Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells

While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone posttranslational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.National Institutes of Health (U.S.) (Grant GM110174

Development of Vibration Specifications for LRUs on Fighter Aircraft from Flight Data

Air defense of many countries, notably India use different types of aircraft for their air combat operations. These types could include twin engine or single engine fighters, have twin/single fins and could have radically different design and operational philosophies. On the other hand LRU's and missile/gun platforms may become common as standardization and indeginization and single sourcing is attempted. These could be mounted at various locations of an aircraft fuselage, wing etc. As aircraft age, retrofitting and upgrades are now extremely cost effective. These upgrades especially of radar, avionics etc. need to be accommodated at various locations. Weapon systems also change over time and new advanced technology installed on these aircraft. While these are generally qualified to various standards like MIL-810E, the qualification by these standards is based on a number of assumptions. Sources of vibration identified are engine noise impinging on aircraft structures, turbulence, shock pressure pulse in gun firing, maneuvers, buffeting etc. Typical MIL 810E acknowledges that vibration spectra are characteristic of the particular airframe and evaluated through measured data. It notes that outer regions of flexible structure are especially where the data is required. MIL-810E also provides a zonal test condition at various zones: Wing, fuselage, equipment mounted on engines etc. and accounting for whether the aircraft is propeller driven or by turbine engine, the spectra changes. Typical for helicopter the rotor frequencies drive the zoning that needs to be considered

A quantitative data representation framework for structural and functional MR Imaging with application to prostate cancer detection

Prostate cancer (CaP) is currently the second leading cause of cancer-related deaths in the United States among men, but there is a paucity of non-invasive image-based information for CaP detection and staging in vivo. Studies have shown the utility of multi-protocol magnetic resonance imaging (MRI) to improve CaP detection accuracy by using both T2-weighted (T2w), dynamic contrast enhanced (DCE), and diffusion weighted (DWI) MRI information. In this thesis, we present methods for quantitative representation of structural and functional imaging data with the objective of building automated classifiers to improve CaP detection accuracy in vivo. In vivo disease presence was quantified via extraction of textural signatures from T2w MRI. Evaluation of these signatures showed that CaP appearance within each of the two dominant prostate regions (central gland, peripheral zone) is significantly different. A classifier trained on zone-specific features also yielded a higher detection accuracy compared to a simpler, monolithic combination of all the texture features. While a number of automated classifiers are available, classifier choice must account for limitations in dataset size and annotation (such as with in vivo prostate MRI). A comprehensive evaluation of different classifier schemes was undertaken for the specific problem of automated CaP detection via T2w MRI on a zonewise basis. It was found that simple classifiers yielded significantly improved CaP detection accuracies compared to complex classifiers. Fundamental differences must be overcome when constructing a unified quantitative representation of structural (T2w) and functional (DCE, DWI) MRI. We present a novel technique, referred to as consensus embedding, which constructs a lower dimensional representation (embedding) from a high dimensional feature space such that information (class-based or otherwise) is optimally preserved. Consensus embedding is shown to result in an improved representation of the data compared to alternative DR-based strategies in a variety of experimental domains. A unified quantitative representation of T2w, DCE, and DWI prostate MRI was constructed via the consensus embedding framework. This yielded an integrated classifier which was more accurate for CaP detection in vivo as compared to using structural and functional information individually, or using a naive combination of such differing types of information.Ph. D.Includes bibliographical referencesIncludes vitaby Satish Easwar Viswanat

Pengaruh Motivasi, Efikasi Diri, Metakognisi, dan Kecerdasan Ketahanmalangan Terhadap Prestasi Belajar Metamatika Siswa SMK Negeri di Kota Makassar.

ABSTRAK Penelitian ini bertujuan untuk: (1) Mengetahui gambaran motivasi belajar , efikasi diri, metakognisi dan kecerdasan ketahanmalangan terhadap prestasi belajar matematika siswa (2) Mengetahui dan menjelaskan seberapa besar pengaruh motivasi terhadap prestasi belajar matematika siswa baik secara berlangsung maupun tidak langsung (melalui kecerdasan ketahanmalangan) siswa (3) mengetahui dan menjelaskan seberapa besar pengaruh efikasi diri terhadap prestasi belajar matematika siswa baik secara berlangsung maupun tidak langsung (melalui kecerdasan ketahanmalangan) siswa, (4) mengetahui dan menjelaskan Seberapa besar pengaruh metakognisi terhadap prestasi belajar matematika siswa baik secara berlangsung maupun tidak langsung (melalui kecerdasan ketahanmalangan) siswa. Data dianalisis dengan statistik deskriptif dan analysis model SEM (Structural Equation Modeling) dengan bantuan paket software SPSS versi 20 dan AMOS (Analysis Of Moment Structure) versi 20. Jenis penelitian ini adalah ex-post facto yang bersifat kausalitas. Populasi dalam penelitian ini adalah siswa SMK Negeri di Kota Makassar. Data dikumpulkan melalui 253 orang sampel yang terpilih dengan menggunakan teknik penyampelan berkelompok (equalsize cluster random sampling). Hasil Penelitian menunjukkan bahwa: (1) Rata-rata sampel memiliki Motivasi, efikasi diri, metakognisi, kecerdasan ketahanmalangan dan prestasi belajar ada pada kategori sedang, (2) Motivasi berpengaruh negatif terhadap prestasi belajar matematika sebesar -0,058, Motivasi berpengaruh positif terhadap prestasi belajar matematika melalui kecerdasan ketahanmalangan dalam belajar matematika sebesar 0,000; (3) Efikasi diri berpengaruh positif terhadap kecerdasan ketahanmalangan belajar matematika sebesar 0,986, Efikasi diri berpengaruh positif terhadap prestasi belajar matematika melalui kecerdasan ketahanmalangan sebesar 0,004; (4) Metakognisi dalam belajar matematika berpengaruh positif terhadap prestasi belajar matematika sebesar 0,85, Metakognisi berpengaruh positif terhadap kecerdasan ketahanmalangan belajar matematika sebesar 0,986; 5) Kecerdasan ketahanmalangan berpengaruh positif terhadap prestasi belajar matematika sebesar 0,37. ABSTRACT REZKI RAMDANI, 2014, The Influence Of Motivation, Self Efficacy, Meta-cognition, and Unfortunate Resilience Intelligence toward the Student’s Learning Achievement in Mathematics in Public Vocational Schools in Makassar (Supervised By Muhammad Darwis and Hisyam Ihsan). This research aims at (1) discovering the description of the learning motivation, self efficiency, meta-cognition, and unfortunate resilience intelligence toward the student’s learning achievement in Mathematics, (2) examining and explaining the influence of motivation toward the student’s learning achievement directly and indirectly (through unfortunate resilience intelligence) , (3) examining and explaining the influence of self efficiency toward the student’s learning achievement directly and indirectly (through unfortunate resilience intelligence), (4) examining and explaining the influence of meta-cognition toward the student’s learning achievement directly and indirectly (through unfortunate resilience intelligence). This research was a causality ex-post fact service. The population of this research was the students of SMKN (Public Vocational Schools) in Makassar. The data was collected through 253 samples which were chosen by using equality cluster random sampling technique. The data was analyzed by descriptive statistic and SEM (Structural and Equation Modeling) analysis with assistance of SPSS version 20 and AMOS (Analysis of Moment Structure) software package. The result of this research showed that: (1) the average of the sample had motivation, self efficacy, meta-cognition, and unfortunate resilience intelligence and learning achievement in moderate strategy, (2) motivation gave negative influence toward the student’s learning achievement in Mathematics at -0.058, Motivation gave positive influence toward the student’s learning achievement in Mathematics through unfortunate resilience intelligence in learning Mathematics at 0.000, (3) self efficacy gave positive influence toward the student’s unfortunate resilience intelligence in learning Mathematics at 0.986, self efficacy gave positive influence toward the student’s learning achievement in Mathematics through unfortunate resilience intelligence in learning Mathematics at 0.004, (4) meta-cognition in Mathematics gave positive influence toward the students’ learning achievement in Mathematics at 0.85, meta-cognition gave positive influence toward unfortunate resilience intelligence in learning Mathematics at 0.986, (5) unfortunate resilience intelligence gave positive influence toward the student’s learning achievement in Mathematics at 0.37

Madabhushi A: A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

ABSTRACT Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multisite ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate