Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinct Genomic Profiles in Hereditary Breast Tumors Identified by Array-based Comparative Genomic Hybridization

Mutations in BRCA1 and BRCA2 account for a significant proportion of hereditary breast cancers. Earlier studies have shown that inherited and sporadic tumors progress along different somatic genetic pathways and that global gene expression profiles distinguish between these groups. To determine whether genomic profiles similarly discriminate among BRCA1, BRCA2, and sporadic tumors, we established DNA copy number profiles using comparative genomic hybridization to BAC-clone microarrays providing <1 Mb resolution. Tumor DNA was obtained from BRCA1 (n = 14) and BRCA2 (n = 12) mutation carriers, as well as sporadic cases (n = 26). Overall, BRCA1 tumors had a higher frequency of copy number alterations than sporadic breast cancers (P = 0.00078). In particular, frequent losses on 4p, 4q, and 5q in BRCA1 tumors and frequent gains on 7p and 17q24 in BRCA2 tumors distinguish these from sporadic tumors. Distinct amplicons at 3q27.1-q27.3 were identified in BRCA1 tumors and at 17q23.3-q24.2 in BRCA2 tumors. A homozygous deletion on 5q12.1 was found in a BRCA1 tumor. Using a set of 169 BAC clones that detect significantly (P < 0.001) different frequencies of copy number changes in inherited and sporadic tumors, these could be discriminated into separate groups using hierarchical clustering. By comparing DNA copy number and RNA expression for genes in these regions, several candidate genes affected by up-or down-regulation were identified. Moreover, using support vector machines, we correctly classified BRCA1 and BRCA2 tumors (P < 0.0000004 and 0.00005, respectively). Further validation may prove this tumor classifier to be useful for selecting familial breast cancer cases for further mutation screening, particularly, as these data can be obtained using archival tissue.
Deep Learning Based on Face Emotion Recognition using an Artificial Neural Network

Facial Emotion Recognition has become a critical research domain in Artificial Intelligence due to its vital applications across multiple fields, including security and healthcare. This study aims to overcome prevailing limitations of current methodologies, which primarily rely on static image frames and neutral facial expressions, hindering optimal emotion recognition rates. Leveraging advanced deep learning techniques, this research focused on the identification of seven human emotions—happiness, anger, disgust, fear, sadness, surprise, and neutrality—using both static and dynamic images from CK+ and FER datasets. The research experimented with various models such as Lightweight MobileNet and Artificial Neural Networks, aiming for enhanced accuracy in real-time emotion recognition applications. A novel visualization technique was developed to pinpoint the crucial facial regions integral for detecting distinct emotions by analyzing classifier outputs, revealing that different emotions are sensitive to different facial areas. The results emphasize the value of specific facial regions in conveying and recognizing emotions and illustrate the substantial promise of deep learning methodologies in advancing Facial Emotion Recognition technology.
Index Array Flattening Through Program Transformation

This paper presents techniques for compiling loops with complex, indirect array accesses into loops whose array references have at most one level of indirection. The transformation allows prefetching of array indices for more efficient structuring of communication on distributed-memory machines. It can also improve performance on other architectures by enabling prefetching of data between levels of the memory hierarchy or exploitation of hardware support for vectorized gather/scatter. Our techniques are implemented in a compiler for Fortran D and execution speed improvements are given for multiprocessor and vector machines.
Therapy-related Acute Myeloid Leukemia Following Treatment for Burkitt's Lymphoma

https://doi.org/10.4068/cmj.2017. 53.3. 229 C Chonnam Medical Journal, 2017 Chonnam Med J 2017;53:229-230 Corresponding Author: Seung-Shin Lee Department of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun 58128, Korea Tel: +82-61-379-7636, Fax: +82-61-379-7628, E-mail: pepupa80@gmail.com Article History: Received July 15, 2017 Revised July 30, 2017 Accepted August 2, 2017 FIG. 1. Microscopic images show a monotonous infiltrate of medium-sized lymphoid cells with round, non-cleaved nuclei (hematoxylin and eosin stain, ×200) (A) and a strong positivity (>99% of all malignant cells) in immunohistochemical stain for the cell proliferation marker Ki-67 (B). Bone marrow aspirate shows increased numbers of blasts with some cytoplasmic vacuoles and azurophilic granules (Wright stain, ×1,000) (C). Therapy-related Acute Myeloid Leukemia Following Treatment for Burkitt’s Lymphoma
Experience with Learning Agents which Manage Internet-Based Information

To provide a~istance with t~ml~ such M retrieving USENET news articles or identifying interesting Web pages, an intelligent agent requires information about a user’s interests and needs. Machine learning techniques are now being used to acquire this information. A general architecture is presented, and two approaches to learning through observation are described. An instautiation of the architecture is then evaluated.
Performance Analysis of Image Compression Using Enhanced Vector Quantizer Designed with Self Organizing Feature Maps: The Quality Perspective

This paper presents a novel approach for designing an enhanced vector quantizer (VQ) used for compression of images. The main aim of this paper is to propose a method for efficient VQ design, which will be well applicable to all kinds of images including medical images. For the performance evaluation of images, different quality measures along with conventionally used PSNR are used. The results with the conventional method for VQ design using self organized feature maps and our proposed are discussed and compared using quality measures such as structural content, Image fidelity, normalized correlation coefficient and a newly developed by researchers, mean structural similarity index (MSSIM) and universal quality index (UQI).
The Grasshoppers (Caelifera) of Costa Rica and Panama

The purpose of this book is to present in condensed form most of what is currently known of the grasshoppers (Orthoptera Caelifera) of Costa Rica and Panama. That this goal can be even contemplated by a single author is a sign of how extremely limited our knowledge of the biology of the Tropics truly is. Costa Rica is biologically one of the best known of tropical lands, and grasshoppers are relatively large, conspicuous insects, and rather limited in the number of their species in comparison with many other groups of insects. The fact that nearly half the presently known Costa Rican grasshoppers have been described directly or indirectly as a result of the collecting work of my associates and myself in the past 30 years says more about the richness of the tropical fauna and the paucity of previous systematic work there than of our industry. Of all Costa Rican insects, perhaps only the larger day-fl ying butterfl ies are known taxonomically at the depth that temperate-zone biologists assume to be normal and universal, and even they only found their biographer with the publication of Phil DeVries’ (1987) standard-setting work.
Tensorized Label Learning on Anchor Graph

Graph-based multimedia data clustering has attracted much attention due to the impressive clustering performance for arbitrarily shaped multimedia data. However, existing graph-based clustering methods need post-processing to get labels for multimedia data with high computational complexity. Moreover, it is sub-optimal for label learning due to the fact that they exploit the complementary information embedded in data with different types pixel by pixel. To handle these problems, we present a novel label learning model with good interpretability for clustering. To be specific, our model decomposes anchor graph into the products of two matrices with orthogonal non-negative constraint to directly get soft label without any post-processing, which remarkably reduces the computational complexity. To well exploit the complementary information embedded in multimedia data, we introduce tensor Schatten p-norm regularization on the label tensor which is composed of soft labels of multimedia data. The solution can be obtained by iteratively optimizing four decoupled sub-problems, which can be solved more efficiently with good convergence. Experimental results on various datasets demonstrate the efficiency of our model.
Excessive spinal cord toxicity from intensive central nervous system ‐directed therapies

Background. Intrathecal chemotherapy, radiation therapy, and systemic chemotherapy are used for both prophylaxis and treatment of central nervous system (CNS) disease in hematologic malignancies. Twenty‐three cases of myelopathy that occurred in patients who received intensive CNS‐directed therapy were evaluated to identify the determinants of this severe CNS toxicity.
The Integration of Geographic Visualization with Knowledge Discovery in Databases and Geocomputation

This paper details the research agenda of the International Cartographic Association commission on Visualization: working group on Database-Visualization links. The paper stresses the need for the closer integration of three largely disparate technologies: geographic visualization, knowledge discovery in databases and geocomputation. The introduction explains the meaning behind these terms, the ethos behind their practice and their connections within the broad realm of knowledge construction activities. The state of the art is then described for different approaches to knowledge construction, concentrating where possible on visual and geographically oriented methods. From these sections, a research agenda is synthesized in the form of three sets of research questions addressing: (1) visual approaches to knowledge discovery, (2) visual support for knowledge construction and geocomputation and (3) databases and data models that must be satisfied to make visually-led knowledge construction a reality in the geographic sphere of interest. Conclusions relate this agenda to issues of (1) data, (2) geographic knowledge and (3) the visualization environment and pose significant challenges to the way we currently represent geographic information and knowledge.
Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steadystate data obtained from electric motor accelerated degradation testing is used for method validation. Ten threephase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life.
Compact Model for Ferroelectric Negative Capacitance Transistor With MFIS Structure

We present a physics-based compact model for a ferroelectric negative capacitance FET (NCFET) with a metal–ferroelectric–insulator–semiconductor (MFIS) structure. The model is computationally efficient, and it accurately calculates the gate charge density as a function of the applied voltages. For the first time, an explicit expression for the channel current in bulk NCFET is also deduced taking into account the spatial variation of ferroelectric polarization in the longitudinal direction. Using current continuity condition in the channel, we find that different regions of the ferroelectric may operate in a positive or a negative capacitance state depending on the external biases. The model captures the impact of ferroelectric thickness scaling and variation in the ferroelectric material parameters, and has been validated against the implicit approach involving full numerical computations as well as experimental data. We also compare the device characteristics of the MFIS structure with those of the metal–ferroelectric–metal–insulator–semiconductor structure.
CLINICOPATHOLOGICAL STUDY ON FATAL MYOCARDIAL SARCOIDOSIS *

Presented here was an outline of fatal myocardial sarcoidosis in Japan based on 42 autopsy cases. The incidence, clinical features, pathological findings (particularly the distribution of sarcoid lesions in various organs, and gross as well as histological patterns have been reviewed. The ratio of fatal myocardial sarcoidosis to the total number of sarcoidosis deaths is higher than that of other countries and it appears characteristic of Japanese sarcoidosis that the disease occurs here predominantly after the fourth decade in females. Only 12% of myocardial sarcoidosis cases are diagnosed exactly. Conduction disturbances and dysrhythmias due to myocardial damage are its most common manifestations. The modes of presentation or cardiac death are sudden death, 16 cases (41%); congestive heart failure, 9 cases (23.1%); Adams-Stokes syndrome, except sudden death, 7 cases (17.9%); death due to dysrhythmia, 6 cases (15.4%); and 2 pacemaker deaths among the application of pacemakers, 7 cases. As for the ECG findings, A-V block and bundle-branch block was observed in most cases and ectopic beats were also frequently observed. The difficulty in exact diagnosis of myocardial sarcoidosis appears to be due to the frequency of lack of manifestation of systemic sarcoidosis as well as insidious cardiac involvement. The gross findings on myocardial lesions showed 3 patterns and each of the localized patterns corresponded to the histological extension. The conglomerate-band-like pattern corresponded to expansive invasion, the dendrite pattern to interstitial extension. The histological findings on the myocardial lesions were classified into four types: 1) exudative type, 2) granuloma type, 3) combined type of granuloma and fibrosis, 4) fibrotic type. Two extreme cases showed the exudative and fibrotic type, respectively, and were discussed.
Bienzymatic Biosensor for Rapid Detection of Aspartame by Flow Injection Analysis

A rapid, simple and stable biosensor for aspartame detection was developed. Alcohol oxidase (AOX), carboxyl esterase (CaE) and bovine serum albumin (BSA) were immobilised with glutaraldehyde (GA) onto screen-printed electrodes modified with cobalt-phthalocyanine (CoPC). The biosensor response was fast. The sample throughput using a flow injection analysis (FIA) system was 40 h−1 with an RSD of 2.7%. The detection limits for both batch and FIA measurements were 0.1 μM for methanol and 0.2 μM for aspartame, respectively. The enzymatic biosensor was successfully applied for aspartame determination in different sample matrices/commercial products (liquid and solid samples) without any pre-treatment step prior to measurement.
Impact of a pharmacy benefit change on new use of mail order pharmacy among diabetes patients: the Diabetes Study of Northern California (DISTANCE).

OBJECTIVE To assess the impact of a pharmacy benefit change on mail order pharmacy (MOP) uptake. DATA SOURCES/STUDY SETTING Race-stratified, random sample of diabetes patients in an integrated health care delivery system. STUDY DESIGN In this natural experiment, we studied the impact of a pharmacy benefit change that conditionally discounted medications if patients used MOP and prepaid two copayments. We compared MOP uptake among those exposed to the benefit change (n = 2,442) and the reference group with no benefit change (n = 8,148), and estimated differential MOP uptake across social strata using a difference-in-differences framework. DATA COLLECTION/EXTRACTION METHODS Ascertained MOP uptake (initiation among previous nonusers). PRINCIPAL FINDINGS Thirty percent of patients started using MOP after receiving the benefit change versus 9 percent uptake among the reference group (p < .0001). After adjustment, there was a 26 percentage point greater MOP uptake (benefit change effect). This benefit change effect was significantly smaller among patients with inadequate health literacy (15 percent less), limited English proficiency (14 percent less), and among Latinos and Asians (24 and 16 percent less compared to Caucasians). CONCLUSIONS Conditionally discounting medications delivered by MOP effectively stimulated MOP uptake overall, but it unintentionally widened previously existing social gaps in MOP use because it stimulated less MOP uptake in vulnerable populations.
Prevention of ischemically induced neointimal hyperplasia using ex vivo antisense oligodeoxynucleotides.

BACKGROUND Chronic graft vascular disease in cardiac allografts results from coronary artery neointimal formation. Vascular ischemia has been shown to provoke the development of neointimal hyperplasia through endothelial damage. We used a rodent model of neointimal formation to test the in vivo effects of antisense oligodeoxynucleotides (AS ODN) specifically designed to block this process. METHODS Aortas from ACI rats were mock transfected or transfected with 18 base pair AS ODNs against the 5' start codon region of both CDC2 kinase, and proliferating cell nuclear antigen (PCNA) mRNA. Transfection was accomplished by placing the aorta in ODN solution (transfected group, 40 micromol/L of each sequence) or saline solution alone (mock-transfected group) and exposing to hydrostatic pressure (2 atm) for 24 hours at 4 degrees C. Vessels were then interposition-grafted into the abdominal aorta of untreated isogenic recipients and procured on postoperative days (POD) 1, 6, and 14. RESULTS Nuclear localization of fluorescein isothiocyanate ODN was observed in 81%+/-3% of medial smooth muscle cells at 24 hours after interposition grafting and reperfusion. Efficacy of AS ODNs at blocking CDC2 kinase and PCNA was verified on POD 6 by enzyme-linked immunosorbent assay. This blockade significantly reduced ischemically induced vascular narrowing on POD 14 as assessed by use of computerized image analysis (3.85%+/-2.45% luminal narrowing for transfected vs 7.11%+/-2.03% for control subjects, p < 0.03). CONCLUSIONS Our data show the efficacy of AS ODN at blocking rat PCNA and CDC2 kinase up-regulation provoked by ischemia. This ex vivo approach had beneficial effects against vascular narrowing in a rodent model of ischemically induced neointimal hyperplasia, an antigen-independent factor important in the development of subsequent chronic graft vascular disease.
Continuous positive airway pressure treatment of mild to moderate obstructive sleep apnea reduces cardiovascular risk.

RATIONALE Obstructive sleep apnea (OSA) is linked to increased cardiovascular risk, but the impact of mild forms of OSA and their treatment on cardiovascular outcomes remains controversial. OBJECTIVES To prospectively investigate cardiovascular outcomes in treated versus untreated patients with OSA. METHODS Consecutive sleep laboratory patients with all degrees of OSA were included. Endpoints were nonfatal (myocardial infarction, stroke, and acute coronary syndrome requiring revascularization procedures) and fatal (death from myocardial infarction or stroke) cardiovascular events. MEASUREMENTS AND MAIN RESULTS Comparison of event-free survival rates in treated versus untreated patients (Kaplan-Meier estimates, log-rank test). Of 449 patients enrolled (age, 56.0 +/- 10.5 years; body mass index, 30.8 +/- 5.4 kg/m(2)), 364 patients received OSA treatment, and 85 patients remained untreated. Median follow-up was 72.0 months (range, 1-156). Mean apnea-hypopnea index before treatment was 30.9 +/- 21.8/hour in treated and 15.3 +/- 13.0/hour in untreated patients, but there were no differences in cardiovascular comorbidities or risk factors. In patients with mild-moderate OSA (n = 288), events were more frequent in untreated patients (estimated event-free survival at 10 yr, 51.8 vs. 80.3% [P < 0.001]; absolute risk reduction, 28.5%; number needed to treat to prevent one event/10 yr, 3.5). After adjustment for age, gender, cardiovascular risk factors, and comorbidities at baseline, OSA treatment was an independent predictor for events (hazard ratio, 0.36; 95% confidence interval, 0.21-0.62; P < 0.001). CONCLUSIONS OSA treatment was associated with a cardiovascular risk reduction of 64% independent from age and preexisting cardiovascular comorbidities. OSA treatment should be considered for primary and secondary cardiovascular prevention, even in milder OSA.
Contextual Help for Free with Formal Dialogue Design

This paper presents how the contextual help system for a user-driven application may be generated automatically from the formal specifications of the application. In our case the formal specifications are based on Petri nets and the help system is built by transforming the Petri nets in an augmented transition network. This network is then used by the contextual help system in order to answer users' questions.
Selecting Reliable mRNA Expression Measurements Across Platforms Improves Downstream Analysis

With increasing use of publicly available gene expression data sets, the quality of the expression data is a critical issue for downstream analysis, gene signature development, and cross-validation of data sets. Thus, identifying reliable expression measurements by leveraging multiple mRNA expression platforms is an important analytical task. In this study, we propose a statistical framework for selecting reliable measurements between platforms by modeling the correlations of mRNA expression levels using a beta-mixture model. The model-based selection provides an effective and objective way to separate good probes from probes with low quality, thereby improving the efficiency and accuracy of the analysis. The proposed method can be used to compare two microarray technologies or microarray and RNA sequencing measurements. We tested the approach in two matched profiling data sets, using microarray gene expression measurements from the same samples profiled on both Affymetrix and Illumina platforms. We also applied the algorithm to mRNA expression data to compare Affymetrix microarray data with RNA sequencing measurements. The algorithm successfully identified probes/genes with reliable measurements. Removing the unreliable measurements resulted in significant improvements for gene signature development and functional annotations.
Copy-number variations and human disease.

R.A. H. is supported by operating grants from the Canadian Institutes of Health Research (MT14030), the Heart and Stroke Foundation of Ontario, Genome Canada through the Ontario Genomics Institute, a Career Investigator award from the Heart and Stroke Foundation of Ontario, the Edith Schulich Vinet Canada Research Chair (Tier I) in Human Genetics, and the Jacob J. Wolfe Distinguished Medical Research Chair.