Successful treatment of periprosthetic joint infection caused by Granulicatella para-adiacens with prosthesis retention: a case report.

Granulicatella and Abiotrophia spp. are difficult to detect due to their complex nutritional requirements. Infections with these organisms are associated with high treatment failure rates. We report the first implant-associated infection caused by Granulicatella para-adiacens, which was cured with anti-microbial treatment consisting of anti-biofilm-active rifampin and debridement, exchange of mobile parts and retention of the prosthesis. Patient with a history of left hip arthroplasty presented with acute onset of fever, pain and limited range of motion of the left hip. Arthrocentesis of the affected joint yielded purulent fluid and exchange of mobile parts of the prosthesis, but retention of fixed components was performed. Granulicatella para-adiacens grew from preoperative and intraoperative cultures, including sonication fluid of the removed implant. The transesophageal echocardiography showed a vegetation on the mitral valve; the orthopantogram demonstrated a periapical dental abscess. The patient was treated with intravenous penicillin G and gentamicin for 4 weeks, followed by levofloxacin and rifampin for additional 2 months. At discharge and at follow-up 1, 2 and 5 years later, the patient was noted to have a functional, pain-free, and radiologically stable hip prosthesis and the serum C-reactive protein was normal. Although considered a difficult-to-treat organism, we report a successful treatment of the Granulicatella hip prosthesis infection with prosthesis retention and a prolonged antibiofilm therapy including rifampin. The periapical dental abscess is considered the primary focus of hematogenously infected hip prosthesis, underlining the importance treatment of periodontitis prior to arthroplasty and of proper oral hygiene for prevention of hematogenous infection after arthroplasty

If the state decertified gender, what might happen to its meaning and value?

As jurisdictions reform gender identity laws to accommodate transgender and intersex people, this article speculatively explores a more fundamental shift: eliminating state law’s role in determining and assigning gender status altogether. Adopting a feminist perspective, we explore what the meaning and effects of comprehensively reforming legal gender might be in terms of gender’s constitution as a socio-legal property, differentially recognised and protected by diverse but unequal bodies. Our discussion proceeds along two intersecting paths. The first concerns the different classificatory methods which could enable state law, without assigning gender, to continue to regulate gender identity decisions, thereby allowing state law to remain involved in tackling gender discrimination. The second concerns the changing form gender might take in conditions where state law withdraws its allocative function. These paths converge in a final discussion which considers what legal and political effects might follow from gender becoming a property that is individually and collectively cultivated

DogOnt - Ontology Modeling for Intelligent Domotic Environments

Abstract. Home automation has recently gained a new momentum thanks to the ever-increasing commercial availability of domotic components. In this context, researchers are working to provide interoperation mechanisms and to add intelligence on top of them. For supporting intelligent behaviors, house modeling is an essential requirement to understand current and future house states and to possibly drive more complex actions. In this paper we propose a new house modeling ontology designed to fit real world domotic system capabilities and to support interoperation between currently available and future solutions. Taking advantage of technologies developed in the context of the Semantic Web, the DogOnt ontology supports device/network independent description of houses, including both “controllable ” and architectural elements. States and functionalities are automatically associated to the modeled elements through proper inheritance mechanisms and by means of properly defined SWRL auto-completion rules which ease the modeling process, while automatic device recognition is achieved through classification reasoning.

Preliminary Investigation of Possible Biochar Use as Carbon Source in Polyacrylonitrile Electrospun Fiber Production

Electrospinning with consequent thermal treatment consists in a carbon fiber production method that spins a polymer solution to create fibers with diameters around a few hundred nanome-ters. The thermal treatments are used for the cyclization and then carbonization of the material at 1700◦C for one hour. The unique structure of micro-and nano-carbon fibers makes them a promis-ing material for various applications ranging from future battery designs to filtration. This work investigated the possibility of using milled gasification biochar, derived from a 20 kW fixed-bed gasifier fueled with vine pruning pellets, as an addictive in the preparation of electrospinning solu-tions. This study outlined that solvent cleaning and the consequent wet-milling and 32 µm sifting are fundamental passages for biochar preparation. Four different polyacrylonitrile-biochar shares were tested ranging from pure polymer to 50–50% solutions. The resulting fibers were analyzed via scanning electron microscopy, and energy-dispersive X-ray and infrared spectroscopy. Results from the morphological analysis showed that biochar grains dispersed themselves well among the fiber mat in all the proposed shares. All the tested solutions, once carbonized, exceeded 97%wt. of carbon content. At higher carbonization temperatures, the inorganic compounds naturally showing in biochar such as potassium and calcium disappeared, resulting in an almost carbon-pure fiber matrix with biochar grains in between

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT--enabling it to generate Science Birds levels with high stability and character-like qualities--fully using their creativity as well as prompt engineering skills. ChatGPT is a conversational agent developed by OpenAI. Science Birds is selected as the competition platform because designing an Angry Birds-like level is not a trivial task due to the in-game gravity; the playability of the levels is determined by their stability. To lower the entry barrier to the competition, we limit the task to the generation of capitalized English alphabetical characters. Here, the quality of the generated levels is determined by their stability and similarity to the given characters. A sample prompt is provided to participants for their reference. An experiment is conducted to determine the effectiveness of its modified versions on level stability and similarity by testing them on several characters. To the best of our knowledge, we believe that ChatGPT4PCG is the first competition of its kind and hope to inspire enthusiasm for prompt engineering in procedural content generation.Comment: This paper under review is made available for participants of ChatGPT4PCG Competition (https://chatgpt4pcg.github.io/) and readers interested in relevant area

Iron(III) Complexes on a Dendrimeric Basis and Various Amine Core Investigated by Mössbauer Spectroscopy

Dendrimers of various generations were synthesized by the divergent method. Starting from various amine cores (G(0a), G(0b), G(0c)) the generations were built by reaction of the amine with acrylnitrile followed by hydrogenation with DIBAL-H. Treatment with salicylaldehyde creates a fivefold coordination sphere for iron in the molecular periphery. The resulting multinuclear coordination compounds are investigated by Mossbauer spectroscopy

Model-based probabilistic frequent itemset mining

Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain database induces an exponential number of possible worlds. To tackle this problem, we propose a novel methods to capture the itemset mining process as a probability distribution function taking two models into account: the Poisson distribution and the normal distribution. These model-based approaches extract frequent itemsets with a high degree of accuracy and support large databases. We apply our techniques to improve the performance of the algorithms for (1) finding itemsets whose frequentness probabilities are larger than some threshold and (2) mining itemsets with the {Mathematical expression} highest frequentness probabilities. Our approaches support both tuple and attribute uncertainty models, which are commonly used to represent uncertain databases. Extensive evaluation on real and synthetic datasets shows that our methods are highly accurate and four orders of magnitudes faster than previous approaches. In further theoretical and experimental studies, we give an intuition which model-based approach fits best to different types of data sets. © 2012 The Author(s).published_or_final_versio