Pengembangan Aplikasi Multimedia Untuk Pembelajaran Satelit Astronomi NASA Dengan Teknologi Augmented Reality Berbasis Android

With the advancement of technology in various aspects of life of today's technology allows developers to create applications that aims to facilitate the public in obtaining information based on mobile. Seeing the importance of the role of astronomy satellites it needs to make a multimedia application with Augmented Reality technology that can provide an overview and brief specifications on astronomy satellite without having to go to the planetarium or observatory.This research is using MDLC (Multimedia Development Life Cycle) framework. The process of assembly (pemasangan) was performed using Unity and Vuforia library. The result is a book containing related image about NASA's astronomy satellites which functioned as a marker and an Augmented Reality application based on Android that is capable of displaying NASA's astronomical satellites on the top of marker that can display brief information about the shape and its parts when touched on the screen. The application that has been made function properly and in accordance with the concept and design. The ideal condition of using the application in places exposed to light with the intensity of 108,3 ± 2,5 lux upwards. In addition the camera is directed to the distance between 10-75 cm of the marker and the angle between 40 ± 0,5 O – 140 ± 0,5 O .. The greater the intensity of the light does not affect the sensitivity of the camera to detect the marker increases. In other words, the marker will not be detected if the distance marker on the camera more than 75 cm and the angle of the camera marker less than 40 ± 0,5 O or more than 140 ± 0,5 O though the light intensity is greater

High Grade Glioma Mimicking Voltage Gated Potassium Channel Complex Associated Antibody Limbic Encephalitis

Though raised titres of voltage gated potassium channel (VGKC) complex antibodies have been occasionally associated with extracranial tumours, mainly presenting as Morvan's Syndrome or neuromyotonia, they have not yet been reported to be associated with an intracranial malignancy. This is especially important as misdiagnosis of these conditions and delay of the appropriate treatment can have important prognostic implications. We describe a patient with a high grade glioma presenting with clinical, radiological, and serological features consistent with the diagnosis of VGKC antibody associated limbic encephalitis (LE). This is the first association between a primary brain tumour and high titre of VGKC complex antibodies. Clinicoradiological progression despite effective immunosuppressive treatment should prompt clinicians to look for alternative diagnoses. Further studies to elucidate a possible association between VGKC complex and other surface antigen antibodies with primary brain tumours should be carried out

The shocklet transform: a decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series

We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series—termed the Discrete Shocklet Transform (DST)—and an associated similarity search routine, the Shocklet Transform And Ranking (STAR) algorithm, that indicates time windows during which panels of time series display qualitatively-similar anomalous behavior. After distinguishing our algorithms from other methods used in anomaly detection and time series similarity search, such as the matrix profile, seasonal-hybrid ESD, and discrete wavelet transform-based procedures, we demonstrate the DST’s ability to identify mechanism-driven dynamics at a wide range of timescales and its relative insensitivity to functional parameterization. As an application, we analyze a sociotechnical data source (usage frequencies for a subset of words on Twitter) and highlight our algorithms’ utility by using them to extract both a typology of mechanistic local dynamics and a data-driven narrative of socially-important events as perceived by English-language Twitter

Treatment patterns and blood counts in patients with polycythemia vera treated with hydroxyurea in the United States: An analysis from the REVEAL study

BACKGROUND: Polycythemia vera (PV) is associated with increased blood cell counts, risk of thrombosis, and symptoms including fatigue and pruritus. National guidelines support the use of hydroxyurea (HU) in high-risk patients or those with some other clinical indication for cytoreduction. PATIENTS AND METHODS: REVEAL is a prospective, observational study designed to collect data pertaining to demographics, disease burden, clinical management, patient-reported outcomes, and health care resource utilization of patients with PV in the United States. In this analysis, HU treatment patterns and outcomes were assessed from 6 months prior to enrollment to the time of discontinuation, death, or data cutoff. RESULTS: Of the 1381 patients who received HU for ≥ 3 months, the median HU exposure was 23.6 months (range, 3.1-38.5 months). The most common maximum daily HU doses were 1000 mg (30.6%) and 500 mg (30.1%); only 6.4% received ≥ 2 g/d HU. Approximately one-third (32.3%) of patients had dose adjustments, 23.8% had dose interruptions, and 257 (18.6%) discontinued HU. The most common reasons for HU discontinuations and interruptions were adverse events/intolerance (37.1% and 54.5%, respectively) and lack of efficacy (35.5% and 22.1%, respectively). Of those who received HU for ≥ 3 months, 57.1% had hematocrit values \u3e 45% on ≥ 1 occasion, 33.1% continued to receive phlebotomies, and 27.4% had uncontrolled myeloproliferation. CONCLUSION: The results of this analysis emphasize the need for active management of patients with PV with appropriate HU dose titration to maintain blood count control while monitoring for signs and symptoms of HU intolerance

Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future

Proximity extension assay testing reveals novel diagnostic biomarkers of atypical parkinsonian syndromes.

OBJECTIVE: The high degree of clinical overlap between atypical parkinsonian syndromes (APS) and Parkinson's disease (PD) makes diagnosis challenging. We aimed to identify novel diagnostic protein biomarkers of APS using multiplex proximity extension assay (PEA) testing. METHODS: Cerebrospinal fluid (CSF) samples from two independent cohorts, each consisting of APS and PD cases, and controls, were analysed for neurofilament light chain (NF-L) and Olink Neurology and Inflammation PEA biomarker panels. Whole-cohort comparisons of biomarker concentrations were made between APS (n=114), PD (n=37) and control (n=34) groups using logistic regression analyses that included gender, age and disease duration as covariates. RESULTS: APS versus controls analyses revealed 11 CSF markers with significantly different levels in cases and controls (p<0.002). Four of these markers also reached significance (p<0.05) in APS versus PD analyses. Disease-specific analyses revealed lower group levels of FGF-5, FGF-19 and SPOCK1 in multiple system atrophy compared with progressive supranuclear palsy and corticobasal syndrome. Receiver operating characteristic curve analyses suggested that the diagnostic accuracy of NF-L was superior to the significant PEA biomarkers in distinguishing APS, PD and controls. The biological processes regulated by the significant proteins include cell differentiation and immune cell migration. Delta and notch-like epidermal growth factor-related receptor (DNER) had the strongest effect size in APS versus controls and APS versus PD analyses. DNER is highly expressed in substantia nigra and is an activator of the NOTCH1 pathway which has been implicated in the aetiology of other neurodegenerative disorders including Alzheimer's disease. CONCLUSIONS: PEA testing has identified potential novel diagnostic biomarkers of APS