Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring functional diversity of Nicotiana tabacum Aquaporins

Aquaporins (AQPs) are involved in a variety of vital plant physiological processes, including water relations, development, stress responses and photosynthesis. Nicotiana tabacum (tobacco) has 84 AQP genes due to its recent hybridisation and allotetraploid genome. We functionally characterised a diverse subset of tobacco AQPs spanning the 3 largest AQP subfamilies, selecting nine isoforms from the PIPs, TIPs, and NIPs. Using high-throughput yeast-based functional assays, we determined AQP permeability to water, hydrogen peroxide (H2O2), boric acid (BA) and urea. For each AQP, subcellular localisations in planta were established using GFP translational fusions. From 3D protein homology modelling, we found that the monomeric pore shape/size, selectivity filter region and NPA motifs is insufficient to comprehensively predict their transport capabilities. PIPs had the narrowest pore diameter and were permeable to water, H2O2 and BA. The pore in TIPs was wider and more cylindrical in shape than for the PIPs. TIP1;1s was permeable to all four substrates tested, and is highly expressed in leaves and flowers, suggesting it functions in multiple roles. By contrast, NIP5;1t, with a larger pore size than the NtTIPs, is only expressed in young flowers and enhanced permeability only to BA. Its homolog in Arabidopsis (AtNIP5;1) has the same substrate specificity and functions as a boron channel.Diverse tobacco PIP, TIP and NIP aquaporin isoforms were functionally characterised using high-throughput yeast-based assays, assessing their transport capabilities for key plant solutes: water, H2O2, boric acid and urea.
The formation of the bacterial RNA polymerase–promoter open complex involves a branched pathway

The expression of most bacterial genes commences with the binding of RNA polymerase (RNAP)–σ70 holoenzyme to the promoter DNA. This initial RNAP–promoter closed complex undergoes a series of conformational changes, including the formation of a transcription bubble on the promoter and the loading of template DNA strand into the RNAP active site; these changes lead to the catalytically active open complex (RPO) state. Recent cryo-electron microscopy studies have provided detailed structural insight on the RPO and putative intermediates on its formation pathway. Here, we employ single-molecule fluorescence microscopy to interrogate the conformational dynamics and reaction kinetics during real-time RPO formation. We find that the RPO pathway is branched, generating RPO complexes with different stabilities. The RNAP cleft loops, and especially the β’ rudder, stabilise the transcription bubble. The RNAP interactions with the promoter upstream sequence (beyond −35) stimulate transcription bubble nucleation and tune the reaction path towards stable forms of the RPO. The mechanistic heterogeneity of the RPO pathway may be a prerequisite for its regulation since such heterogeneity allows the amplification of small promoter sequence or transcription-factor-dependent changes in the free energy profile of the RPO pathway to large differences in transcription efficiency.
A Review of Chemometrics Applied to Comprehensive Two-dimensional Separations from 2008–2010

This review article covers developments in multidimensional separations combined with chemometrics that were published in 2008 through 2010, specifically for multidimensional gas chromatography, liquid chromatography, and electrophoresis. Although different instrumentation is used to generate multidimensional separations data, many similar data processing options and chemometrics can be applied in order to objectively distill the data into useful knowledge while reducing manual analysis and preserving data integrity. This review article describes the chemometrics employed in the referenced studies in terms of unsupervised, supervised, preprocessing, resolution, and image analysis algorithms. Other factors that affect converting data into useful knowledge are the structure of the data and the format of the data submitted to the analysis methods, so the studies are also described in terms of data dimensionality and data format (i.e., whether peak tables or raw data points were analyzed).
Draft genome of the Korean smelt Hypomesus nipponensis and its transcriptomic responses to heat stress in the liver and muscle

Pond smelt (Hypomesus nipponensis) is a cold-freshwater fish species as a winter economic resource of aquaculture in South Korea. Due to its high susceptibility to abnormal water temperature from global warming, a large number of smelt die in hot summer. Here, we present the first draft genome of H. nipponensis and transcriptomic changes in molecular mechanisms or intracellular responses under heat stress. We combined Illumina and PacBio sequencing technologies to generate the draft genome of H. nipponensis. Based on the reference genome, we conducted transcriptome analysis of liver and muscle tissues under normal (NT, 5°C) versus warm (HT, 23°C) conditions, to identify heat stress-induced genes and gene categories. We observed a total of 1,987 contigs, with N50 of 0.46 Mbp with a largest contig (3.03 Mbp) in the assembled genome. A total number of 20,644 protein coding genes were predicted, and 19,224 genes were functionally annotated: 15,955 genes for Gene Ontology (GO) terms; and 11,560 genes for KEGG Orthology (KO). We conducted the lost and gained genes analysis compared with three species that human, zebrafish and salmon. In the lost genes analysis, we detected smelt lost 4,461 (22.16%), 2,825 (10.62%), and 1,499 (3.09%) genes compare with above three species, respectively. In the gained genes analysis, we observed smelt gain 1,133 (5.49%), 1,670 (8.09%), and 229 (1.11%) genes compare with above species, respectively. From transcriptome analysis, a total of 297 and 331 differentially expressed genes (DEGs) with False discovery rate (FDR) < 0.05 were identified in the liver and muscle tissues, respectively. Gene enrichment analysis of DEGs indicates that up-regulated genes were significantly enriched for lipid biosynthetic process (GO:0008610, P < 0.001) and regulation of apoptotic process (GO:0042981, P < 0.01), and down-regulated genes by immune responses such as myeloid cell differentiation (GO:0030099, P < 0.001) in the liver under heat stress. In muscle tissue, up-regulated genes were enriched for hypoxia (GO:0001666, P < 0.05), transcription regulator activity (GO:0140110, P < 0.001) and calcium-release channel activity (GO:0015278, P < 0.01), and down-regulated genes for nicotinamide nucleotide biosynthetic process (GO:0019359, P < 0.01). The results of KEGG pathway analysis were similar to that of gene enrichment analysis. The draft genome and transcriptomic of H. nipponensis will be used as a useful genetic resource for functional and evolutionary studies. Our findings will improve understanding of the molecular mechanisms and heat responses and will be useful for predicting survival of the smelt and its closely related species under global warming.
Th17 cell master transcription factor RORC2 regulates HIV-1 gene expression and viral outgrowth

Significance HIV-1 infects CD4 T cells, and, among these, T helper 17 (Th17) cells are known to be particularly permissive for virus replication. The infection of Th17 cells is critical for AIDS pathogenesis and viral persistence. It is, however, not clear why these cells are highly permissive to HIV-1. We found that Th17 cell permissiveness depends on the expression of the hormone receptor RORC2, which is the master transcriptional regulator of Th17 cell differentiation. We identify RORC2 as a cell-specific host-dependency factor that can be targeted by small molecules. Our results suggest that RORC2 may be a cell-specific target to mitigate the loss of Th17 cells as a consequence of their preferential HIV-1 infection. Among CD4+ T cells, T helper 17 (Th17) cells are particularly susceptible to HIV-1 infection and are depleted from mucosal sites, which causes damage to the gut barrier, resulting in a microbial translocation-induced systemic inflammation, a hallmark of disease progression. Furthermore, a proportion of latently infected Th17 cells persist long term in the gastrointestinal lymphatic tract where a low-level HIV-1 transcription is observed. This residual viremia contributes to chronic immune activation. Thus, Th17 cells are key players in HIV pathogenesis and viral persistence. It is, however, unclear why these cells are highly susceptible to HIV-1 infection. Th17 cell differentiation depends on the expression of the master transcriptional regulator RORC2, a retinoic acid-related nuclear hormone receptor that regulates specific transcriptional programs by binding to promoter/enhancer DNA. Here, we report that RORC2 is a key host cofactor for HIV replication in Th17 cells. We found that specific inhibitors that bind to the RORC2 ligand-binding domain reduced HIV replication in CD4+ T cells. The depletion of RORC2 inhibited HIV-1 infection, whereas its overexpression enhanced it. RORC2 was also found to promote HIV-1 gene expression by binding to the nuclear receptor responsive element in the HIV-1 long terminal repeats (LTR). In treated HIV-1 patients, RORC2+ CD4 T cells contained more proviral DNA than RORC2− cells. Pharmacological inhibition of RORC2 potently reduced HIV-1 outgrowth in CD4+ T cells from antiretroviral-treated patients. Altogether, these results provide an explanation as to why Th17 cells are highly susceptible to HIV-1 infection and suggest that RORC2 may be a cell-specific target for HIV-1 therapy.
Informal caregiving in schizophrenia: correlates and predictors of perceived rewards

ABSTRACT Schizophrenia is a debilitating mental illness that has repercussions for the afflicted individual as well as the immediate family. While family caregiving entails enormous burden, it is also acknowledged that the experience may be perceived as being rewarding. This study seeks to understand key aspects of caregiving in terms of perceived rewards, the experience of hope, and the social support available to family caregivers. Standardized instruments to assess these variables were administered to primary caregivers of people with schizophrenia and a comparative group. It was seen that both hope, and perceived social support significantly predicted reward perception in the caregivers.
Frequency and neural correlates related to psychosis in motor neurone disease

Psychosis is a challenging feature of the syndromes motor neurone disease (MND), frontotemporal dementia and their overlap. Clinically evident psychosis affects 5-10% of patients, and more in those with C9orf72 expansions. However, subthreshold psychosis features may be overlooked in the context of overriding concern for physical impairment. This prospective study aimed to establish the prevalence and severity of psychosis features in a population-representative sample of MND, and to identify the neural correlates of psychosis by structural magnetic resonance imaging. A three-tiered system was applied to recruit people with MND, with cognitive and psychosis screening (Tier 1: N=111 with the Edinburgh ALS Cognitive and Behavioural Screen), in-depth neuropsychiatric assessment (Tier 2: N=60) and imaging (Tier 3: N=30). Age, education and sex-matched healthy controls were recruited to Tier 2 (N=30) and Tier 3 (N=20). Overt psychosis was identified in 10% of the Tier 1 cohort, whilst 46% showed milder and diverse neuropsychiatric change. Grey matter correlates of psychosis included atrophy of the cingulate cortex and the hippocampus. White matter correlates included compromised integrity along frontotemporal and temporal-parietal association pathways, especially those connecting the anterior temporal lobe. These grey and white matter changes in MND represent vulnerability to psychosis and are qualitatively similar to volumetric and white matter abnormalities observed in other primary psychotic disorders. Neuropsychiatric features are common, even though overt psychosis is identified in a minority of people with motor neurone disease.
DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.
An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data

Urban vitality is an important indicator of urban development capacity. Streets’ metrics can depict intro-urban fabrics and physiognomy in detail, and thus street vitality affected by street metrics is a concrete manifestation of urban vitality. However, few studies have evaluated dynamic vitality or explored how it is influenced by land use. To bridge this gap, we fully evaluated street dynamic vitality and explored how to enhance the street dynamic vitality by changing the distribution and combination of land use. Specifically, we examined the street dynamic vitality and land use diversity in the main urban zone of Xining city in China using mobile communication and point of interest data, adopted optimized K-means clustering to identify street dynamic vitality types, evaluated the classification result based on vitality intensity and vitality stability, and explored the link between land use and dynamic vitality. Since vitality intensity limitations were found in describing street dynamic vitality, it was necessary to introduce vitality stability. We also found a positive correlation between the vitality intensity and land use density, there were outstanding traffic facilities in high-intensity vitality streets, and improving the abundance and uniformity of land use was beneficial to increase vitality stability. Overall, describing street vitality from a dynamic perspective can improve resource utilization efficiency and rationally plan layouts.
A Perception Model for Optimizing and Evaluating Evacuation Guidance Systems

To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively.
PLD-SLAM: A New RGB-D SLAM Method with Point and Line Features for Indoor Dynamic Scene

RGB-D SLAM (Simultaneous Localization and Mapping) generally performs smoothly in a static environment. However, in dynamic scenes, dynamic features often cause wrong data associations, which degrade accuracy and robustness. To address this problem, in this paper, a new RGB-D dynamic SLAM method, PLD-SLAM, which is based on point and line features for dynamic scenes, is proposed. First, to avoid under-over segmentation caused by deep learning, PLD-SLAM combines deep learning for semantic information segmentation with the K-Means clustering algorithm considering depth information to detect the underlying dynamic features. Next, two consistency check strategies are utilized to check and filter out the dynamic features more reasonably. Then, to obtain a better practical performance, point and line features are utilized to calculate camera pose in the dynamic SLAM, which is also different from most published dynamic SLAM algorithms based merely on point features. The optimization model with point and line features is constructed and utilized to calculate the camera pose with higher accuracy. Third, enough experiments on the public TUM RGB-D dataset and the real-world scenes are conducted to verify the location accuracy and performance of PLD-SLAM. We compare our experimental results with several state-of-the-art dynamic SLAM methods in terms of average localization errors and the visual difference between the estimation trajectories and the ground-truth trajectories. Through the comprehensive comparisons with these dynamic SLAM schemes, it can be fully demonstrated that PLD-SLAM can achieve comparable or better performances in dynamic scenes. Moreover, the feasibility of camera pose estimation based on both point features and line features has been proven by the corresponding experiments from a comparison with our proposed PLD-SLAM only based on point features.
Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories

Lacking indoor navigation graph has become a bottleneck in indoor applications and services. This paper presents a novel automated indoor navigation graph reconstruction approach from large-scale low-frequency indoor trajectories without any other data sources. The proposed approach includes three steps: trajectory simplification, 2D floor plan extraction and 3D navigation graph construction. First, we propose a ST-Join-Clustering algorithm to identify and simplify redundant stay points embedded in the indoor trajectories. Second, an indoor trajectory bitmap construction based on a self-adaptive Gaussian filter is developed, and we then propose a new improved thinning algorithm to extract 2D indoor floor plans. Finally, we present an improved CFSFDP algorithm with time constraints to identify the 3D topological connection points between two different floors. To illustrate the applicability of the proposed approach, we conducted a real-world case study using an indoor trajectory dataset of over 4000 indoor trajectories and 5 million location points. The case study results showed that the proposed approach improves the navigation network accuracy by 1.83% and the topological accuracy by 13.7% compared to the classical kernel density estimation approach.
Consideration of Uncertainty Information in Accessibility Analyses for an Effective Use of Urban Infrastructures

Accessibility analyses are an essential step in the evaluation and planning of urban infrastructures such as transport or pipeline networks. However, these studies generally produce sharply defined lines (called isovarones) or areas (called isovarone areas) that represent the same or similar accessibility. Uncertainties in the input data are usually not taken into account. The aim of this contribution is, therefore, to set up a structured framework that describes the integration of uncertainty information for accessibility analyses. This framework takes uncertainties in the input data, in the processing step, in the target variables, and in the final visualization into account. Particular attention is paid, on the one hand, to the impact of the uncertainties in the target values, as these are key factors for reasoning and decision making. On the other hand, the visualization component is emphasized by applying a dichotomous classification of uncertainty visualization methods. This framework leads to a large set of possible combinations of uncertainty categories. Five selected examples that have been generated with a new software tool and that cover important combinations are presented and discussed.
When big data made the headlines: mining the text of big data coverage in the news media

Big data-driven analytics emerged as one of the most sought-after business strategies of the decade. This paper reviews the news coverage of this phenomenon in the popular press. The study uses natural language processing (NLP) and text mining algorithms to determine the focus and tenor of the news media reporting of big data. A detailed content analysis of a five million-word corpus reveals that most news coverage focused on the newness of big data technologies that showcased usual suspects in big data geographies and industries. The insights gained from the text analysis show that big data news coverage indeed evolved where the initial focus on the promise of big data moderated over time. This study also offers a detailed expose of text mining and NLP algorithms and illustrates their application in news content analysis.
Evaluation of Urban Vibrancy and Its Relationship with the Economic Landscape: A Case Study of Beijing

As one of the essential indicators for the development of a city, urban vibrancy plays an important role in evaluating the quality of urban areas and guiding urban construction. The development of spatial big data makes it possible to obtain information on user trajectories and the built environment, providing support for the evaluation of urban vibrancy. However, previous studies focused on the number of regional activities when evaluating urban vibrancy and ignored diversity, which was produced by diverse economic landscapes. In this paper, using mobile phone trajectory data, we propose a method for evaluating urban vibrancy from two dimensions: busyness and diversity, based on the improved PageRank algorithm and an index of entropy. Furthermore, in order to explore the relationship between urban vibrancy and the economic landscape, we construct an economic landscape index system based on multi-source data, including points of interest (POIs), roads, building footprints, house prices, the gross domestic product (GDP), and population data. Then, multiple linear regression is utilized to model the relationship between urban vibrancy and the urban economic landscape. The results show that combining busyness and diversity can better characterize urban vibrancy than any single indicator, and the adjusted R-squared (R2) of the regression with economic landscape reaches 0.59.