Selected design features of numerically controlled milling machines and machining centers

W pracy przedstawiono wybrane rozwiązania konstrukcyjne współczesnych obrabiarek sterowanych numerycznie CNC. Pracę osadzono w nurcie trendów rozwojowych w zakresie innowacyjności budowy obrabiarki, jej funkcjonalności i bogatych możliwości kinematycznych. Praca bazuje na prezentowanych podczas targów i spotkań studyjnych w firmachrozwiązaniach konstrukcyjnych. Zawiera syntetyczne omówienie wybranych zespołów funkcjonalnych, ich głównych cech charakterystycznych, zarówno wad jak i zalet. Powstała z myślą o młodych adeptach nauki i przybliża co prawda podstawowe ale niezwykle istotne - innowacyjne rozwiązania konstrukcyjne obrabiarek CNC.This paper presents some construction solutions of modern numerical controlled CNC machine tools. The paper presented the stream of development trends in innovation construction of CNC machine tools, its functionality and rich kinematic possibilities. This paper is based on the fair and presented at the meeting for study design companies solutions. Includes discussion of selected synthetic functional groups of CNC machine tools, their main characteristics, disadvantages and advantages. This paper was created for young adepts of science, and indeed brings the basic but extremely important - innovative design of CNC machine tools

Dynamics of major histocompatibility complex class II-positive cells in the postischemic brain - influence of levodopa treatment

Background: Cerebral ischemia activates both the innate and the adaptive immune response, the latter being activated within days after the stroke onset and triggered by the recognition of foreign antigens. Methods: In this study we have investigated the phenotype of antigen presenting cells and the levels of associated major histocompatibility complex class II (MHC II) molecules in the postischemic brain after transient occlusion of the middle cerebral artery (tMCAO) followed by levodopa/benserazide treatment. Male Sprague Dawley rats were subjected to tMCAO for 105 minutes and received levodopa (20 mg/kg)/benserazide (15 mg/kg) for 5 days starting on day 2 after tMCAO. Thereafter, immune cells were isolated from the ischemic and contralateral hemisphere and analyzed by flow cytometry. Complementarily, the spatiotemporal profile of MHC II-positive (MHC II+) cells was studied in the ischemic brain during the first 30 days after tMCAO; protein levels of MHC II and the levels of inflammation associated cytokines were determined in the ischemic hemisphere. Results: We found that microglia/macrophages represent the main MHC II expressing cell in the postischemic brain one week after tMCAO. No differences in absolute cell numbers were found between levodopa/benserazide and vehicle-treated animals. In contrast, MHC II protein levels were significant downregulated in the ischemic infarct core by levodopa/benserazide treatment. This reduction was accompanied by reduced levels of IFN-gamma, TNF-alpha and IL-4 in the ischemic hemisphere. In the contralateral hemisphere, we exclusively detected MHC II+ cells in the corpus callosum. Interestingly, the number of cells was increased by treatment with levodopa/benserazide independent from the infarct size 14 days after tMCAO. Conclusions: Results suggest that dopamine signaling is involved in the adaptive immune response after stroke and involves microglia/macrophages

The National Swedish Lymphoma Register - a systematic validation of data quality

Background and purpose: The Swedish Lymphoma Register (SLR) was initiated in the year 2000 with the aim to monitor quality of care in diagnostics, treatment and outcome of all lymphomas diagnosed nationally among adults. Here, we present the first systematic validation of SLR records as a basis for improved register quality and patient care. Patients and methods: We evaluated timeliness and completeness of register records among patients diagnosed with lymphoma in the SLR (n n = 16,905) compared with the National Cancer Register for the period 2013-2020. Comparability was assessed through evaluation of coding routines against national and international guidelines. Accuracy of 42 variables was evaluated through re-abstraction of data from medical records among 600 randomly selected patients diagnosed in 2016-2017 and treated across all six Swedish healthcare regions. Results: Completeness was high, >95% per year for the period 2013-2018, and >89% for 2019-2020 compared to the National Cancer Register. One in four patients was registered within 3 months, and 89.9% within 2 years of diagnosis. Registration instructions and coding procedures followed the prespecified guidelines. Missingness was generally low (<5%), but high for occasional variables, for example, those describing maintenance and consolidative treatment. Exact agreement of categorical variables was high overall (>80% for 24/34 variables), especially for treatment-related data (>80% for 17/19 variables). Interpretation: Completeness and accuracy are high in the SLR, while timeliness could be improved. Finetuning of variable registration guided by this validation can further improve reliability of register reports and advance service to lymphoma patients and health care in the future

A Content-Based Image Retrieval Method Based on the Google Cloud Vision API with WordNet

[[abstract]]Content-Based Image Retrieval (CBIR) method analyzes the content of an image and extracts the features to describe images, also called the image annotations (or called image labels). A machine learning (ML) algorithm is commonly used to get the annotations, but it is a time-consuming process. In addition, the semantic gap is another problem in image labeling. To overcome the first difficulty, Google Cloud Vision API is a solution because it can save much computational time. To resolve the second problem, a transformation method is defined for mapping the undefined terms by using the WordNet. In the experiments, a well-known dataset, Pascal VOC 2007, with 4952 testing figures is used and the Cloud Vision API on image labeling implemented by R language, called Cloud Vision API. At most ten labels of each image if the scores are over 50. Moreover, we compare the Cloud Vision API with well-known ML algorithms. This work found this API yield 42.4% mean average precision (mAP) among the 4,952 images. Our proposed approach is better than three well-known ML algorithms. Hence, this work could be extended to test other image datasets and as a benchmark method while evaluating the performances.[[notice]]補正完