Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA methylation signatures for prediction of biochemical recurrence after radical prostatectomy of clinically localized prostate cancer.

PURPOSE Diagnostic and prognostic tools for prostate cancer (PC) are suboptimal, causing overtreatment of indolent PC and risk of delayed treatment of aggressive PC. Here, we identify six novel candidate DNA methylation markers for PC with promising diagnostic and prognostic potential. METHODS Microarray-based screening and bisulfite sequencing of 20 nonmalignant and 29 PC tissue specimens were used to identify new candidate DNA hypermethylation markers for PC. Diagnostic and prognostic potential was evaluated in 35 nonmalignant prostate tissue samples, 293 radical prostatectomy (RP) samples (cohort 1, training), and 114 malignant RP samples (cohort 2, validation) collected in Denmark, Switzerland, Germany, and Finland. Sensitivity and specificity for PC were evaluated by receiver operating characteristic analyses. Correlations between DNA methylation levels and biochemical recurrence were assessed using log-rank tests and univariate and multivariate Cox regression analyses. RESULTS Hypermethylation of AOX1, C1orf114, GAS6, HAPLN3, KLF8, and MOB3B was highly cancer specific (area under the curve, 0.89 to 0.98). Furthermore, high C1orf114 methylation was significantly (P < .05) associated with biochemical recurrence in multivariate analysis in cohort 1 (hazard ratio [HR], 3.10; 95% CI, 1.89 to 5.09) and was successfully validated in cohort 2 (HR, 3.27; 95% CI, 1.17 to 9.12). Moreover, a significant (P < .05) three-gene prognostic methylation signature (AOX1/C1orf114/HAPLN3), classifying patients into low- and high-methylation subgroups, was trained in cohort 1 (HR, 1.91; 95% CI, 1.26 to 2.90) and validated in cohort 2 (HR, 2.33; 95% CI, 1.31 to 4.13). CONCLUSION We identified six novel candidate DNA methylation markers for PC. C1orf114 hypermethylation and a three-gene methylation signature were independent predictors of time to biochemical recurrence after RP in two PC patient cohorts.
Title Globalization and Similarities in Corporate Governance : A Cross-Country Analysis

Some scholars have argued that globalization should pressure firms to adopt a common set of the most efficient corporate governance practices, while others maintain that such convergence will not occur because of a variety of forms of path-dependence. With new data on governance in 24 developing countries as well as data on laws protecting shareholders and creditors in 49 developed and developing countries, we search for evidence that globalization is correlated with similarity in corporate governance. We find robust evidence of de jure similarity in governance. Interestingly, this is not driven by convergence to U.S. standards. Rather pairs of economically interdependent countries – especially if the countries are both economically developed appear to adopt common corporate governance standards, even after accounting for the effects of common legal origin. In contrast to the de jure results, we find virtually no evidence of de facto similarity in corporate governance in a battery of estimations at the country, industry and firm levels. This is consistent with either the proposition that complementarities result in different national systems appropriately having different corporate governance systems, or the proposition that globalization is not strong enough to overcome local vested interests. We conclude that globalization may have induced the adoption of some common corporate governance standards but that there is little evidence that these standards have been implemented. We are grateful to Michael Kremer, Dani Rodrik, Andrei Shleifer, John Sutton, and Louis Wells for helpful conversations, and for suggestions from seminar participants at the HBS/Tsinghua conference on Global Corporate Governance (Shanghai, July 2001), the Aspen Institute for Social Business/William Davidson Institute conference (Aspen, September 2001), the Harvard/MIT Growth and Development Seminar (October 2001), the University of Michigan Business School Corporate Strategy seminar, the Asian Development Bank conference on Corporate Governance (November 2001), Hitotsubashi University Economics Seminar (Tokyo, November 2001), Australian Graduate School of Management Seminar (Sydney, November 2001), Hong Kong University of Science and Technology Economics Seminar (November 2001), Columbia Law School Law & Economics Seminar (December 2001), Instituto Tecnológico Autónomo de México Business School seminar (February 2002), Pontificia Universidad Católica de Chile Business School seminar (March 2002), and Harvard Business School Accounting and Control seminar (April 2002). The HBS Division of Research generously funded this research. All errors remain our own.
State estimation in power engineering using the Huber robust regression technique

The least-squares method has been used extensively for state estimation in electric power engineering. However, this method is not always suitable when the process matrix is characterized by structural defects. This letter demonstrates the Huber function technique in a power engineering application. This technique is a slight departure from the least-squares method and downweighs large residuals. A statistical test to identify possible structural imperfections in the model is introduced.
Deep Learning Based Analysis of Breast Cancer Using Advanced Ensemble Classifier and Linear Discriminant Analysis

In the recent past, the Classifiers are based on genetic signatures in which many microarray studies are analyzed to predict medical results for cancer patients. However, the Signatures from different studies have been benefitted with low-intensity ratio during the classification of individual datasets has been considered as a significant point of research in the present scenario. Hence to overcome the above-discussed issue, this paper provides a Deep Learning Framework that combines an algorithm of necessary processing of Linear Discriminant Analysis (LDA) and Auto Encoder (AE) Neural Network to classify different features within the profile of gene expression. Hence, an advanced ensemble classification has been developed based on the Deep Learning (DL) algorithm to assess the clinical outcome of breast cancer. Furthermore, numerous independent breast cancer datasets and representations of the signature gene, including the primary method, have been evaluated for the optimization parameters. Finally, the experiment results show that the suggested deep learning frameworks achieve 98.27% accuracy than many other techniques such as genomic data and pathological images with multiple kernel learning (GPMKL), Multi-Layer Perception (MLP), Deep Learning Diagnosis (DLD), and Spatiotemporal Wavelet Kinetics (SWK).
High Performance and Low Power Synthesis Approach for ACTEL based FPGAs

This paper presents a novel logic synthesis and technology mapping approach for Actel-1 MUX-based Field Programmable Gate Arrays (FPGAs) for low power and high performance applications. To deal with functions of large number of variables, decomposed BDD representation has been used. As there is one-to-one correspondence between the BDD representation and the 2-to-l MUX realization, BDD representation allows the use of simple and efficient algorithm for technology mapping to MUX-based FPGAs. Several optimization techniques have been adopted in technology independent and technology mapping phases to minimize area, delay and power dissipation of the realized circuits. Performance of the proposed approach has been compared with the help of experimental results on a large number of ISCAS benchmark circuits.
Cancer incidence and mortality across Canada.

OBJECTIVES This article analyses provincial and territorial patterns in incidence and mortality rates for selected cancer sites. DATA SOURCES Cancer incidence data were obtained from the National Cancer incidence Reporting System and from the Canadian Cancer Registry. Mortality data are from the Canadian Vital Statistics Data Base. ANALYTICAL TECHNIQUES Age-standardized incidence and mortality rates were calculated for Canada and each province/territory for men and women for major cancer sites for the 1991-1993 period. MAIN RESULTS Geographic variations in cancer incidence and mortality rates are strongly influenced by trends in the four leading cancers: lung, colorectal, prostate and breast. Cancer rates tended to be significantly high in Quebec and Nova Scotia and significantly low in the three westernmost provinces. These patterns generally reflect provincial/territorial variations in smoking prevalence, dietary habits, and the extent of cancer control programs, such as screening.
Kernel Optimization in Pattern Recognition Using a Genetic Algorithm

Moritoshi Yasunaga Inst. Info. Sci. and Elec. University of Tsukuba Tsukuba, Ibaraki, 305-8573 JAPAN yasunaga@is.tsukuba.ac.jp Taro Nakamura Doctoral Program of Eng. University of Tsukuba Tsukuba, Ibaraki, 305-8573 JAPAN nakamura@ islab.is.tsukuba.ac.jp Ikuo Yoshihara Faculty of Eng. Miyazaki University Miyazaki, 889-2192 JAPAN yoshiha@cs.miyazaki-u.ac.jp Jung Hwan Kim The Center for Advanced Computer Studies Univ. of Louisiana Lafayette, LA 70504-4330 kim@cacs.sul. edu
Location through Proximity in RFID Systems

In this paper we introduce Location Estimation through Proximity Information (LEPI), an algorithm that aims at locating portable RFID readers in areas where active-RFID grids are settled. Location estimation is accomplished through a proximity information, which the reader derives by performing tag interrogation at increasing RF power levels. RFID tags surrounding the reader are incrementally detected and their known positions are eventually averaged, this providing an accurate estimation of the reader location. The performance of the proposed approach is assessed by experimental trials, conducted in indoor environments. They testify both to the actual feasibility of such a solution and to its better accuracy when compared to other reference RFID-based location tech-
Cyber Attacks and Stock Market Activity

I study how financial markets react to unexpected corporate security breaches in the short and the long-term. The main results show that daily excess returns drop, trading volume increases due to selling pressure, and liquidity deteriorates upon the public disclosure of first-time corporate hacking events. The evidence from the search frequency in Google suggests that such short-lived market reaction is due to increasing investors’ attention. Cyber attacks affect firms’ policies in the long run, up to five years after the security breach announcement. These results are consistent with the hypothesis that security breaches represent unexpected negative shocks to firms’ reputations.
An Energy-Efficient VM migrations optimization in Cloud Data Centers

owadays more attention focuses on VM management strategies in a variety of scenarios. The problem of virtual machine placement and migration is an optimization problem aiming for multiple goals. The efficiency of a data center, therefore, depends a lot on how virtual machines are provisioned and where they are located. An efficient VM allocation policy will improve energy efficiency while limiting the degradation of the quality of service (QoS) and alleviate hotspots. , but will also reduce the operating costs of the data center. Migrating VMs into a fewer number of Physical Machines (PMs) can maximize the utilization of Cloud servers and reduce the energy consumption of the Cloud data center. In this paper, we study the problem of optimal VM allocation policy and migration to minimize power consumption in a data center while preserving QoS. CloudSim simulator is used to create a cloud environment. It provides the interface to deal with the physical and virtual machines. We evaluate and compare our algorithms corresponding to different approaches in order to find the one that optimizes VM placement and migration. The simulation result shows that virtual machine placement and migration techniques minimize the energy consumption, migrations and total simulation time.owadays more attention focuses on VM management strategies in a variety of scenarios. The problem of virtual machine placement and migration is an optimization problem aiming for multiple goals. The efficiency of a data center, therefore, depends a lot on how virtual machines are provisioned and where they are located. An efficient VM allocation policy will improve energy efficiency while limiting the degradation of the quality of service (QoS) and alleviate hotspots. , but will also reduce the operating costs of the data center. Migrating VMs into a fewer number of Physical Machines (PMs) can maximize the utilization of Cloud servers and reduce the energy consumption of the Cloud data center. In this paper, we study the problem of optimal VM allocation policy and migration to minimize power consumption in a data center while preserving QoS. CloudSim simulator is used to create a cloud environment. It provides the interface to deal with the physical and virtual machines. We evaluate and compare our algorithms corresponding to different approaches in order to find the one that optimizes VM placement and migration. The simulation result shows that virtual machine placement and migration techniques minimize the energy consumption, migrations and total simulation time.N
A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning

Malware is ‘malicious software’ programs that carry out many of the cyberattacks on the Internet, including cybercrime, fraud, scams and nation-state cyberwar. These malicious software programs come in a wide range of different classifications such as viruses, Trojans, worms, spyware, botnet malware, ransomware, Rootkit, etc. Ransomware is class of malware that holds the victim’s data hostage by encrypting the data on a user’s computer to make it unavailable to the user and only decrypt it after the user pays a ransom in the form of a sum of money. To avoid detection, different variants of ransomware utilise one or more techniques in their attack flow including Machine Learning (ML) algorithms. There is, therefore, a need to understand the techniques used ransomware development and their deployment strategy in order to understand their attack flow better to develop appropriate countermeasures. In this paper, we propose DNAact-Ran, A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning. DNAact-Ran utilises Digital DNA sequencing design constraints and k-mer frequency vector. To measure the efficacy of the proposed approach, we evaluated DNAact-Run on 582 ransomware and 942 goodware instances to measure the performance of precision, recall, f-measure and accuracy. Compared to other methods, the evaluation results show that DNAact-Run can predict and detect ransomware accurately and effectively.
Comparative Polymorphism of BAT-26 between Healthy Individuals and Cancer Patients and Its Cancer Risk Implication for Local Chinese

Purpose BAT-26 is one of the representative markers for microsatellite instability evaluation and presents different polymorphisms in different ethnic populations. The current knowledge of its comparative polymorphism between healthy individuals and cancer patients in the Chinese population is insufficient. This study aims to analyze germline polymorphic variations of BAT-26 between healthy individuals and cancer patients in Chinese from Jiangsu province and the associated cancer risk implications. Methods The various BAT-26 alleles and their percentages in cervical cells from 500 healthy women were assessed by direct sequencing. Twenty of these samples were also analyzed by fragment analysis. BAT-26 of blood DNA from 24 healthy individuals and 247 cancer patients was analyzed by fragment analysis. Results Compared with the sequencing results, 122.6-122.9 bp, 123.4-123.8 bp and 124.1-124.8 bp corresponded to the A25, A26 and A27 alleles, respectively. The 524 healthy individuals showed 4.58%, 92.18% and 3.24% of A25, A26 and A27, respectively. The variant alleles A18, A24, A28, A29 and A32 were only found in cancer patients, accounting for 0.81%, 0.40%, 0.40%, 0.40% and 0.40%, respectively; the A25, A26 and A27 alleles in cancer patients accounted for 6.48%, 77.33% and 13.77%. Conclusions Healthy individuals had a stable BAT-26 profile within the quasimonomorphic variation range (QMVR), but cancer patients harbored variant alleles outside QMVR and showed a trend from quasimonomorph to polymonomorph, suggesting that variant alleles of BAT-26 in germline cells may be regarded as a potential marker of higher cancer risk in the Chinese population from Jiangsu province.
A Microbead-based System for Identifying and Characterizing RNA-Protein Interactions by Flow Cytometry*

We present a high throughput, versatile approach to identify RNA-protein interactions and to determine nucleotides important for specific protein binding. In this approach, oligonucleotides are coupled to microbeads and hybridized to RNA-protein complexes. The presence or absence of RNA and/or protein fluorescence indicates the formation of an oligo-RNA-protein complex on each bead. The observed fluorescence is specific for both the hybridization and the RNA-protein interaction. We find that the method can discriminate noncomplementary and mismatch sequences. The observed fluorescence reflects the affinity and specificity of the RNA-protein interaction. In addition, the fluorescence patterns footprint the protein recognition site to determine nucleotides important for protein binding. The system was developed with the human protein U1A binding to RNAs derived from U1 snRNA but can also detect RNA-protein interactions in total RNA backgrounds. We propose that this strategy, in combination with emerging coded bead systems, can identify RNAs and RNA sequences important for interacting with RNA-binding proteins on genomic scales.
Guide pratique pour l’utilisation de la statistique en recherche. Le cas des petits échantillons

Combien de personnes devons-nous interroger pour qu'une enquete soit credible? Voila certes la question que tout chercheur se pose avant d'entreprendre une demarche quantitative. On retrouve egalement la meme interrogation dans l'esprit des lecteurs de cette meme recherche. [. ] Plus encore, l'application de certaines regles de base permet de repondre avec confiance a la question "Comment, a partir d'un petit nombre, pouvons-nous extrapoler sur l'ensemble? " [. ] L'objectif de ce document est donc de fournir un guide, un support technique, quant a la maniere d'utiliser l'outil statistique, notamment en ce qui concerne l'echantillonnage et plus particulierement les petits echantillons. La suite de ce document s'intitule Principes d'analyse statistique pour sociolinguistes
Necklaces: Inhomogeneous and Point-Enhanced Deformable Models

Abstract In many advanced segmentation problems objects have inhomogeneous boundaries, hindering segmentation under uniform boundary assumption. We present a multifeature image segmentation method, called necklaces, that exploits local inhomogeneities to reduce the complexity of the segmentation problem. Multiple continuous boundary features, deduced from a set of training objects, are statistically analyzed and encoded into a deformable model. On the deformable model salient features are identified on the basis of the local differential geometric characteristics of the features, yielding a classification into point landmarks, curve landmarks, and sheet points. Salient features are exploited within a priority segmentation scheme that tries to find complete boundaries in an unknown image, first by landmarks and then by sheet points. The application of our method to segment vertebrae from CT data shows promising results despite their articulated morphology and despite the presence of interfering structures.
HEALTH CARE SERVICES FOR LESBIAN AND BISEXUAL WOMEN: SOME CANADIAN DATA

Specific health needs and health services for lesbian and bisexual women are relatively invisible in existing research, with little Canadian data on these topics. To redress such gaps, data were selectively analyzed from a larger study with 98 lesbian/bisexual women living in a Maritime Province in Canada. As part of a semistructured interview, women were asked to indicate whether a particular health care service was important to them. General physical examination, Pap smear, breast examination, and holistic medicine were the four services chosen with the highest frequency. HIV/AIDS information/screening and safer sex information were judged important by over half of the participants. When asked to rank their top three services, however, participants chose the physical examination, holistic medicine, and psychological counseling. Our results are discussed in terms of barriers to basic health care, accessibility of services, and the importance of accurate information about AIDS and safer sex.
Relationship Between Two Androgenetic Clam Species, Corbicula leana and Corbicula fluminea, Inferred from Mitochondrial Cytochrome b and Nuclear 28S rRNA Markers

Two shell color types, yellow (type I) and brown (type II), of hermaphrodite Corbicula fluminea clams from Ritto, Shiga Prefecture, Japan, are sympatric with both male and hermaphrodite Corbicula leana. In the present study, the mitochondrial DNA (mtDNA) cytochrome b and nuclear 28S rRNA genes of C. fluminea were sequenced to construct a haplotype network in order to investigate the genetic relationship with C. leana. Ninety C. fluminea samples revealed only two cytb haplotypes; the majority (97.8%) were CB7, while the remainder were CB1. In C. leana, only CB1 was detected in hermaphrodites, but both CB1 and CB7 were detected in males. Nuclear 28S rRNA haplotypes of C. fluminea type I individuals were divergent from those of hermaphrodite C. leana. However, C. fluminea type I clams shared haplotypes with male C. leana individuals, whereas C. fluminea type II individuals shared haplotypes with both hermaphrodite and male C. leana samples. These results suggest that it may be difficult to define a clear genetic border between these species.
Education and Training in End-of-Life Care for Certified Nursing Assistants in Long-Term Care.

BACKGROUND The growth in the number of older adults in the United States requires appropriately prepared health care providers at all levels who will be able to effectively care for this population. First-line caregivers such as certified nursing assistants (CNAs) need education and training in end-of-life care so they can provide high-quality care for residents and families in the long-term-care (LTC) setting. METHOD CNAs were recruited from an LTC facility in western New York to attend an educational intervention consisting of six 45-minute sessions on various topics selected by the researchers using the curriculum from the Core Curriculum for the Hospice and Palliative Nursing Assistant. Pretests and posttests were administered to assess changes in knowledge, and a satisfaction survey was used for the CNAs to identify additional learning needs. RESULTS Nineteen CNAs completed the six education and training sessions. Data analysis revealed a significant increase in knowledge for the participants. CNAs were also able to identify additional learning needs. CONCLUSION Significant improvement in knowledge was seen after the CNAs attended the educational intervention. CNAs working in LTC facilities need education and training in end-of-life care so they can provide appropriate and effective care to residents and their families. Education and training for CNAs led to increased knowledge of end-of-life care and desire for more educational opportunities. J Contin Educ Nurs. 2017;48(2):81-85.