Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproducibility of NMR Analysis of Urine Samples: Impact of Sample Preparation, Storage Conditions, and Animal Health Status

Introduction. Spectroscopic analysis of urine samples from laboratory animals can be used to predict the efficacy and side effects of drugs. This employs methods combining 1H NMR spectroscopy with quantification of biomarkers or with multivariate data analysis. The most critical steps in data evaluation are analytical reproducibility of NMR data (collection, storage, and processing) and the health status of the animals, which may influence urine pH and osmolarity. Methods. We treated rats with a solvent, a diuretic, or a nephrotoxicant and collected urine samples. Samples were titrated to pH 3 to 9, or salt concentrations increased up to 20-fold. The effects of storage conditions and freeze-thaw cycles were monitored. Selected metabolites and multivariate data analysis were evaluated after 1H NMR spectroscopy. Results. We showed that variation of pH from 3 to 9 and increases in osmolarity up to 6-fold had no effect on the quantification of the metabolites or on multivariate data analysis. Storage led to changes after 14 days at 4°C or after 12 months at −20°C, independent of sample composition. Multiple freeze-thaw cycles did not affect data analysis. Conclusion. Reproducibility of NMR measurements is not dependent on sample composition under physiological or pathological conditions.
Early detection of malicious web content with applied machine learning

This thesis explores the use of applied machine learning techniques to augment traditional methods of identifying and preventing web-based attacks. Several factors complicate the identification of web-based attacks. The first is the scale of the web. The amount of data on the web and the heterogeneous nature of this data complicate efforts to distinguish between benign sites and attack sites. Second, an attacker may duplicate their attack at multiple, unexpected locations (multiple URLs spread across different domains) with ease. Third, attacks can be hosted nearly anonymously; there is little cost or risk associated with hosting or publishing a web-based attack. In combination, these factors lead one to conclude that, currently, the webs threat landscape is unfavorably tilted towards the attacker. To counter these advantages this thesis describes our novel solutions to web security problems. The common theme running through our work is the demonstration that we can detect attacks missed by other security tools as well as detecting attacks sooner than other security responses. To illustrate this, we describe the development of BayeShield, a browser-based tool capable of successfully identifying phishing attacks in the wild. Progressing from specific to a more general approach, we next focus on the detection of obfuscated scripts (one of the most commonly used tools in webbased attacks). Finally, we present TopSpector, a system we’ve designed to forecast malicious activity prior to it’s occurrence. We demonstrate that by mining Top-Level DNS data we can produce a candidate set of domains that contains up to 65% of do-
J un 2 00 0 Spatial competition and price formation

We look at price formation in a retail setting, that is, compa nies set prices, and consumers either accept prices or go someplace else. In contrast to mos t other models in this context, we use a two-dimensional spatial structure for information ra smission, that is, consumers can only learn from nearest neighbors. Many aspects of this c an be understood in terms of generalized evolutionary dynamics. In consequence, we fi rst look at spatial competition and cluster formation without price. This leads to establis hement size distributions, which we compare to reality. After some theoretical consideratio ns, which at least heuristically explain our simulation results, we finally return to price fo rmation, where we demonstrate that our simple model with nearly no organized planning or ra tionality on the part of any of the agents indeed leads to an economically plausible pric .
Timing capacity of queues with random arrival and modified service times

A queue timing channel with random arrival and service times is investigated. The message is encoded into a sequence of additional delays that packets are subject to before they depart from the queue. We derive upper and lower bounds of the channel capacity for general arrival and service processes, and general load. We establish the channel capacity for the queue with exponential server and no load constraint by keeping the queue stable. We discuss the consequences of this result and describe a possible application where the timing channel is used to send covert information.
A pilot study of a nurse-delivered cognitive behavioral therapy intervention (Ziphamandla) for adherence and depression in HIV in South Africa

Depression is prevalent among people living with HIV in South Africa and interferes with adherence to antiretroviral therapy. This study evaluated a nurse-delivered, cognitive behavioral therapy intervention for adherence and depression among antiretroviral therapy users with depression in South Africa (n = 14). Primary outcomes were depression, antiretroviral therapy adherence, feasibility, and acceptability. Findings support robust improvements in mood through a 3-month follow up. Antiretroviral therapy adherence was maintained during the intervention period. Participant retention supports acceptability; however, modest provider fidelity despite intensive supervision warrants additional attention to feasibility. Future effectiveness research is needed to evaluate this nurse-delivered cognitive behavioral therapy intervention for adherence and depression in this context.
The role of DNA methylation in ageing and cancer

The aim of the present review paper is to survey the literature related to DNA methylation, and its association with cancer and ageing. The review will outline the key factors, including diet, which modulate DNA methylation. Our rationale for conducting this review is that ageing and diseases, including cancer, are often accompanied by aberrant DNA methylation, a key epigenetic process, which is crucial to the regulation of gene expression. Significantly, it has been observed that with age and certain disease states, DNA methylation status can become disrupted. For instance, a broad array of cancers are associated with promoter-specific hypermethylation and concomitant gene silencing. This review highlights that hypermethylation, and gene silencing, of the EN1 gene promoter, a crucial homeobox gene, has been detected in various forms of cancer. This has led to this region being proposed as a potential biomarker for diseases such as cancer. We conclude the review by describing a recently developed novel electrochemical method that can be used to quantify the level of methylation within the EN1 promoter and emphasise the growing trend in the use of electrochemical techniques for the detection of aberrant DNA methylation.
Simultaneous Observations of GRS 1758–258 in 1997 by VLA, IRAM, SEST, RXTE, and OSSE: Spectroscopy and Timing

We report the results of our multiwavelength observations of GRS 1758-258 made in 1997 August. The energy bands include radio, millimeter, X-ray, and gamma-ray. The observations enable us to obtain a complete spectrum of the source over an energy range of 2-500 keV. The spectrum shows that GRS 1758-258 was in its hard state. It is well fit by the Sunyaev-Titarchuk (ST) Compton scattering model with a plasma temperature of 45 keV and a Thomson depth of 3.3. Taking relativistic effects into account, we get a little higher plasma temperature (52 keV) by using the improved version of the ST model (HT model) plus a soft blackbody component. The spectrum is also fit by a power law with an exponential cutoff (PLE) plus a soft blackbody component. The temperature of the soft components in both models is about 1.2 keV, and the energy flux is less than 1.5% of the total X-ray and gamma-ray flux. The deduced hydrogen column density is in the range of (0.93-2.0) × 1022 cm-2. No significant iron lines are detected. The radio emission has a flat energy spectrum. The daily radio, X-ray, and gamma-ray light curves show that GRS 1758-258 was stable during the observation period but was highly variable on smaller timescales in X-rays and gamma rays. The power density spectra are typical for the low state, but we find the photon flux for the 5-10 keV band to be more variable than that in the other two energy bands (2-5 keV and 10-40 keV). Harmonically spaced quasi-periodic oscillations are observed in the power spectra. The phase lags between the hard photons and the soft photons have a flat distribution over a wide range of frequencies. A high coherence of about 1.0 (0.01-1 Hz) between the hard photons and the soft photons is also obtained in our observations. We compare these results with two variation models. Our millimeter observations did not reveal any conclusive signatures of an interaction between the jet from GRS 1758-258 and the molecular cloud that lies in the direction of GRS 1758-258.
Distribution of Yeast-Like Fungi at a University Hospital in Turkey

Background: The increased life span has led to application of more invasive procedures for diagnosis and treatment of particularly immunosuppressed individuals. This situation drew more attention to fungal infections due to existence of yeast-like fungi. Candida infections have increased due to transplant in patients, prolonged intensive care unit (ICU) stays, and invasive procedures. Recently, identification of yeast-like fungi as well as antifungal susceptibility test has been gaining more importance. Objectives: In our study, we aimed to evaluate the distribution of yeast-like fungi strains isolated from blood, urine, wound and respiratory specimens, which were sent from various departments of Izmir University School of Medicine University Hospital. Materials and Methods: The 262 yeast strains (of 13860 clinical specimens), isolated during 30.05. 2012-20.05. 2013, which were sent from various departments of Izmir University School of Medicine to Medical Microbiology Laboratory, were included in this study. Blood, wound, respiratory (sputum, tracheal secretion), and urine specimens were cultivated on blood agar and Sabouraud dextrose agar and incubated for 24-48 hours at 37°C. The isolates were cultivated on CHROMagar Candida and Cornmeal Tween 80 medium for identification. Besides, the automatized Vitek version 2.0 system was used for identification of the yeast strains as well as the antifungal susceptibility of blood culture strains. Results: A total of 262 strains, isolated from the Anesthesiology and Reanimation Unit, as well as from the departments of Hematology, Urology, Infectious Diseases, Gynecology and Obstetrics, and Ear Nose and Throat, were included in this study. The most common isolated yeast-like species was Candida albicans. C. parapsilosis was the most common yeast-like fungus isolated from blood cultures. All the blood culture strains were susceptible to amphotericin B, flucytosine, fluconazole and voriconazole. Conclusions: Candida strains isolated from newborns, elderly patients, and intensive care patients, identified and isolates from blood cultures, should be studied for antifungal susceptibility for management of the treatment. Our University Hospital is a recently opened center and these are the first data of our center. Gradually, as the number of patients increases, this data will be evaluated further.
Parameter-Tuned Data Mining: A General Framework

Real-world data mining applications often confront us with complex and noisy data, which makes it necessary to optimize the data mining models thoroughly to achieve high-quality results. We describe in this contribution an approach to tune the parameters of the model and the feature selection conjointly. The aim is to use one framework to solve a variety of tasks. We show that tuning is of large importance for high-quality results in benchmark tasks like the Data Mining Cup: tuned models achieve rank 2 or 4 in the ranking tables, where the untuned model had rank 21 out of 67. We discuss several issues of special relevance for the tuning of data mining models, namely resampling strategies and oversearching.
Ancestral graph Markov models

Abstract : This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs, called maximal ancestral graphs, has two attractive features: there is at most one edge between each pair of vertices; every missing edge corresponds to an independence relation. These features lead to a simple parametrization of the corresponding set of distributions in the Gaussian case.
A short introduction to Columbus/CAN

In this paper we shortly present a reverse engineering framework called Columbus that is able to analyze large C/C++ projects. Columbus supports project handling, data extraction, -representation, -storage and -export. Efficient filtering methods can be used to produce comprehensible diagrams from the extracted information. The flexible architecture of the Columbus system (based on plug-ins) makes it a really versatile and an easily extendible tool for reverse engineering.
Animal Face Detection Technique Using DCT

The problem of facilitating machine to learn & detect patterns of animals and categorizing them in the rapidly expanding image databases has become increasingly challenging for image retrieval systems. The problem to be solved is detection of animal faces in an image. In this regard, we propose transform domain techniques to identify the low frequency components present in an image signal to generate highly discriminative geometrical features preserving color and texture information which are analogous to human vision perceptual models. A human can do this easily, but a computer needs precise instructions and constraints. Animal detection based researchers are useful for many real life applications. Animal detection methods are helpful on the research related to locomotive behavioral of targeted animal and also to prevent dangerous animal intrusion in residential area. There are a few branches of research related to animal detection. Therefore, in this paper, Face Detection is done by Discrete Cosine Transform (DCT) approach.
The continued marginalization of campus police

Purpose The purpose of this paper is to examine a campus community’s knowledge and acceptance of their campus police as “real” police. Design/methodology/approach Using the liminality theoretical framework, this study surveyed students, faculty, staff, and administrators (n=1,484). Students were surveyed in-person, while staff, faculty, and administrators participated through an e-mail link to an online survey. Findings Results indicate that campus police are stuck in a liminal state. While 80 percent of the sample thought campus police should be armed, almost two-thirds (64 percent) did not know or were unsure of campus police officer tasks and three-quarters (75 percent) did not know or were unsure of campus police training requirements. Research limitations/implications The participants come from one university campus, so the generalizability of the sample is limited. Originality/value This study provides more evidence of the marginalization of campus police. Specifically, this study highlights that a majority of participants could not or were unable to identify campus police officers’ training and duties. Instead of using small qualitative samples, this study utilized over 1,400 participants on one campus, which provides more explanatory power about the perception problems of the campus police. This study also continues to advance and expand liminal theory.
Improving the Accuracy of Object Tracking in Three Dimensional WSNs Using Bayesian Estimation Methods

Target tracking plays a critical role in the applications of wireless sensor networks. In this paper, we propose a target tracking algorithm based on Bayesian estimation, the key steps involved in the algorithm include target detection based on a probabilistic model, preliminary localization using the two-stage target locating algorithm, further prediction adopting Bayesian estimation. Simulation results show that our simple and effective algorithm can accurately track the target and save much energy during the tracking process of a moving target.
A Knowledge Based System Converting ER Model into an Object-Oriented Database Schema

In this paper, we discuss a knowledge based system, KERO, which implements a methodology converting an entity-relationship (ER) model into a structurally objectoriented database schema. A set of rules that can convert most semantic constructs of ER models into an OODB schema is discussed. Our method is an improved one which can properly convert many-to-many relationships with non-key attributes and aggregation in ER model. The ideas which can further enhance the converted OODB schema are also presented. We think that the translation methodologies from semantic data models into OODB schema are important since most existing relational database design methodologies are based on the semantic approach and OODB systems must subsume existing relational database systems. The KERO system has been implemented in MIKE, which is a meta-interpreter written in Prolog.
Multiple Containment Methods

We present three di erent methods for nding solutions to the 2D translation-only containment problem: nd translations for k polygons that place them inside a given polygonal container without overlap. Both the container and the polygons to be placed in it may be nonconvex. First, we provide several exact algorithms that improve results for k = 2 or k = 3. In particular, we give an algorithm for three convex polygons and a nonconvex container with running time in O(m 3 n logmn), where n is the number of vertices in the container, and m is the sum of the vertices of the k polygons. This is an improvement of a factor of n 2 over previous algorithms. Second, we give an approximation algorithm for k nonconvex polygons and a nonconvex container based on restriction and subdivision of the con guration space. Third, we develop a MIP (mixed integer programming) model for k nonconvex polygons and a nonconvex container. This paper is available from the Center for Research in Computing Technology, Division of Applied Sciences, Harvard University as technical report TR-12-94. y This research was funded by the Textile/Clothing Technology Corporation from funds awarded to them by the Alfred P. Sloan Foundation and by NSF grants CCR-91-157993 and CCR-90-09272.
An Algorithm for Summarization of Paragraph Up to One Third with the Help of Cue Words Comparison

In the fast growing information era utility of technology are more precise than completing the assignment manually. The digital information technology creates a knowledge-based society with high-tech global economy which spreads over and influence the corporate and service sector to operate in more efficient and convenient way. Here an attempt was made on Extract Technology based on research. In this technology data could be refined and sourced with certainty and relevance. The application of artificial intelligence matched with the theories of machine learning would prove to be very effective. Sometime summarization of paragraph required rather than page or pages. So, Auto Summarization Model is an agnostic content summarization technology that automatically parses news, information, documents and many more into relevant and contextually accurate abbreviated summaries. This is a concept to convert a whole paragraph into one third. The Auto summarization technology reads a document, much better way than manually prepared, where, keywords and key phrases accurately weighted as they are found in the document, text or web page.
Representative BPMN 2.0 Process Model Generation from Recurring Structures

The use of process fragments to leverage reuse of process models is well established in the literature. Process fragments are manually or semi-automatically extracted and mainly focus on the textual or behavioural semantics of the process models that they are extracted from. However, in many use cases we also need to use these fragments to derive synthetic process models that satisfy specific structural properties. In order to tackle this challenge we propose a method for automatically generating synthetic, representative, executable process models expressed in Business Process Model and Notation 2.0 (BPMN 2.0) with respect to specific user-defined structural criteria. Our method identifies, selects, and combines recurring sub-structures discovered in a collection of thousands of real world process models. The recurring sub-structures are seen as an extended type of process fragments. For our method we have developed a proof-of-concept prototype and for this we discuss the experimental results obtained from its evaluation.
AEHRC & QUT at TREC 2011 Medical Track: A Concept-Based Information Retrieval Approach

The Australian e-Health Research Centre and Queensland University of Technology recently participated in the TREC 2011 Medical Records Track. This paper reports on our methods, results and experience using a concept-based information retrieval approach. Our concept-based approach is intended to overcome specific challenges we identify in searching medical records. Queries and documents are transformed from their term-based originals into medical concepts as de ned by the SNOMED-CT ontology. Results show our concept-based approach performed above the median in all three performance metrics: bref (+12%), R-prec (+18%) and Prec@10 (+6%).