Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of an Autophagy-Immune Related Genes Score Signature and Prognostic Model and its Correlation with Immune Response for Bladder Cancer

Purpose: The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases. Methods: The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model. Results: Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy. Conclusion: We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.
Multivariate pattern analysis of anatomic, physiologic, and metabolic imaging data for improved management of patients with gliomas

The characterization of brain tumors involves the analysis of multiple heterogeneous data sets that include various types of medical images and spectroscopy, clinical and histopathology data, treatment history, and patient outcome. Of particular interest for such analyses is the utilization of recent advances in brain tumor imaging research, which have given rise to novel techniques for exploiting a number of different biological properties of normal and tumor tissue. The systematic analysis of these data could lead to better disease understanding, diagnosis, prognosis, and treatment. The main focus of this thesis was the development of computer-assisted support for glioma understanding, diagnosis, and prognosis in clinical environments. This was achieved by analyzing heterogeneous biomedical data using multivariate pattern recognition methods. The tools developed in this thesis were used to characterize biological changes predictive of malignant transformations and treatment effects in gliomas, and for the early detection of disease progression. They were crucial in finding links between in vivo and ex vivo data that could give insight into the biology of brain cancer and help determine the right course of treatment for individual patients. The methods that are described in this thesis can contribute to clinical practice by improving the selection of biopsy sites and the targeting of treatment. The models that were learned in this thesis produced results with high classification accuracy, interpretability by means of clinical knowledge, and capacity to generalize the performance to new samples. The technical aspects covered in this thesis included the feature selection and modeling of biomedical data, the inference and evaluation of predictive models, and the use of models for clinical applications.
Origin and evolution of the Kolbeinsey Ridge and Iceland Plateau, N‐Atlantic

Variations in crustal structure along the 700 km long KRISE7 refraction/reflection and gravity profile, straddling 66.5°N across the Iceland Shelf, Iceland Plateau and western Norway Basin confirm that extinct spreading centers coexisted with the now extinct Ægir Ridge prior to the initiation of the Kolbeinsey Ridge at 26 Ma. The western 300 km of the profile, across the Iceland shelf, formed by rifting at the Kolbeinsey Ridge, whereas the eastern 400 km, across the Iceland Plateau and the western Norway Basin, formed by earlier rifting, possibly containing slivers of older oceanic or continental crust rifted off the central E‐Greenland margin along with the Jan Mayen Ridge. Crustal thickness increases gradually across the Iceland shelf, from 12 to 13 km near the Kolbeinsey Ridge to 24–28 km near the eastern shelf edge, decreasing abruptly across the shelf edge, to 12–13 km. The Iceland Plateau has crustal thickness ranging from 12 to 15 km decreasing to 5–8 km across the western Norway Basin and 4–5 km at the Ægir Ridge. We suggest that high‐velocity lower crustal domes and corresponding gravity highs across the Iceland plateau mark the location of extinct rift axes that coexisted with the Ægir Ridge. Similar lower crustal domes are associated with the currently active rift segments within Iceland and the Kolbeinsey Ridge.
libRoadRunner: A High Performance SBML Simulation and Analysis Library

MOTIVATION This article presents libRoadRunner, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations. RESULTS libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. Its Python Application Programming Interface (API) is similar to the APIs of MATLAB ( WWWMATHWORKSCOM: ) and SciPy ( HTTP//WWWSCIPYORG/: ), making it fast and easy to learn. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and non-linear algebraic equations) including several SBML extensions (composition and distributions). It offers multiple deterministic and stochastic integrators, as well as tools for steady-state analysis, stability analysis and structural analysis of the stoichiometric matrix. AVAILABILITY AND IMPLEMENTATION libRoadRunner binary distributions are available for Mac OS X, Linux and Windows. The library is licensed under Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi. http://www.libroadrunner. org provides online documentation, full build instructions, binaries and a git source repository. CONTACTS hsauro@u.washington. edu or somogyie@indiana.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Attitudes toward brown bears (Ursus arctos) in Sweden

The Swedish brown bear population has grown from very few animals to at least 2500 individuals in about 80 years time, with a rather constant growth of 1.5 % per year. I wanted to see what the attitudes towards bears are like in Sweden now when the bears expand their habitats closer to urban areas and it is getting more common to encounter bears or tracks from bears in nature. A national mail survey was made in 2004, when no human deaths related to bears had occurred for 102 years, and data was analysed from that survey. I show that most Swedes hold positive attitudes toward bears, despite almost half of the respondents being afraid of encounters with bears. Urbanites are the ones with the most positive attitudes, but also the ones who fear bears the most. When compared to studies from other European countries Swedes are less positive towards bears than people from southern Europe. A large portion of the variation in why people hold their attitudes toward bears remains unexplained, which is most likely due to this survey being designed with previous experiences from wolves in mind and attitudes toward bears are not formed on the same grounds. My conclusion was that future surveys need to focus on examining other aspects to how attitudes toward bears are formed in order to gain a full understanding of this.
Mass balance of gaseous and particulate products analysis from α-pinene/NOx/air in the presence of natural sunlight

The daytime oxidation of α-pinene in the presence of natural sunlight and oxides of nitrogen, NOx, was studied in a large outdoor smog chamber using gas chromatograph mass spectrometry as method of identification and quantification. A Scanning Mobility Particle Sizer system (3936, TSA) and a Condensation Particle Counter (3025A, TSA) were used to study the secondary organic aerosol formation, and a filter pack/denuder sampling system was used for simultaneously collecting gas phase and particle phase products for analysis. A gas chromatograph coupled to an electron impact mass spectrometry (GC-EIMS) method is described for the identification and quantification of gas and aerosol products. The sensitivity of the method was sufficient to produce good quality mass spectra over the range of concentrations used in this study, and compares very well with classical methods based on derivatization and liquid chromatography-ion trap mass spectroscopy methods. Mass balances for gaseous and aerosol reaction products are reported over the course of the reaction. More than 16 products were identified and quantified in this study. On average, measured gas and particle phase products accounted for ∼54% to ∼71% of the carbon reacted α-pinene. Measurements show that 10-hydroxypinonic acid, 10-hydroxypinonaldehyde, 4-oxopinonic acid, and 10-oxopinonic acid are observed in the early stage in the aerosol phase and may play an important role in the early formation of secondary aerosols.
Application of Hyperspectral Techniques to Monitoring and Management of Invasive Plant Species Infestation

Abstract : This report summarizes and integrates the main findings, using several case studies to illustrate conclusions. Case studies include cheatgrass and Russian knapweed at Yakima Training Center, showing that multiple dates that combine different growing seasons improve map accuracy. Also included are maps for kudzu and Johnson lovegrass at Fort Benning, tamarisk at Yuma Proving Ground and phragmites at Aberdeen Proving Ground. Examples were chosen to illustrate a range of problems in correctly mapping invasive plant species under different environmental conditions. A detailed analysis of the CART results for Vandenberg Air Force Base show how it can be used to predict likely locations for spread and the most probable causes for invasion at specific locations. A CART analysis for kudzu at Ft. Benning is included and the same method was applied to tree decline at Ft. Benning. A User's Guide is provided in the appendix as is a hyperspectral training tutorial.
Avaliação do habitat e das assembléias de macroinvertebrados bentônicos de riachos em uma área de proteção ambiental

Headwater streams are relevant suppliers of water, sediment, and biota. They support low autochthonous primary productivity and suffer strong influence from riparian vegetation as in these streams the basic energy for communities maintenance has its origin in the allochthonous particulate organic matter. Hence, deforestation and inadequate soil use negatively affect stream conditions, being among the main causes of environmental degradation in developing nations. A rapid assessment of the physical habitat quality can provide a general idea about stream conditions and detect anthropic changes in a rapid, easy and low cost manner. The combination of this tool with the assessment of other physical, chemical and biological features, especially the benthic macroinvertebrate assemblage composition, is essential to the development of monitoring strategies in those ecosystems. The general aim of this study was to identify the main anthropic changes on streams of Environmental Protection Area Embu Verde and to select those with better habitat conditions for benthic fauna. So it would be possible to know the assemblages composition and its relation with the habitat. First we evaluated 23 sampling sites using a Habitat Assessment Protocol and some relevant variables. Therewith we selected four streams, grounded primarily on the similarity of the surroundings features, little variation on the environmental degradation level and potential of habitats to macroinvertebrates. Then we made an assessment of stream riparian vegetation and substrate types to improve the understanding of the relation between macroinvertebrates and the physical environment. The major anthropic change in the sampling area is the deforestation of the natural riparian cover, and we observed evidences of its negative effects on streams, as bank erosion and fine sediment deposition. Variables that better explained fauna variations are related to the stream size, water kinetic energy and substrate types. Substrate was determinant to the composition of benthic macroinvertebrate assemblages as these ones variate more with the substrates types than with the sampling sites. Chironomidae dominated in all the sampling sites and substrate types. Family diversity and richness increased with the stability of inorganic substrates.
Propagation of a Vertical Hydraulic Fracture

The propagation of hydraulic fractures of limited vertical extent and elliptic cross section is discussed, with the effect of fluid loss included. Numerical and asymptotic approximate solutions in dimensionless form give the fracture length and width at any value of time for any set of physical parameters. The insight provided by the dimensionless results and approximate solutions should be useful in the design of fracture treatments. (14 refs.)
A simple model of saturation excess runoff generation based on geomorphology, steady state soil moisture

A simple conceptual model of saturation excess runoff generation, the Hydrogeomorphic Steady State model (HGSS) is developed using widely observed geomorphic relationships: the saturation excess threshold, the area‐slope relationship, and the cumulative area diagram. HGSS provides theoretical insight into the link between runoff generation by saturation excess and the geomorphology that governs it. Current models for saturation excess runoff generation require detailed analysis of catchment digital elevation maps (DEMs), and their prediction accuracy depends on the DEM grid resolution and the analysis techniques used on the DEM data. HGSS can predict the saturated area at steady state without site‐specific DEM analysis. In its simplest form, HGSS requires only two parameters, each a combination of geomorphology parameters, together with soil and recharge properties. Effectively, HGSS predicts an antecedent recharge‐dependent runoff coefficient. A scaling solution indicates that the percentage of the catchment saturated decreases with the square root of catchment area over a broad range of catchment areas and that the proportion of the catchment saturated has a log‐log linear relationship with recharge. HGSS is tested by comparing its predictions with two DEM rainfall‐runoff models, the steady state TOPMODEL and a kinematic wave saturation excess model DISTFW‐EXT, with HGSS predictions matching for steady state conditions.
Study of Snow Cover Dynamics of Pinder Watershed in Central Himalaya using Remote Sensing and GIS Techniques

This paper is an attempt to examine the dynamics of snow cover using Normalized Difference Snow Index (NDSI) in Pinder Watershed, Central Himalaya, India. Landsat satellite imageries of three different time periods, i.e., Landsat TM of 1990, Landsat TM of 1999 and Landsat TM 2011 were used to quantify snow cover. The geographical distribution of snow cover reveals that in 1990 and 1999 about 9.40% (176.20 km 2 ) and 8.60% (161.08 km 2 ) area of the Pinder Watershed was under snow cover while in 2011 the snow cover was found 7.80% (147.50 km 2 ). These data suggest that due to global warming about 28.7 km 2 snow cover of Pinder Watershed has been converted into non-snow cover area at an average rate of 1.36 km²/year during the last two decades, i.e., in between 1990 to 2011.
The Quarterly Journal of Experimental Psychology the Evolution of a Visual-to-auditory Sensory Substitution Device Using Interactive Genetic Algorithms

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Dynamic compression alters NFjB activation and IjB-a expression in IL-1b-stimulated chondrocyte/agarose constructs

Objective and design Determine the effect of IL-1b and dynamic compression on NFjB activation and IjB-a gene expression in chondrocyte/agarose constructs. Methods Constructs were cultured under free-swelling conditions or subjected to dynamic compression for up to 360 min with IL-1b and/or PDTC (inhibits NFjB activation). Nuclear translocation of NFjB-p65 was analysed by immunofluoresence microscopy. Gene expression of IjB-a, iNOS, IL-1b and IL-4 was assessed by real-time qPCR. Results Nuclear translocation of NFjB-p65 was concomitant with an increase in nuclear fluorescence intensity which reached maximum values at 60 min with IL-1b (p \ 0.001). Dynamic compression or PDTC reduced nuclear fluorescence and NFjB nuclear translocation in cytokine-treated constructs (p \ 0.001 and p \ 0.01 respectively). IL-1b increased IjB-a expression (p \ 0.001) at 60 min and either induced iNOS (p \ 0.001) and IL-1b (p \ 0.01) or inhibited IL-4 (p \ 0.05) expression at 360 min. These time-dependent events were partially reversed by dynamic compression or PDTC (p \ 0.01) with IL-1b. Co-stimulation by dynamic compression and PDTC favoured suppression (IjB-a, iNOS, IL-1b) or induction (IL-4) of gene expression. Conclusions NFjB is one of the key players in the mechanical and inflammatory pathways, and its inhibition by a biophysical/therapeutic approach could be a strategy for attenuating the catabolic response in osteoarthritis.
Individual Variation in Brain Network Topology and Social Cognitive Ability

Background: Social cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits. Objective: Using a data-driven approach, we sought to identify the brain network basis of social cognition using a combination of fMRI (functional magnetic resonance imaging) and a validated measure of social cognition. Methods: Study participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Social cognition was measured using the MATRICS battery of cognitive tests. A data-driven, connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with social cognitive performance using multivariate distance matrix regression (MDMR). Results: We identified a region in the left superior parietal lobule (SPL) where individual network topology predicted social cognitive ability. Specifically, the association of this region with the Default Mode Network predicted higher social cognitive performance and association with the Dorsal Attention Network predicted poorer performance. This correlation was observed in both schizophrenia and healthy comparison participants. Conclusion: Previous studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts complex cognitive ability.
Individual Variation in Brain Network Topology is Linked to Schizophrenia

Background: Resting state fMRI (rsfMRI) has demonstrated that the brain is organized into distributed brain networks and numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of these networks are invariant between individuals. This dogma has recently been overturned by observing that the spatial organization of these brain networks shows significant variation between individuals. We sought to determine if previously observed relationships between schizophrenia ‘negative’ symptom severity and network connectivity are actually due to individual differences in brain network spatial organization. Methods: 44 participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis was used to examine how each participant’s whole brain functional connectivity correlates with ‘negative’ symptom severity at the individual voxel level. Results: Brain connectivity to a region of the right dorso-lateral pre-frontal cortex (r DLPFC) correlates with ‘negative’ symptom severity. This is result is explained by individual differences in the topographic distribution of two brain networks: the default mode network (DMN) and the task positive network (TPN). Both networks demonstrate strong (r~0.49) and significant (p<0.001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is a part of the DMN. In highly symptomatic individuals, this region is a part of the TPN. Conclusion: Previously overlooked individual variation in brain organization is tightly linked to individual variation in schizophrenia symptom severity. The recognition of critical links between network topology and pathological symptomatology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topology likely underlies individual variability in response to clinical interventions that rely on anatomical targeting (e.g. TMS).