Miracle-dz/UniFAR-FTUs huggingface dataset:
Widespread mortality of trembling aspen (Populus tremuloides) throughout interior Alaskan boreal forests resulting from a novel canker disease

Over the past several decades, growth declines and mortality of trembling aspen throughout western Canada and the United States have been linked to drought, often interacting with outbreaks of insects and fungal pathogens, resulting in a “sudden aspen decline” throughout much of aspen’s range. In 2015, we noticed an aggressive fungal canker causing widespread mortality of aspen throughout interior Alaska and initiated a study to quantify potential drivers for the incidence, virulence, and distribution of the disease. Stand-level infection rates among 88 study sites distributed across 6 Alaska ecoregions ranged from <1 to 69%, with the proportion of trees with canker that were dead averaging 70% across all sites. The disease is most prevalent north of the Alaska Range within the Tanana Kuskokwim ecoregion. Modeling canker probability as a function of ecoregion, stand structure, landscape position, and climate revealed that smaller-diameter trees in older stands with greater aspen basal area have the highest canker incidence and mortality, while younger trees in younger stands appear virtually immune to the disease. Sites with higher summer vapor pressure deficits had significantly higher levels of canker infection and mortality. We believe the combined effects of this novel fungal canker pathogen, drought, and the persistent aspen leaf miner outbreak are triggering feedbacks between carbon starvation and hydraulic failure that are ultimately driving widespread mortality. Warmer early-season temperatures and prolonged late summer drought are leading to larger and more severe wildfires throughout interior Alaska that are favoring a shift from black spruce to forests dominated by Alaska paper birch and aspen. Widespread aspen mortality fostered by this rapidly spreading pathogen has significant implications for successional dynamics, ecosystem function, and feedbacks to disturbance regimes, particularly on sites too dry for Alaska paper birch.
Metabolomics strategy for diagnosing urinary tract infections

Metabolomics has emerged as a mainstream approach for investigating complex metabolic phenotypes. Despite its obvious suitability to diagnostics, it has yet to be integrated into routine clinical laboratory applications. Metabolomics-based diagnosis of infectious diseases is a logical application of this technology since microbial metabolic waste products are concentrated at the site of infection. In the case of urinary tract infections (UTIs), one of the most common bacterial infections, microbial catabolites are trapped in the bladder and thus could enable rapid diagnosis of UTIs. We conducted an untargeted metabolomics screen of 110 clinical specimens and identified two metabolites, agmatine and N6-methyladenine, that are predictive of a wide transect of pathogen species that collectively account for over 90% of UTI infections. We showed that these metabolites were consistently observed in multiple independent cohorts, including a blinded trial of over 587 clinical specimens (95% sensitivity, 86% specificity; PPV, 0.68; NPV, 0.98). These findings demonstrate the potential utility of metabolomics for diagnosing infectious diseases. Moreover, the rapid analysis times enabled through our metabolomics diagnostic approach - six minutes per sample - could curtail the inappropriate UTI clinical prescribing practices that are currently contributing to the selection of antimicrobial resistance.
Effects of COVID-19 pandemic on working lives and psychological health of orthopedic surgeons in Pakistan

ABSTRACT COVID-19 pandemic is a global health crisis. It has affected the working lives and psychological health of orthopedic surgeons in Pakistan. However, there is a lack of research on this topic, therefore the current study has examined the effects of COVID-19 Pandemic on the working lives and psychological health of orthopedic surgeons in Pakistan. Using a cross-sectional survey research design, data were collected from 364 orthopedic surgeons in Pakistan. Missing data analysis were performed with Little’s Test for Missing Data. Descriptive Statistics were used to report results on effects of COVID-19 pandemic. Chi-square test was used to determine association between COVID-19 pandemic and psychological health. Findings revealed that majority of orthopedic surgeons were not directly involved in the care of COVID-19 patients, however, those who were involved had experienced a high level of stress, fear, and anxiety. It was because the orthopedic surgeons had no proper personal protection equipment, and government response toward COVID-19 was not satisfactory. Moreover, work-life balance of orthopedic surgeons was also disturbed. The COVID-19 pandemic is major health crisis. It has severely affected the working lives and psychological health of the orthopedic surgeons in Pakistan. The Orthopedic surgeons can cope with the psychological effects of COVID-19 through developing resilience and self-control. Moreover, they can avail social support from family, and engage in two-way communication and teambuilding activities in hospitals.
Sentiment mapping: point pattern analysis of sentiment classified Twitter data

ABSTRACT Detecting and monitoring collective public opinion via social media platforms can provide real-time information to researchers and policymakers. Human emotions, culture, and opinions can be tracked over time to understand where different sentiments manifest themselves geographically. Expanding on existing methodology, the present study draws from sentiment analysis and point pattern analysis to categorize and analyze sentiment toward natural gas across the United States as a means of applying these techniques together. Three methods of machine learning were used to classify collected tweets into positive and negative categories: Naïve Bayes, Support Vector Machine, and Logistic Regression. Spatial clustering methods and spatial scan statistics were then applied to geocoded tweets to examine the distribution of sentiment about natural gas. In this analysis, the Logistic Regression and Support Vector Machine methods outperformed Naïve Bayes in classifying sentiment. The different methods produced not rather different classification results but also produced varying geographic results. The spatial analyses successfully indicated persistent patterns of negative and positive tweeting about natural gas that correlate with expectations given the physical and cultural environment of various regions. Further, the temporal variation of geographic hotspots of sentiment was readily apparent, suggesting that these approaches can reveal dynamic sentiment landscapes.
Let Your Students “Speak the Speech”: The Academic and Social Benefits of a Performance-Based Approach to Teaching Shakespeare’s Plays to Middle School and High School Students

Abstract Most students meet Shakespeare in middle or high school, on a page in a book in an English classroom. This first encounter is a disservice to the students themselves and to Shakespeare: Shakespeare’s plays are works of both literature and theatre, and should be taught as such. A performance-based teaching approach not only provides high school students, like me, and middle school students with the richest understanding of Shakespeare’s plays but also offers a wide array of transcendent academic and social benefits. From increased vocabulary to deeper emotional connections, these benefits help middle and high schoolers grow as students and as individuals. Guided by the opinions of teaching artists, I relive my own middle and high school experiences with Shakespeare to demonstrate the merits of the performance-based approach, both under normal circumstances and during the COVID-19 pandemic.
Examining the impacts of extended vocabulary instruction in mixed-English-proficiency science classrooms

Abstract Vocabulary is a building block of understanding, especially in science classrooms where language abilities are mixed. To reduce academic inequalities, approaches to vocabulary instruction need to demonstrate benefits for students across learning contexts. We summarize the results from the first unit, “Earth Systems and Catastrophic Events,” in a longitudinal study investigating the effectiveness of the Science Vocabulary Support (SVS) program originally designed for sheltered classrooms. Four grade 7 classrooms, including 106 students (25% native English speakers; 29% former English learners [ELs]; 46% current ELs), participated in this quasi-experimental, matched-group study. Results indicated that students in SVS classrooms outperformed those experiencing regular instruction on science and targeted academic vocabulary, as well as on reading comprehension measures. We detected no significant program impact differences between current EL and proficient English-speaking students across measures. Results suggest that programs such as SVS may help teachers meet the needs of all students in mixed-English-proficiency classrooms.
Psychological and nutritional effects on a COVID-19-quarantined population in Bangladesh

ABSTRACT With the novel coronavirus (COVID-19) outbreak worldwide, many people were instructed to stay at home as a precautionary measure for not catching the disease. The same measure was imposed in Bangladesh, where people were in self and mandatory quarantine for 14 days. The objectives of this study were to assess the psychological and nutritional changes of a quarantined population in Bangladesh during the COVID-19 outbreak. A total of 5792 individuals comprised the population of this study. Subjects were respondents to an online questionnaire that was administered through social media. The questionnaire included questions on personal information, items of the Impact of Event Scale-Revised (IES-R), and the Center for Epidemiologic Studies–Depression (CES-D) scale. Data were collected and analyzed by factor analysis and regression utilizing an SPSS program. Results of the CES-D scale indicated that 24% of respondents (scored ≥16) were depressed and had a significant association with nutritional behavior changes (misconceptions). Data of the IER-S scale showed that 35% of respondents scored ≥20, which indicated that they had a probability of post-traumatic stress disorder (PTSD)—however, there this did not have a significant relationship with nutritional misconceptions. The quick spread of the COVID-19 outbreak caused people to be depressed and made them adopt nutritional attitudes that can be considered as misconceptions. If the government and health care workers become aware of such findings, measures and policies can be taken to alleviate the resultant sufferings.
Out-mobility from Stockholm’s immigrant-dense neighbourhoods – a study of two cohorts

ABSTRACT The central themes of this study are how housing tenure affects moving away from neighbourhoods with differing degrees of immigrant concentration in Stockholm and, comparing two cohorts, how the relationship between housing tenure, ethnic background and residential mobility develops over time. The findings reveal that the effect of housing tenure on moving varies marginally between neighbourhood types, and differences between housing tenures are reduced over time. Residential mobility is increasingly associated with higher income levels and members of the non-western, foreign-born group are less likely to move when the housing market is increasingly owner dominated. These ethnic hierarchies also apply to residential sorting. The sorting of out-movers from neighbourhoods with high concentrations of immigrants is increasingly affected by income. However, the non-western, foreign-born group is also increasingly dependent on housing assets in order to leave immigrant-dense neighbourhoods, which is not the case for other groups. The results show a shift in the necessary resources for residential mobility trajectories for those at the bottom end of the ethnic hierarchy and indicate that the place-stratification framework needs to differentiate between different types of assets and population groups to enable a better understanding of residential sorting in a changing housing market.
Improving the digital skills of older adults in a COVID-19 pandemic environment

ABSTRACT The COVID-19 pandemic has affected the global population, not only in terms of health, but also in the economic, psychosocial, and educational domains. Information and communication technologies (ICT) have been presented as the main currently available solution to reduce the effects of social isolation and for the maintenance of educational activities, as well as basic services like appointments, shopping, etc. However, the older adult population, one of the higher risk groups for COVID-19, is affected by low levels of digital skills, which makes it harder to adapt to the new paradigms imposed by the pandemic. This paper aims to discuss the implications of the COVID-19 pandemic on older adult education, as well as how to develop digital skills in a pandemic scenario.
“Just the Way You Are”: Linking Music Listening on Spotify and Personality

Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences; rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation.
Capability of the variogram to quantify the spatial patterns of surface fluxes and soil moisture simulated by land surface models

Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geographic domains – namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises.
SPF-Net: Semantic Parsed Feature for Pedestrian Attribute Recognition

It is a challenging problem to recognize pedestrian attributes in video analysis scenario, which requires extracting adequate and discriminative information from noisy and deficient samples. In recent years, part-level feature has been generally adopted to provide extra fine-grained local information, which proves highly beneficial. Besides, the correlation between attributes contains potential semantic information to improve the recognition performance of related attributes. We aim to utilize both type of features to enhance pedestrian attribute recognition and, more specifically, we extract local part features and correlation features for each attribute. The main contribution of this paper is summarized as follows: (I) We propose a Semantic Parsed Feature (SPF) computed with two branches, one of which extracts global features of the input images, while the other extracts several part-level features focusing on local patches generated by offline human parsing module. SPF builds an explicit correspondence between attributes and local regions and achieves competitive results with the state-of -the-art methods. (II) An attribute correlation module (ACM), which learns the attribute correlations in a pedestrian image, is introduced in our work. Instead of capturing correlation information in a sequential model, ACM learns jointly all the attribute correlations with an adjacent matrix. Our proposed method has achieved competitive performance on two widely recognized pedestrian attribute benchmarks PETA and RAP
Graph-Based Specification of Admin-CBAC Policies

We present a graph-based language for the specification of administrative access control policies in Admin-CBAC, an administrative model for Category-Based Access Control. More precisely, we propose a multi-level graph representation of policies and a graph-rewriting semantics for administrative actions, from which properties (such as safety, liveness and effectiveness of policies) and constraints (such as separation of duties) can be checked using graph traversal algorithms and rewriting properties. Since Admin-CBAC is a generic model, the techniques are directly applicable to a variety of access control models. In particular, we illustrate our techniques for the RBAC and ABAC instances of Admin-CBAC.
Simulation of In-Memory Database Workload: Markov Chains versus Relative Invocation Frequency and Equal Probability - A Trade-off between Accuracy and Time

In the last years, performance modeling approaches have been proposed to tackle new concepts for modern In-Memory Database Systems (IMDB). While these approaches model specific performance-relevant aspects, workload representation during performance modeling is considered only marginally. Furthermore, the manual integration of workload into modeling approaches comes along with high effort and requires deep domain-specific knowledge. This paper presents our experience in representing workload within performance models for IMDB. In particular, we use a Markov chain-based approach to extract and reflect probabilistic user behavior during performance modeling. An automatic model generation process is integrated to simplify and reduce the effort for transferring workload characteristics from traces to performance models. In an experimental series running analytical and transactional workloads on an IMDB, we compare this approach with two other methods which rely on less granular data to reflect database workload within performance models, namely reproducing the relative invocation frequency of queries and using the same query execution probability. The results reveal a trade-off between accuracy and speed when simulating database workload. Markov chains are the most accurate independent from workload characteristics, but the relative invocation frequency approach is appropriate for scenarios where simulation speed is important.
Image Fusion Method Based on NSCT and Adaptive Sparse Representation

ABSTRACT: Aiming at the imaging characteristics of multispectral and panchromatic image and the problem that the traditional image fusion methods cannot take into account the spectral quality and spatial resolution of the image at the same time, a novel fusion method based on the Non-subsampled Contourlet Transform (NSCT) domain and adaptive sparse representation (ASR) algorithm is presented in this paper. It combines the advantages of principal component substitution and multi-scale analysis, and uses the ASR model to encode the low-frequency components, which improves the shortcomings of the traditional SR-based approach applications. Firstly, NSCT transform is performed on the first principal component of multi-spectral image obtained by PCA transform and panchromatic image, and the low-pass and high-pass coefficients of the source image are obtained. Secondly, the ‘Max-absolute’ rule and the ASR model are used to fuse the separated high-pass and low-pass bands respectively. Finally, we obtained the final fused image by the corresponding inverse transformation reconstruction. The results show that the fusion algorithm proposed in this paper performs well in both spatial resolution and spectral performance; it not only improves the subjective visual effect, but also improves the objective evaluation index.
High-Order Structure Exploration on Massive Graphs

Modeling and exploring high-order connectivity patterns, also called network motifs, are essential for understanding the fundamental structures that control and mediate the behavior of many complex systems. For example, in social networks, triangles have been proven to play the fundamental role in understanding social network communities; in online transaction networks, detecting directed looped transactions helps identify money laundering activities; in personally identifiable information networks, the star-shaped structures may correspond to a set of synthetic identities. Despite the ubiquity of such high-order structures, many existing graph clustering methods are either not designed for the high-order connectivity patterns, or suffer from the prohibitive computational cost when modeling high-order structures in the large-scale networks. This article generalizes the challenges in multiple dimensions. First (Model), we introduce the notion of high-order conductance, and define the high-order diffusion core, which is based on a high-order random walk induced by the user-specified high-order network structure. Second (Algorithm), we propose a novel high-order structure-preserving graph clustering framework named HOSGRAP, which partitions the graph into structure-rich clusters in polylogarithmic time with respect to the number of edges in the graph. Third (Generalization), we generalize our proposed algorithm to solve the real-world problems on various types of graphs, such as signed graphs, bipartite graphs, and multi-partite graphs. Experimental results on both synthetic and real graphs demonstrate the effectiveness and efficiency of the proposed algorithms.