PENALARAN BERBASIS KASUS UNTUK MENDIAGNOSA PENYAKIT INFEKSI MENULAR SEKSUAL (IMS) MENGGUNAKAN ALGORITMA WEIGHTED EUCLIDEAN DISTANCE

Case-based reasoning is a methodology for solving problems by utilizing previous experience. In this study the authors apply case-based reasoning to diagnose sexually transmitted infection using the weighted Euclidean distance method. Source of the knowledge base was obtained by collecting medical record of patients with sexually transmitted infections in 2016-2017. The process of finding a solution starts with eliminating irrelevant data using the C4.5 method and continues with the calculation of the similarity value using the Weighted Euclidean Distance algorithm. This system can diagnose 5 types of sexually transmitted infections based on 123 existing symptoms. System result in the form of sexually transmitted infections based on symptoms experienced by the patient, treatment solution and presentation of similarities between new cases and old cases. Based on the result of testing with 127 cases of sexually transmitted infections obtained result: testing uses the K-Fold Cross Validation scenario, the total data is divided into 10fold and the testing process is divided into 2 parts, namely testing using indexing and testing without using indexing. For testing using the highest accuracy indexing obtained at 90.84% in the second fold, and the average accuracy of the entire fold is 88.55% with the average time generated 9498 ms (millisecond), while testing without using the highest accuracy indexing obtained by 63.03% in the second fold, and the average accuracy of the entire fold is 53.48% with the average time generated 9975 ms (millisecond).  Penalaran Berbasis Kasus adalah sebuah metedologi untuk penyelesaian masalah dengan memanfaatkan pengalaman sebelumnya. Pada penelitian ini penulis menerapkan penalaran berbasis kasus untuk mendiagnosa penyakit infeksi menular seksual menggunakan metode weighted euclidean distance. Sumber basis pengetahuan diperoleh dengan mengumpulkan berkas rekam medis pasien penyakit infeksi menular seksual pada tahun 2016-2017. Proses pencarian solusi dimulai dengan mengeliminasi data yang tidak relevan menggunakan C4.5 dan berlanjut dengan perhitungan nilai kemiripan menggunakan algoritma weighted euclidean distance. Sistem ini dapat mendiagnosis 5 jenis penyakit infeksi menular seksual berdasarkan 123 gejala yang ada. Hasil sistem berupa jenis penyakit infeksi menular seksual berdasarkan gejala yang dialami pasien, solusi pengobatan dan presentasi kemiripan kasus baru dengan kasus lama. Berdasarkan hasil pengujian dengan 127 kasus infeksi menular seksual (IMS) didapatkan hasil: Pengujian menggunakan skenario K-Fold Cross Validation, total data dibagi menjadi 10 fold dan proses pengujian dibagi menjadi 2 bagian yaitu pengujian menggunakan indexing dan pengujian tanpa menggunakan indexing. Untuk pengujian menggunakan indexing akurasi tertinggi yang didapat sebesar 90.84% pada fold ke-2, dan rata-rata akurasi dari keseluruhan fold adalah sebesar 88.55% dengan rata-rata waktu yang dihasillkan 9498 ms (milidetik) sedangkan pengujian tanpa menggunakan indexing akurasi tertinggi yang didapat sebesar 63.03% pada fold ke-2, dan rata-rata akurasi dari keseluruhan fold adalah sebesar 53.48% dengan rata-rata waktu yang dihasilkan 9975 ms (milidetik). &nbsp

PB1-F2 Finder: scanning influenza sequences for PB1-F2 encoding RNA segments

<p>Abstract</p> <p>Background</p> <p>PB1-F2 is a major virulence factor of influenza A. This protein is a product of an alternative reading frame in the PB1-encoding RNA segment 2. Its presence of is dictated by the presence or absence of premature stop codons. This virulence factor is present in every influenza pandemic and major epidemic of the 20th century. Absence of PB1-F2 is associated with mild disease, such as the 2009 H1N1 (“swine flu”).</p> <p>Results</p> <p>The analysis of 8608 segment 2 sequences showed that only 8.5% have been annotated for the presence of PB1-F2. Our analysis indicates that 75% of segment 2 sequences are likely to encode PB1-F2. Two major populations of PB1-F2 are of lengths 90 and 57 while minor populations include lengths 52, 63, 79, 81, 87, and 101. Additional possible populations include the lengths of 59, 69, 81, 95, and 106. Previously described sequences include only lengths 57, 87, and 90. We observed substantial variation in PB1-F2 sequences where certain variants show up to 35% difference to well-defined reference sequences. Therefore this dataset indicates that there are many more variants that need to be functionally characterized.</p> <p>Conclusions</p> <p>Our web-accessible tool PB1-F2 Finder enables scanning of influenza sequences for potential PB1-F2 protein products. It provides an initial screen and annotation of PB1-F2 products. It is accessible at <url>http://cvc.dfci.harvard.edu/pb1-f2</url>.</p

Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000-13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network

Direct Identification of an HPV-16 Tumor Antigen from Cervical Cancer Biopsy Specimens

Persistent infection with high-risk human papilloma viruses (HPV) is the worldwide cause of many cancers, including cervical, anal, vulval, vaginal, penile, and oropharyngeal. Since T cells naturally eliminate the majority of chronic HPV infections by recognizing epitopes displayed on virally altered epithelium, we exploited Poisson detection mass spectrometry (MS3) to identify those epitopes and inform future T cell-based vaccine design. Nine cervical cancer biopsies from HPV-16 positive HLA-A*02 patients were obtained, histopathology determined, and E7 oncogene PCR-amplified from tumor DNA and sequenced. Conservation of E7 oncogene coding segments was found in all tumors. MS3 analysis of HLA-A*02 immunoprecipitates detected E711–19 peptide (YMLDLQPET) in seven of the nine tumor biopsies. The remaining two samples were E711–19 negative and lacked the HLA-A*02 binding GILT thioreductase peptide despite possessing binding-competent HLA-A*02 alleles. Thus, the conserved E711–19 peptide is a dominant HLA-A*02 binding tumor antigen in HPV-16 transformed cervical squamous and adenocarcinomas. Findings that a minority of HLA-A*02:01 tumors lack expression of both E711–19 and a peptide from a thioreductase important in processing of cysteine-rich proteins like E7 underscore the value of physical detection, define a potential additional tumor escape mechanism and have implications for therapeutic cancer vaccine development

Human Leukocyte Antigen Typing Using a Knowledge Base Coupled with a High-Throughput Oligonucleotide Probe Array Analysis

Human leukocyte antigens (HLA) are important biomarkers because multiple diseases, drug toxicity, and vaccine responses reveal strong HLA associations. Current clinical HLA typing is an elimination process requiring serial testing. We present an alternative in situ synthesized DNA-based microarray method that contains hundreds of thousands of probes representing a complete overlapping set covering 1,610 clinically relevant HLA class I alleles accompanied by computational tools for assigning HLA type to 4-digit resolution. Our proof-of-concept experiment included 21 blood samples, 18 cell lines, and multiple controls. The method is accurate, robust, and amenable to automation. Typing errors were restricted to homozygous samples or those with very closely related alleles from the same locus, but readily resolved by targeted DNA sequencing validation of flagged samples. High-throughput HLA typing technologies that are effective, yet inexpensive, can be used to analyze the world’s populations, benefiting both global public health and personalized health care

Exploring Hydride Formation in Stainless Steel Revisits Theory of Hydrogen Embrittlement

Various mechanisms have been proposed for hydrogen embrittlement, but the causation of hydrogen-induced material degradation has remained unclear. This work shows hydrogen embrittlement due to phase instability (decomposition). In-situ diffraction measurements revealed metastable hydrides formed in stainless steel, typically declared as a non-hydride forming material. Hydride formation is possible by increasing the hydrogen chemical potential during electrochemical charging and low defect formation energy of hydrogen interstitials. Our findings demonstrate that hydrogen-induced material degradation can only be understood if measured in situ and in real-time during the embrittlement process.Comment: 31 Pages, 18 Figures, Preprin

Nonlinear Time Series Analysis of Nodulation Factor Induced Calcium Oscillations: Evidence for Deterministic Chaos?

Legume plants form beneficial symbiotic interactions with nitrogen fixing bacteria (called rhizobia), with the rhizobia being accommodated in unique structures on the roots of the host plant. The legume/rhizobial symbiosis is responsible for a significant proportion of the global biologically available nitrogen. The initiation of this symbiosis is governed by a characteristic calcium oscillation within the plant root hair cells and this signal is activated by the rhizobia. Recent analyses on calcium time series data have suggested that stochastic effects have a large role to play in defining the nature of the oscillations. The use of multiple nonlinear time series techniques, however, suggests an alternative interpretation, namely deterministic chaos. We provide an extensive, nonlinear time series analysis on the nature of this calcium oscillation response. We build up evidence through a series of techniques that test for determinism, quantify linear and nonlinear components, and measure the local divergence of the system. Chaos is common in nature and it seems plausible that properties of chaotic dynamics might be exploited by biological systems to control processes within the cell. Systems possessing chaotic control mechanisms are more robust in the sense that the enhanced flexibility allows more rapid response to environmental changes with less energetic costs. The desired behaviour could be most efficiently targeted in this manner, supporting some intriguing speculations about nonlinear mechanisms in biological signaling

Variational Bayesian causal connectivity analysis for fMRI

The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.This work was partially supported by the National Institute of Child Health and Human Development (R01 HD042049). Martin Luessi was partially supported by the Swiss National Science Foundation Early Postdoc Mobility fellowship 148485. This work was supported in part by the Department of Energy under Contract DE-NA0000457, the “Ministerio de Ciencia e Innovación” under Contract TIN2010-15137, and the CEI BioTic with the Universidad de Granada Data were provided (in part) by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University

Sparsity-based single-shot sub-wavelength coherent diffractive imaging

We present the experimental reconstruction of sub-wavelength features from the far-field intensity of sparse optical objects: sparsity-based sub-wavelength imaging combined with phase-retrieval. As examples, we demonstrate the recovery of random and ordered arrangements of 100 nm features with the resolution of 30 nm, with an illuminating wavelength of 532 nm. Our algorithmic technique relies on minimizing the number of degrees of freedom; it works in real-time, requires no scanning, and can be implemented in all existing microscopes - optical and non-optical