Case-report: a case of peritoneal tuberculosis in young women without lung lesion

Scopul lucrării. Prezentarea unui caz dificil de diagnostic diferențiat cu stabilirea afecțiunii tuberculoase extrapulmonare peritoneale. Materiale și metode. Pacientă tânără fără semne de leziuni pulmonare și alte comorbidități, cu leziuni neclare peritoneale. Rezultate. A fost aplicat algoritmul complet de examinare pentru cancer ovarian și anume: analize clinice generale, ecografie transvaginala ale organelor bazinului mic, tomografie computerizată a toracelui, abdomenului si bazinului mic cu contrastare intravenoasă, rezonanță magnetică nucleară a bazinului mic cu contrastare intravenoasă, endoscopie digestivă superioară și inferioară, markerii tumorali CA 125, HE4, indicele ROMA, examenul citologic al lichidului ascitic. Diagnosticul definitivat după biopsie peritoneala în cadrul laparoscopiei diagnostice, examenul patomorfologic și imunohistochimic fiind unul de tuberculoza peritoneală. Concluzii. Procesul de diagnostic și apreciere a tacticii de tratament pacienților cu suspiciune de carcinomatoză peritoneală necesită abordare multidisciplinară și imperative sunt dependente de rezultatele examinărilor patomorfologice și imunohistochimice ale probelor bioptice.Aim of study. To demonstrate a diagnostically hard case of peritoneal tuberculosis without pulmonary manifestations. Materials and methods. We perform diagnostically hard cases of peritoneal tuberculosis in young women without pulmonary lesions or other comorbidities. Results. We performed a full plan of investigations that are typical for ovarian cancer. Clinical signs and investigations results were mostly corresponded to ovarian cancer: routine blood analyses, transvaginal US, CT of thorax, abdomen and pelvis with contrast, MRI of pelvis with contrast, video gastroscopy, video colonoscopy, markers CA 125, HE4, ROMA index, laparocentesis with cytological investigation of peritoneal fluid. The diagnosis was made only after diagnostic laparoscopy, random peritoneal biopsy and subsequent pathology and immunohistochemistry. Conclusions. All diagnosis for peritoneal canceromatosis and choice of treatment must be based on multidisciplinary approach and results of pathology and immunohistochemistry of peritoneal biopsies

Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201

A Tale of Four “Carp”: Invasion Potential and Ecological Niche Modeling

. We assessed the geographic potential of four Eurasian cyprinid fishes (common carp, tench, grass carp, black carp) as invaders in North America via ecological niche modeling (ENM). These “carp” represent four stages of invasion of the continent (a long-established invader with a wide distribution, a long-established invader with a limited distribution, a spreading invader whose distribution is expanding, and a newly introduced potential invader that is not yet established), and as such illustrate the progressive reduction of distributional disequilibrium over the history of species' invasions.We used ENM to estimate the potential distributional area for each species in North America using models based on native range distribution data. Environmental data layers for native and introduced ranges were imported from state, national, and international climate and environmental databases. Models were evaluated using independent validation data on native and invaded areas. We calculated omission error for the independent validation data for each species: all native range tests were highly successful (all omission values <7%); invaded-range predictions were predictive for common and grass carp (omission values 8.8 and 19.8%, respectively). Model omission was high for introduced tench populations (54.7%), but the model correctly identified some areas where the species has been successful; distributional predictions for black carp show that large portions of eastern North America are at risk.ENMs predicted potential ranges of carp species accurately even in regions where the species have not been present until recently. ENM can forecast species' potential geographic ranges with reasonable precision and within the short screening time required by proposed U.S. invasive species legislation

Learning an Executable Neural Semantic Parser

This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general operations defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including a fully supervised training where annotated logical forms are given, weakly-supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of datasets demonstrate the effectiveness of our parser.Comment: In Journal of Computational Linguistic

Analyticalink: An Interactive Learning Environment For Math Word Problem Solving

We present AnalyticalInk, a novel math learning environment prototype that uses a semantic graph as the knowledge representation of algebraic and geometric word problems. The system solves math problems by reasoning upon the semantic graph and automatically generates conceptual and procedural scaffoldings in sequence. We further introduces a step-wise tutoring framework, which can check students\u27 input steps and provide the adaptive scaffolding feedback. Based on the knowledge representation, AnalyticalInk highlights keywords that allow users to further drag them onto the workspace to gather insight into the problem\u27s initial conditions. The system simulates a pen-and-paper environment to let users input both in algebraic and geometric workspaces. We conducted an usability evaluation to measure the effectiveness of AnalyticalInk. We found that keyword highlighting and dragging is useful and effective toward math problem solving. Answer checking in the tutoring component is useful. In general, our prototype shows the promise in helping users to understand geometrical concepts and master algebraic procedures under the problem solving