based on historical data from high-income countries. The aim of this study was to determine age-specific immunity levels that would ensure elimination of measles when taking into account empirically observed contact patterns.MethodsWe combined estimated immunity levels from serological data in 17 countries with studies of age-specific mixing patterns to derive contact-adjusted immunity levels. We then compared these to case data from the 10 years following the seroprevalence studies to establish a contact-adjusted immunity threshold for elimination. We lastly combined a range of hypothetical immunity profiles with contact data from a wide range of socioeconomic and demographic settings to determine whether they would be sufficient for elimination.ResultsWe found that contact-adjusted immunity levels were able to predict whether countries would experience outbreaks in the decade following the serological studies in about 70% of countries. The corresponding threshold level of contact-adjusted immunity was found to be 93%, corresponding to an average basic reproduction number of approximately 14. Testing different scenarios of immunity with this threshold level using contact studies from around the world, we found that 95% immunity would have to be achieved by the age of five and maintained across older age groups to guarantee elimination. This reflects a greater level of immunity required in 5–9-year-olds than established previously.ConclusionsThe immunity levels we found necessary for measles elimination are higher than previous guidance. The importance of achieving high immunity levels in 5–9-year-olds presents both a challenge and an opportunity. While such high levels can be difficult to achieve, school entry provides an opportunity to ensure sufficient vaccination coverage. Combined with observations of contact patterns, further national and sub-national serological studies could serve to highlight key gaps in immunity that need to be filled in order to achieve national and regional measles elimination.
Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients’ DTI data – Theory, simulations and example cases

Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.
Meta-Analysis of Human and Mouse Biliary Epithelial Cell Gene Profiles

Background: Chronic liver diseases are frequently accompanied with activation of biliary epithelial cells (BECs) that can differentiate into hepatocytes and cholangiocytes, providing an endogenous back-up system. Functional studies on BECs often rely on isolations of an BEC cell population from healthy and/or injured livers. However, a consensus on the characterization of these cells has not yet been reached. The aim of this study was to compare the publicly available transcriptome profiles of human and mouse BECs and to establish gene signatures that can identify quiescent and activated human and mouse BECs. Methods: We used publicly available transcriptome data sets of human and mouse BECs, compared their profiles and analyzed co-expressed genes and pathways. By merging both human and mouse BEC-enriched genes, we obtained a quiescent and activation gene signature and tested them on BEC-like cells and different liver diseases using gene set enrichment analysis. In addition, we identified several genes from both gene signatures to identify BECs in a scRNA sequencing data set. Results: Comparison of mouse BEC transcriptome data sets showed that the isolation method and array platform strongly influences their general profile, still most populations are highly enriched in most genes currently associated with BECs. Pathway analysis on human and mouse BECs revealed the KRAS signaling as a new potential pathway in BEC activation. We established a quiescent and activated BEC gene signature that can be used to identify BEC-like cells and detect BEC enrichment in alcoholic hepatitis, non-alcoholic steatohepatitis (NASH) and peribiliary sclerotic livers. Finally, we identified a gene set that can distinguish BECs from other liver cells in mouse and human scRNAseq data. Conclusions: Through a meta-analysis of human and mouse BEC gene profiles we identified new potential pathways in BEC activation and created unique gene signatures for quiescent and activated BECs. These signatures and pathways will help in the further characterization of this progenitor cell type in mouse and human liver development and disease.
Relationships of Trait Anxiety and Loss of Control Eating with Serum Leptin Concentrations among Youth

Loss of control (LOC) eating in youth is associated with elevated fasting serum leptin, even after accounting for adiposity. Anxiety is closely linked to, and may exacerbate, LOC eating. Yet, it remains unclear how anxiety relates to leptin, or if the relationship is moderated by the presence of LOC eating. We examined whether self-reported trait anxiety interacted with LOC eating in relation to leptin in a convenience sample of youths (n = 592; 13.1 ± 2.7 years; body mass index z-score (BMIz) = 0.9 ± 1.1; 61.8% girls; 53.5% non-Hispanic White; 36.6% with LOC eating). LOC eating was assessed by interview. Leptin was measured after an overnight fast. Exploratory analyses were conducted to examine anxiety and LOC eating in relation to laboratory intake patterns in three sub-samples. In a generalized linear model adjusting for relevant covariates, anxiety significantly interacted with LOC eating in relation to leptin (p = 0.02), such that greater trait anxiety related to higher concentrations of leptin only among youth with LOC eating. Trait anxiety was not significantly related to fasting serum leptin independently in a generalized linear model adjusting for age, race, height, sex, study type, and fat mass (kg). Exploratory mechanistic analyses of food intake patterns did not identify consistent results for participants with both anxiety and LOC eating. Among youth with LOC eating, anxiety may be associated with higher serum leptin. Prospective data are required to elucidate the directionality and mechanisms of these relationships.