Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate Modelling Shows Increased Risk to Eucalyptus sideroxylon on the Eastern Coast of Australia Compared to Eucalyptus albens

Aim: To identify the extent and direction of range shift of Eucalyptus sideroxylon and E. albens in Australia by 2050 through an ensemble forecast of four species distribution models (SDMs). Each was generated using four global climate models (GCMs), under two representative concentration pathways (RCPs). Location: Australia. Methods: We used four SDMs of (i) generalized linear model, (ii) MaxEnt, (iii) random forest, and (iv) boosted regression tree to construct SDMs for species E. sideroxylon and E. albens under four GCMs including (a) MRI-CGCM3, (b) MIROC5, (c) HadGEM2-AO and (d) CCSM4, under two RCPs of 4.5 and 6.0. Here, the true skill statistic (TSS) index was used to assess the accuracy of each SDM. Results: Results showed that E. albens and E. sideroxylon will lose large areas of their current suitable range by 2050 and E. sideroxylon is projected to gain in eastern and southeastern Australia. Some areas were also projected to remain suitable for each species between now and 2050. Our modelling showed that E. sideroxylon will lose suitable habitat on the western side and will not gain any on the eastern side because this region is one the most heavily populated areas in the country, and the populated areas are moving westward. The predicted decrease in E. sideroxylon’s distribution suggests that land managers should monitor its population closely, and evaluate whether it meets criteria for a protected legal status. Main conclusions: Both Eucalyptus sideroxylon and E. albens will be negatively affected by climate change and it is projected that E. sideroxylon will be at greater risk of losing habitat than E. albens.
Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study

The popularity, great influence and huge importance made wireless indoor localization has a unique touch, as well its wide successful on positioning and tracking systems for both human and assists also contributing to take the lead from outdoor systems in the scope of the recent research works. In this work, we will attempt to provide a survey of the existing indoor positioning solutions and attempt to classify different its techniques and systems. Five typical location predication approaches (triangulation, fingerprinting, proximity, vision analysis and trilateration) are considered here in order to analysis and provide the reader a review of the recent advances in wireless indoor localization techniques and systems to have a good understanding of state of the art technologies and motivate new research efforts in this promising direction. For these reasons, existing wireless localization position systems and location estimation schemes are reviewed. We also made a comparison among the related techniques and systems along with conclusions and future trends to identify some possible areas of enhancements.
AppDetox: helping users with mobile app addiction

With the increasing adoption of smartphones also a problematic phenomena become apparent: People are changing their habits and become addicted to different services that these devices provide. In this paper we present AppDetox: an app that allows users to purposely create rules that keep them from using certain apps. We describe our deployment of the app on a mobile application store, and present initial findings gained through observation of about 11,700 users of the application. We find that people are rather rigorous when restricting their app use, and that mostly they suppress use of social networking and messaging apps.
Querying XML Data

XML threatens to expand beyond its document markup origins to become the basis for data interchange on the Internet. One highly anticipated application of XML is the interchange of electronic data (EDI). Unlike existing Web documents, electronic data is primarily intended for computer, not human, consumption. For example, businesses could publish data about their products and services, and potential customers could compare and process this information automatically; business partners could exchange internal operational data between their information systems on secure channels; search robots could integrate automatically information from related sources that publish their data in XML format, like stock quotes from financial sites, sports scores from news sites. New opportunities will arise for third parties to add value by integrating, transforming, cleaning, and aggregating XML data. Once it becomes pervasive, it’s not hard to imagine that many information sources will structure their external view as a repository of XML data, no matter what their internal storage mechanisms. Data exchange between applications will then be in XML format. What is then the role of a query language in this world? One could see it as a local adjunct to a browsing capability, providing a more expressive “find” command over one or more retrieved documents. Or it might serve as a souped-up version of XPointer, allowing richer forms of logical reference to portions of documents. Neither of these modes of use is very “databasey”. From the database viewpoint, the enticing role of an XML query language is as a tool for structural and content-based query that allows an application to extract precisely the information it needs from one or several XML data sources. One salient question is why not adapt SQL or OQL to query XML. The answer is that XML data is fundamentally different from relational and object-oriented data, and therefore, neither SQL nor OQL is appropriate for XML. The key distinction between data in XML and data in traditional models is that XML is not rigidly structured. In the relational and object-oriented models, every data instance has a schema, which is separate from and independent of the data. In XML, the schema exists with the data. Thus, XML data is self-describing and can naturally model irregularities that cannot be modeled by relational or object-oriented data. For example, data items may have missing elements or multiple occurrences of the same element; elements may have atomic values in some data items and structured values in others; and collections of elements can have heterogeneous structure. Even XML data that has an associated DTD is self-describing (the data can still be parsed, even if
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.
Experimental and numerical analysis of composite beams strengthened by CFRP laminates in hogging moment region

An experimental and a non linear finite element investigation on the behavior of steel-concrete composite beams stiffened in hogging moment region with Carbon Fiber Reinforced Plastics (CFRP) sheets is presented in this paper. A total of five specimens were tested under two-point loads. Three of the composite beams included concrete slab while the other two beams had composite slabs. The stiffening was achieved by attaching CFRP sheets to the concrete surface at the position of negative bending moment. The suggested CFRP sheets arrangement enhanced the overall beam behavior and increased the composite beam capacity. Valuable parametric study was conducted using a three dimensional finite element model using ANSYS program. Both geometrical and material nonlinearity were included. The studied parameters included CFRP sheet arrangement, concrete strength and degree of shear connection.
Pharmacotherapy of cerebral ischemia

Background: Ischemic stroke remains one of the leading causes of death and disability in the developed world. Despite many promising preclinical results, the only pharmacologic treatments proven effective in improving clinical outcome following ischemic stroke until now are administration of aspirin and acute thrombolysis using tissue-plasminogen activator. Objective: To review currently approved pharmacologic therapies as well as promising future treatment strategies for acute ischemic stroke. Methods: We performed an exhaustive PubMed search for articles published from 1950 through 2009 describing pharmacotherapy of acute ischemic stroke, focusing on agents that are currently in Phase III trials or approved for clinical use. Following this review, we present our interpretation of the existing literature and our vision of the future of pharmacotherapy for ischemic stroke. Results/conclusions: Since the clinical success of the first intravenous thrombolytic, many studies have sought to further characterize the ideal patient population and to extend the therapeutic time window for pharma-cologic thrombolysis. Additionally, despite the many failures of neuroprotective trials, several promising agents are currently undergoing Phase III investigation. With the advent of interventional techniques enabling local delivery of therapeutics into the region of the thrombus, the future of pharmacotherapy for ischemic stroke will probably include a combination of interventional techniques and pharmacotherapy.
EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as human-facing use cases. Existing supervised systems for keyphrase extraction require large amounts of labeled training data and generalize very poorly outside the domain of the training data. At the same time, unsupervised systems found in the literature have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Furthermore, both supervised and unsupervised methods are often too slow for real-time scenarios and suffer from over-generation. Addressing these drawbacks, in this paper, we introduce an unsupervised method for keyphrase extraction from single documents that leverages sentence embeddings. By selecting phrases whose semantic embeddings are close to the embeddings of the whole document, we are able to separate the best candidate phrases from the rest. We show that our embedding-based method is not only simpler, but also more effective than graph-based state of the art systems, achieving higher F-scores on standard datasets. Simplicity is a significant advantage, especially when processing large amounts of documents from the Web, resulting in considerable speed gains. Moreover, we describe how to increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrase semantic overlap leads to no gains in terms of F-score, our diversity enriched selection is preferred by humans.
Multiplex epitope detection: A new method overcomes limitations of antibody arrays

Antibody arrays have been used as an effective method for simultaneously detecting multiple proteins such as cytokines. However, their use in quantifying a large number of cellular proteins has the following limitations: (i) unsuitable for simultaneously detecting proteins that may form complexes with each other; (ii) incapable of quantitatively detecting more than one epitope of each protein such as phospho‐ and nonphospho‐epitopes; and (iii) incapable of simultaneously detecting multiple biomarkers on solid surfaces such as formalin‐fixed tissue sections. Using a novel multiple epitope detection (MED) technique, we have overcome these limitations and have improved upon currently available antibody‐based protein detection technologies. The MED technique employs primary antibody detection of epitopes within fixed cells or tissue, followed by elution of bound antibodies, and subsequent quantification of the eluted antibodies by an epitope array. Using the MED method, we demonstrate accurate detection of individual proteins even in complex with each other, simultaneous quantitative detection of phospho‐ and nonphospho‐epitopes on a protein, and sensitive detection of multiple biomarkers on formalin‐fixed tissue sections.
Therapeutic drug repositioning using personalized proteomics of liquid biopsies.

BACKGROUND In patients with limited response to conventional therapeutics, repositioning of already approved drugs can bring new, more effective options. Current drug repositioning methods, however, frequently rely on retrospective computational analyses and genetic testing - time consuming methods that delay application of repositioned drugs. Here, we show how proteomic analysis of liquid biopsies successfully guided treatment of neovascular inflammatory vitreoretinopathy (NIV), an inherited autoinflammatory disease with otherwise poor clinical outcomes. METHODS Vitreous biopsies from NIV patients were profiled by an antibody array for expression of 200 cytokine-signaling proteins. Non-NIV controls were compared with NIV samples from various stages of disease progression. Patterns were identified by 1-way ANOVA, hierarchical clustering, and pathway analysis. Subjects treated with repositioned therapies were followed longitudinally. RESULTS Proteomic profiles revealed molecular pathways in NIV pathologies and implicated superior and inferior targets for therapy. Anti-VEGF injections resolved vitreous hemorrhages without the need for vitrectomy surgery. Methotrexate injections reversed inflammatory cell reactions without the side effects of corticosteroids. Anti-IL-6 therapy prevented recurrent fibrosis and retinal detachment where all prior antiinflammatory interventions had failed. The cytokine array also showed that TNF-α levels were normal and that corticosteroid-sensitive pathways were absent in fibrotic NIV, helping explain prior failure of these conventional therapeutic approaches. CONCLUSIONS Personalized proteomics can uncover highly personalized therapies for autoinflammatory disease that can be timed with specific pathologic activities. This precision medicine strategy can also help prevent delivery of ineffective drugs. Importantly, proteomic profiling of liquid biopsies offers an endpoint analysis that can directly guide treatment using available drugs.
Defining CT Perfusion Thresholds for Infarction in the Golden Hour and With Ultra-Early Reperfusion

Abstract In this brief report, computed tomography perfusion (CTP) thresholds predicting follow-up infarction in patients presenting <3 hours from stroke onset and achieving ultra-early reperfusion (<45 minutes from CTP) are reported. CTP thresholds that predict follow-up infarction vary based on time to reperfusion: Tmax >20 to 23 seconds and cerebral blood flow <5 to 7 ml/min−1/(100 g)−1 or relative cerebral blood flow <0.14 to 0.20 optimally predicted the final infarct. These thresholds are stricter than published thresholds. RÉSUMÉ Seuils limites d’un examen de tomodensitométrie couplé à une perfusion administré à des patients victimes d’un accident ischémique et aux prises avec un processus de reperfusion très précoce se présentant en temps opportun. Dans ce bref compte-rendu, il est question des seuils limites obtenus à la suite d’un examen de tomodensitométrie couplé à une perfusion. Ces seuils limites peuvent prédire le suivi de patients ayant subi un accident ischémique. Les patients à l’étude devaient s’être présentés moins de 3 heures après l’apparition de l’accident ischémique et avoir montré très tôt des signes de reperfusion (moins de 45 minutes à la suite d’un examen de tomodensitométrie). Les seuils limites pouvant prédire l’évolution de patients à la suite d’un accident ischémique vont varier selon le temps de reperfusion. Cela dit, une durée dépassant les 20 à 23 secondes et un débit sanguin cérébral inférieur à 5-7 ml/min−1/(100 g)−1 ou bien un débit sanguin cérébral relatif inférieur à 0,14-0,20 ont permis de prédire de façon optimale un AVC. Précisons que ces seuils limites sont plus sévères que ceux ayant été publiés antérieurement.
"RePlay Health": An Experiential Role-Playing Sport for Modeling Healthcare Decisions, Policies, and Outcomes.

OBJECTIVE This article presents the design and empirical investigation of the "RePlay Health" game ( www.replayhealth.com/ ), a novel "role-playing sport" derived from a complex, data-driven, computational simulation of healthcare dynamics. By immersing players in a fictional world in which they take on the role of characters facing specific behavioral and environmental risk factors, the "RePlay Health" game models the impact of health and healthcare policy on individual-level livelihood and community-level productivity. SUBJECTS AND METHODS A randomized experiment tested the efficacy of the game for inspiring shifts in thinking about public health and healthcare policy. This study compared the impact of actively playing the game versus passively spectating: 31 young adults were assigned to one of these two roles. Participants completed pretest and posttest measures of their subjective ranking of healthcare policies and attributions for health outcomes. RESULTS Active players (compared with spectators) reported significantly higher prioritizations (from pretest to posttest) in their subjective ranking of several health policies modeled by the game, such as "improving postdischarge care" and "increasing access to healthy foods. " Furthermore, players, but not spectators, were significantly more likely following gameplay to identify environmental or systemic factors as potential causes of health problems. CONCLUSIONS The fact that significant results emerged with a 1-week gap between gameplay and measurement demonstrates that the game could exert a lasting impact on attitudes and perceptions. More broadly, this work illustrates the value of incorporating experiential components, such as narrative, embodiment, and role-playing, in designing efficacious games for health.
Entity Attribute Value Style Modeling Approach for Archetype Based Data

Entity Attribute Value (EAV) storage model is extensively used to manage healthcare data in existing systems, however it lacks search efficiency. This study examines an entity attribute value style modeling approach for standardized Electronic Health Records (EHRs) database. It sustains qualities of EAV (i.e., handling sparseness and frequent schema evolution) and provides better performance for queries in comparison to EAV. It is termed as the Two Dimensional Entity Attribute Value (2D EAV) model. Support for ad-hoc queries is provided through a user interface for better user-interaction. 2D EAV focuses on how to handle template-centric queries as well as other health query scenarios. 2D EAV is analyzed (in terms of minimum non-null density) to make a judgment about the adoption of 2D EAV over n-ary storage model of RDBMS. The primary aim of current research is to handle sparseness, frequent schema evolution, and efficient query support altogether for standardized EHRs. 2D EAV will benefit data administrators to handle standardized heterogeneous data that demands high search efficiency. It will also benefit both skilled and semi-skilled database users (such as, doctors, nurses, and patients) by providing a global semantic interoperable mechanism of data retrieval.
Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.