Miracle-dz/UniFAR-FTUs huggingface dataset:
What have we learned about CKD-MBD From the EVOLVE and PRIMO trials ?

Purpose of Review—The treatment of CKD-MBD (Chronic Kidney Disease-Mineral Bone Disorder) has traditionally focused on improvement in biochemical parameters of the disease. However, studies evaluating hard clinical end points or surrogate end points are limited. Recent Findings—Two randomized trials have recently been published. In the EVOLVE study (EValuation of Cinacalcet Hydrochloride Therapy to Lower Cardiovascular Events) cinacalcet was compared to placebo in 3883 hemodialysis patients with secondary hyperparathyroidism. The primary end point (death, myocardial infarction, unstable angina, heart failure, or peripheral vascular disease) in an unadjusted intention to treat analysis was not significant (HR 0.93; 95% CI 0.85 – 1.02, p = 0.11). However, the a priori defined secondary end points of an adjusted intention to treat analysis (HR 0.88; 95% CI 0.79–0.97, p = 0.008). In the PRIMO (Paricalcitol Capsule Benefits in Renal Failure Induced Cardiac Morbidity), 227 patients with CKD stage 3–4 and left ventricular hypertrophy by echocardiography were randomized to paricalcitol or placebo. The primary end point of change in left ventricular mass index by MRI after 12 months was not different between the two groups, but the pre-specified end point of cardiovascular related hospitalizations were reduced in the paricalcitol treated group (p=0.04). Summary—The results of these two randomized trials leave clinicians with the difficult choice of therapies for secondary hyperparathyroidism.
Title SPARQL Web-Querying Infrastructure : Ready for Action ?

Hundreds of public SPARQL endpoints have been deployed on the Web, forming a novel decentralised infrastructure for querying billions of structured facts from a variety of sources on a plethora of topics. But is this infrastructure mature enough to support applications? For 427 public SPARQL endpoints registered on the DataHub, we conduct various experiments to test their maturity. Regarding discoverability, we find that only one-third of endpoints make descriptive meta-data available, making it difficult to locate or learn about their content and capabilities. Regarding interoperability, we find patchy support for established SPARQL features like ORDER BY as well as (understandably) for new SPARQL 1.1 features. Regarding efficiency, we show that the performance of endpoints for generic queries can vary by up to 3–4 orders of magnitude. Regarding availability, based on a 27-month long monitoring experiment, we show that only 32.2% of public endpoints can be expected to have (monthly) “two-nines” uptimes of 99–100%.
Reconfiguring Network Slices with Migration Graphs

Reconfiguring network slices involves migrating a set of Virtual Network Functions (VNFs) from their original hosts to the ones declared in the new configuration. Nevertheless, identifying the best way of ordering the VNF migrations is a challenging task that has been largely overlooked. We propose to build a migration graph, which represents all the sequences of VNF migrations that can be used to reconfigure the network slices, and to model the problem as a graph traversal problem. We tackle the complexity of this problem by leveraging the properties of the migration graph to improve the performance of classic graph traversal techniques on the migration graph.
Rapid ReInfection with Soil-Transmitted Helminths after Triple-Dose Albendazole Treatment of School-Aged Children in Yunnan , People ’ s Republic of China

Post-treatment soil-transmitted helminth re-infection patterns were studied as part of a randomized controlled trial among school-aged children from an ethnic minority group in Yunnan province, People’s Republic of China. Children with a soil-transmitted helminth infection (N = 194) were randomly assigned to triple-dose albendazole or placebo and their infection status monitored over a 6-month period using the Kato-Katz and Baermann techniques. Baseline prevalence of Trichuris trichiura, Ascaris lumbricoides, hookworm, and Strongyloides stercoralis were 94.5%, 93.3%, 61.3%, and 3.1%, respectively, with more than half of the participants harboring triple-species infections. For the intervention group (N = 99), the 1-month post-treatment cure rates were 96.7%, 91.5%, and 19.6% for hookworm, A. lumbricoides, and T. trichiura, respectively. Egg reduction rates were above 88% for all three species. Rapid re-infection with A. lumbricoides was observed: the prevalence 4 and 6 months post-treatment was 75.8% and 83.8%, respectively. Re-infection with hookworm and T. trichiura was considerably slower.
Investigation and Application of Improved Text mining based on Bayes Algorithm

Finding features in semi-structured or non-structured document data was not an easily thing in the past. With the development of text mining technique, it has become a widely used analysis tools to find out patterns or rules in hugely text data. The purpose of this study tries to discern some common patterns in the lyrics of music chart singles, which make these singles popular. We provide a walk-through of language identification fundamentals by first introducing the theoretical background, followed by a step-by-step guide on model building, according to the standard practice of data mining. We will put emphasis on understanding the problem, and solving it in an effective and costless manner within the open source environment RapidMiner. We will become familiar with RapidMiner’s “Text Mining Extension”, and learn how to create several simple and fast workflows for language identification. The methods we will use are generic, and applicable to any dataset, with the appropriate pre-processing. In addition, we will learn how to use the implemented models in custom projects, by exporting them as web services using Rapid Analytics, and deploying them in custom applications. We will go through a case study of a web application for language identification of web pages, using RapidMiner’s “Web Mining Extension”. Special emphasis will be given to the optimization of the exported workflow, to enable faster execution, and harmonious integration in other applications. Keywords—Bayes Algorithm, text mining, web mining
Epistemology of Causal Inference in Pharmacology Towards a Framework for the Assessment of Harms

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates invoke the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. In this paper, we develop the pluralistic approach along Hill’s (1965) famous criteria for discerning causal associations by employing Bovens’ and Hartmann’s general Bayes net reconstruction of scientific inference to model the assessment of harms in an evidenceamalgamation framework.
A NEURAL NETWORK BASED MODEL FOR PREDICTING ACCELERATION AND GRADEABILITY PERFORMANCES OF TRANSIT BUSES

Slow moving vehicles can cause traffic congestion and prolonged intersection clearance times. Adequate acceleration and gradeability performances are hence essential to keep vehicular traffic flowing in a smooth manner and to minimize accidents. While acceleration tests are relatively straightforward to perform, gradeability tests have some inherent difficulties associated with them. One such difficulty is the seemingly impossible scenario of having all possible road grades available for testing. Computer simulation of acceleration and gradeability performances hence appears appealing as they are closely related, except for the fact that gradeability is a steady state calculation and therefore the static weight of a vehicle is used rather than the effective weight. In this paper, an Artificial Neural Network (ANN) based method is used to predict gradeability and acceleration performances of transit buses. The ANN method used here has an automatic means for generating empirical formulae consisting of product units and their sums (if desired). This alleviates the most common problem associated with Neural Networks – “Neural Networks are like black boxes and are hard to interpret! ” Often, it is also of interest to know the range of possible vehicle configurations that can provide a desired acceleration or gradeability performance. To this effect, an inversion method based on non-linear programming has also been examined. The result of such an inversion is a set of bus design parameters that can be used to achieve the desired performance. A NEURAL NETWORK BASED MODEL FOR PREDICTING ACCELERATION AND GRADEABILITY PERFORMANCES OF TRANSIT BUSES Saravanan Muthiah and Bohdan T. Kulakowski Pennsylvania Transportation Institute, The Pennsylvania State University University Park, PA 16802, USA
The Epidemiology and Pathophysiology of Caseous Lymphadenitis : A Review

Caseous lymphadenitis is one of the most significant zoonotic diseases caused by C. pseudotuberculosis with enormous economic losses in animal industry worldwide. The global burden of its incidence in animal populations remains at an alarming rate. The impact of the disease is multidimensional in nature and not always well understood, therefore, significantly complicating effective policy response. The pathogenesis is complex and governed by several factors working together in synergistic manner. Information related to the epidemiology and pathophysiology is still scarce in the database and control programmes are rarely implemented. Therapy is based on wide spectrum antibiotics with mysterious outcome as pre-existing vaccines appear not promising. Thus, understanding the biological behaviour of the disease becomes a fundamental issue. In this review, we highlight various key aspects of the disease with special reference to the epidemiology and the pathophysiology of the disease in sheep and goat populations.
Screening för kolorektal cancer

Metod och målgrupp: Varje år dör cirka 2 500 svenskar på grund av cancer i tjocktarmen (kolon) eller ändtarmen (rektum). Ett sätt att minska sjukoch dödligheten är att möjliggöra upptäckt i ett tidigare, mer gynnsamt stadium, då cancern och dess förstadier kan opereras med gott resultat. Allmän screening i befolkningen kan genomföras med hjälp av ett test, Hemoccult, som mäter dolt blod i avföringen. Varje deltagare skickar per post ett avföringsprov till laboratorium för analys. Vid fynd av blod i avföringen görs ytterligare undersökningar, vanligen med koloskopi. Om cancer då påvisas, görs i regel ett operativt ingrepp.
The effect of experimental diabetes on guided bone regeneration

The aim of the present PhD thesis was to investigate the impact of uncontrolled and controlled experimental diabetes on the neo-osteogenesis and bone regeneration potential following Guided Bone Regeneration (GBR). Wistar strain rats (n=128) were allocated in three experimental groups: a) streptozotocin-induced, uncontrolled diabetes; b) systemic insulin controlled diabetes; c) systemic health. The impact of the diabetic status on the neo-osteogenesis and on the bone regeneration potential were evaluated histometrically in GBR treated models of de novo mandibular bone formation and regeneration of critical size calvarial defects respectively. Genome-wide microarray analysis was conducted in tissue samples obtained from GBR treated calvarial defects during the early healing stages. Following application of the GBR therapeutic principle, significant neo-osteogenesis and regeneration of critical size osseous defects were observed histologically and morphometrically, even in the presence of uncontrolled diabetes. Nonetheless, the diabetic status was associated with lower outcome predictability, which was improved via systemic insulin mediated glycaemic control. Uncontrolled diabetes compromised the initial stages of intramembranous bone regeneration following GBR, as evidenced by aberrations in fibrin mesh organisation, inflammatory and mesenchymal cells influx and woven bone formation. In parallel, the uncontrolled diabetic condition featured downregulation of genes encoding chemoattractants and inflammatory mediators during the inflammatory phase of the GBR healing process. Further, pathways related to cell division, energy production and osteogenesis were underexpressed during the proliferative phase, while the NF-kB and Wnt signalling pathways were misregulated. The insulin controlled diabetic state enhanced granulation tissue formation and osteogenesis and upregulated genes encoded growth factors and for extracellular matrix proteins during the early healing phases. It is suggested that, although experimental diabetes may compromise the initial stages of osteogenesis, GBR treatment can provide an environment permissive for significant, even though delayed, bone formation. Insulin mediated glycaemic control may enhance the bone regeneration potential in the diabetic status. Genome-wide expression profiling revealed perturbed pathways in GBR healing depending on the metabolic status, which may be applied in the design of novel therapeutic strategies for the reconstruction of osseous defects in diabetic patients.
Behavior Problems of the Dog and Cat

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Detection and Classi cation of Selected Noise Sources in Long-Term Acoustic Climate Monitoring

Continuous acoustical climate monitoring of the environment raises several problems related to large quantities of the recorded data, which often represents information unrelated to the studied noise source. Manual veri cation of such data is time-consuming and costly. Therefore, developing e ective methods for automatic identi cation of transport noise sources becomes an important task for the proper determination of noise levels. This paper presents a concept of such method of automatic detection and classi cation of the noise sources from the air and railway transportation in the acoustic environmental monitoring.
STUDY OF CORRELATION BETWEEN AVERAGE INTEREST RATE AND NON-PERFORMING LOANS IN THE ROMANIAN BANKING SYSTEM DURING 2006- FEBRUARY 2010

This paper aims to examine the correlation between average interest rate and non-performing loans in the Romanian banking system during 2006-February 2010. We based our approach on the Pearson correlation coefficient and we realized an empirical study, which demonstrates how these relevant banking elements are connected. Also, the result of this research suggests that there are other indirect channels which affect the non-performing loans.
Dynamic Resource Provisioning in Data Centers under Demand Uncertainty

In this paper, we consider a dynamic resource provisioning problem that arises in data centers like Google and AWS who need to plan for resources under uncertain demand requirements. For instance, users requests arrive in an online manner with uncertain requirements of multiple resources including memory, computing power and bandwidth. The goal of the service provider (i.e. Google or AWS) is to satisfy these demand requirements with the smallest possible cost of hardware and power. It is usually significantly more costly to procure resources at short notice. Therefore, data centers aim to provision enough resources before the uncertain demands are revealed sequentially and make recourse decisions to procure additional resources in shortfall scenarios. This is referred to as the dynamic resource provisioning problem and raises many challenging questions.
Title Antineoplastic activity of idazoxan hydrochloride Permalink

Purpose Idazoxan hydrochloride (IDA) is a 241 molecular weight imidazoline and adrenoreceptor ligand. It binds to mitochondrial membranes and promotes apoptosis of pancreatic beta cells. Since IDA has not been tested against tumor cells, the purpose of our study was to determine if IDA has antineoplastic activity. Methods We used the conversion of a soluble tetrazolium salt to an insoluble formazan precipitate and diVerential staining cytotoxicity assays to determine if IDA was cytotoxic to cell lines of murine lung cancer and human prostate cancer, as well as to a variety of fresh human tumor samples. We used Xow cytometry to analyze cell death and calreticulin expression. Results IDA is cytotoxic to both cell lines and against aliquots of specimens of breast, gastric, lung, ovarian and prostate cancers as well as non-Hodgkin’s lymphoma. It produces apoptotic cell death and promotes calreticulin expression, suggesting that IDA might be immunomodulatory in vivo. Conclusion We anticipate that IDA will be clinically useful in cancer treatment.
Knowledge-based genetic algorithm for layer assignment

Layer assignment is an important post-layout optimization technique in very large scale integrated circuit (VLSI) layout automation. It re-assigns wire segments in a routing solution to appropriate layers to achieve certain optimization objectives. The paper focuses on investigating the layer assignment problem with application to via minimization, which is known to be NP-complete. A knowledge based genetic algorithm for the layer assignment problem is proposed, with the aim of utilizing domain specific knowledge to speed up the process of evolution and to improve the quality of solutions. Experimental results show that this knowledge based genetic algorithm can consistently produce the same or better results than a heuristic algorithm and a traditional genetic algorithm.
Smart health Smart driving Smart parking Air quality monitoring Smart surveillance Smart transportation Smart lighting CoAP Local wireless networks Local wired networks Backhaul networks COMMUNICATION FRONT-END Aggregation / Adaptation / preprocessing Application COMMUNICATION BACK-END MQTT DEDICATE

The Internet of Things (IoT) era is evolving into sensor initiated, actuation-driven, and machine intelligence-based decision making platform for smart cities. A smart city system aims at a seamless and secure interconnection of sensors, actuators, and data processing resources to ensure digital, efficient, and reliable services. In this article, we present a brief planar overview of a smart city system architecture by introducing the application, sensing, communication, data, and security/privacy planes. Tailoring existing communication protocols and infrastructures to bridge massively deployed sensors and data processing/storage resources introduces unique communication challenges for smart cities. Furthermore, coexistence, integration, and control of dedicated and non-dedicated sensors is a grand challenge while IoT sensors continuously push sensory data through the communication medium towards data processing and analysis planes. While pervasiveness and ubiquity of smart city services are ensured by the interaction of communication and sensing technologies, their robustness and resilience call for customized security and privacy solutions. With these in mind, we focus on sensing/actuation, communication, and security planes of a smart city system and present a comprehensive survey of the challenges and stateof-the-art solutions in each plane. Furthermore, we provide insights for open issues and opportunities in
Connected Vehicle / Infrastructure UTC

Roadside workers and emergency responders, such as police and emergency medical technicians, are at significant risk of being struck by vehicular traffic while performing their duties. While recent work has examined active and passive systems to reduce pedestrian collisions, current approaches require line of sight using either laser-, infrared-, or vision-based systems. We addressed this problem by developing a Global Positioning System (GPS)-based solution that equips roadside workers and vehicles with GPS units to estimate the trajectory of oncoming traffic, and to estimate whether worker strike is imminent. The results of our study show that our approach is 91% accurate in alerting the worker and vehicle of collisions and near misses. Furthermore, accurate warnings can be provided 5 to 6 seconds before any potential collision, allowing time for mitigating solutions.
Water and soil quality of Roktodoha beel , a floodplain of northwest Bangladesh

An investigation was carried out to assess water and soil quality of Roktodaha beel, a floodplain of northwest Bangladesh over period of a flood cycle. The water quality included physical parameters viz., temperature, depth and transparency and chemical parameters viz. pH, dissolved oxygen, ammonia nitrogen, hardness and alkalinity. Soil quality parameters included pH, organic matter, total nitrogen, available phosphorus, potassium, calcium, magnesium, sulfur and boron. Most of the water and soil qualities were found in suitable ranges that support flora and fauna productivity. Though water pH was almost neutral (6.5-7.8), soil pH was found slightly acidic (5.2-5.7). Higher organic matter (3.1%) was specifically found in sanctuary area soil indicating result of sanctuary materials and fish assemblage. In relation to fisheries production such investigation may act as baseline information of such wetlands of Bangladesh.