Miracle-dz/UniFAR-FTUs huggingface dataset:
An educational project to improve knowledge related to pulse oximetry.

BACKGROUND Pulse oximetry is a frequently used, noninvasive monitoring tool for assessing arterial blood oxygenation. Physicians, registered nurses, and respiratory therapists are responsible for the accurate interpretation of pulse oximetry data as part of the evaluation and management of acutely and critically ill patients. OBJECTIVES (1) To evaluate the extent of current knowledge about pulse oximetry and (2) to increase clinicians' knowledge of research-based practices related to the appropriate use of pulse oximetry and interpretation of its results. METHODS A test/survey of 17 true-false questions based on the research-based practice protocol of the American Association of Critical-Care Nurses was developed to evaluate current knowledge of pulse oximetry. A convenience sample of medical, nursing, and respiratory therapy staff was invited to complete the test/survey before and several months after an educational program to improve staff members' knowledge of pulse oximetry. The program included educational forums, policy changes, competency checklists, and verification of inclusion of research-based principles in orientation programs. RESULTS A total of 442 staff members completed the test/survey given before the educational program: 331 nurses, 82 physicians, and 29 respiratory therapists. The overall mean percentage of correct answers was 66%. Differences between disciplines were significant: respiratory therapists scored slightly higher (76%) than did nurses (64%) and physicians (66%) (P = .01). The scores on the test/survey given after the educational program increased significantly, from 66% to 82% (P < .01). CONCLUSIONS This educational project improved staff members' knowledge of pulse oximetry monitoring.
PriB Stimulates PriA Helicase via an Interaction with Single-stranded DNA*

The frequency with which replication forks break down in all organisms requires that specific mechanisms ensure completion of genome duplication. In Escherichia coli a major pathway for reloading of the replicative apparatus at sites of fork breakdown is dependent on PriA helicase. PriA acts in conjunction with PriB and DnaT to effect loading of the replicative helicase DnaB back onto the lagging strand template, either at stalled fork structures or at recombination intermediates. Here we showed that PriB stimulates PriA helicase, acting to increase the apparent processivity of PriA. This stimulation correlates with the ability of PriB to form a ternary complex with PriA and DNA structures containing single-stranded DNA, suggesting that the known single-stranded DNA binding function of PriB facilitates unwinding by PriA helicase. This enhanced apparent processivity of PriA might play an important role in generating single-stranded DNA at stalled replication forks upon which to load DnaB. However, stimulation of PriA by PriB is not DNA structure-specific, demonstrating that targeting of stalled forks and recombination intermediates during replication restart likely resides with PriA alone.
Modeling Wavelength-dependent BRDFs as Factored Tensors for Real-time Spectral Rendering

Spectral rendering takes the full visible spectrum into account when calculating light-surface interaction and can overcome the well-known deficiencies of rendering with tristimulus color models. In this paper we show how to represent wavelength-dependent BRDFs as factored tensors. We use this representation for realtime spectral rendering on modern graphics hardware. Strong data compression and fast rendering times are achieved for mostly diffuse and moderately glossy isotropic surfaces. The method can handle high-resolution tabulated BRDFs, including non-reciprocal ones, which makes it well-suited for measured data. We analyze our approach numerically and visually. One area of application for our research is virtual design applications that require high color fidelity at interactive frame rates.
Constraint-Based Program Debugging Using Data Structure Repair

Developers have used data structure repair over the last few decades as an effective means to recover on-the-fly from errors in program state. Traditional repair techniques were based on dedicated repair routines, whereas more recent techniques have used invariants that describe desired structural properties as the basis for repair. All repair techniques are designed with one primary goal: run-time error recovery. However, the actions that any such technique performs to repair an erroneous program state are meant to produce the effect of the actions of a (hypothetical) correct program. The key insight in this paper is that repair actions on the program state can guide debugging of code (when the erroneous program execution is due to a fault in the program and not an external event). This paper presents an approach that abstracts concrete repair actions that a routine performs to repair an erroneous state into a sequence of program statements that perform the same actions using variables visible in the scope of the faulty code. Thus, appending the generated statements to the original code is akin to performing the repair from within the program. Our implementation uses the Juzi data structure repair tool as an enabling technology. Experimental results using a library data structure as well as two applications demonstrate the effectiveness of our approach in enabling repair of faulty code.
Storage Embedded Networks (SEN) and Adaptive Caching Using Multiple Experts (ACME)

The gap between CPU speeds and the speed of the technologies p roviding the data is increasing. This causes the performance of processes to be l imit d by the performance of the storage devices, the networks and the buses. Furtherm or , the number of CPUs that share these data access resources is growing exponenti ally. Caching, prefetching and parallelism are some of the techniques used today to cope wit h I/O latency and system scalability to support more users. This paper describes the two major contributions of our ongo ing research on distributed data access. The first contribution is the design of the Stora ge Embedded Networks (SEN) architecture that aims to improve user response times and sc alability on the Internet by better distribution of caches. SEN architecture is compose d of trusted routers embedded with volatile and non–volatile storage that snoop bypassin g objects for caching. Requests are checked by every hop, thus ensuring the transmission of t he closest copy on the data path and load reduction at the upstream. The two main control over heads of other architectures, connection establishment and continuous cache communicat ions, do not exist in SEN. The second contribution is the design of adaptive caching sc hemes using multiple experts, called ACME, that manage the SEN caches and further im prove the hit rates over static caching techniques. Machine learning algorithms ar e used to rate and select the current best policies or mixtures of policies via weight update s based on their recent success. Each adaptive cache node can tune itself based on the workloa d it bserves. Since no cache databases or synchronization messages are exchanged for ad aptivity, the clusters composed of these nodes will be exceedingly scalable and manageable. We propose to extend our preliminary designs and analysis in two directions. The first is to compare the Storage Embedded Networks (SEN) with the ex isting hierarchical and distributed cache clusters in terms of user response times, network bandwidth usage, server load reductions and scalability. For this part we will run la rge scale simulations over realistic topologies using real web proxy, file system and raw dis k traces. We will start these comparisons with static caching techniques. In the second p art we will introduce the adaptive caching techniques to eliminate manual tuning and manu al topological placement of static caches. We will measure the performance improvement s gained by using the adaptive techniques and probe the performance limits by theoret ical optimal algorithms. We will also quantify the time and space complexities of our schemes . R al system implementations will help us optimize our designs.
Rational choice and the structure of the environment.

A growing interest in decision making in psychology is evidenced by the recent publication of Edwards’ review article in the Psychological Bulletin (1) and the Santa Monica Conference volume, Decision Processes (7). In this work, much attention has been focused on the characterization of rational choice, and because the latter topic has been a central concern in economics, the theory of decision making has become a natural meeting ground for psychological and economic theory. A comparative examination of the models of adaptive behavior employed in psychology (e.g., learning theories), and of the models of rational behavior employed in economics, shows that in almost all respects the latter postulate a much greater complexity in the choice mechanisms, and a much larger capacity in the organism for obtaining information and performing computations, than do the former. Moreover, in the limited range of situations where the predictions of the two theories have been compared (see [7, Ch. 9, 10, 18]), the learning theories appear to account for the observed behavior rather better than do the theories of rational behavior. Both from these scanty data and from an examination of the postulates of the economic models it appears probable that, however adaptive the behavior of organisms in learning and choice situations, this adaptiveness falls far short of the ideal of “maximizing” postulated in economic theory. Evidently, organisms adapt well enough to “satisfice”; they do not, in general, “optimize. ” If this is the case, a great deal can be learned about rational decision making by taking into account, at the outset, the limitations upon the capacities and complexity of the organism, and by taking account of the fact that the environments to which it must adapt possess properties that permit further simplication of its choice mechanisms. It may be useful, therefore, to ask: How simple a set of choice mechanisms can we postulate and still obtain the gross features of observed adaptive choice behavior? In a previous paper (6) I have put forth some suggestions as to the kinds of “approximate” rationality that might be employed by an organism possessing limited information and limited computational facilities. The suggestions were “hypothetical” in that, lacking definitive knowledge of the human decisional processes, we can only conjecture on the basis of our everyday experiences, our introspection, and a very limited body of psychological literature what these
Perceptions of the accountant held by business students and business professionals in an emerging country: The case of Qatar

Purpose – The purpose of this paper is to investigate how business students and business professionals in Qatar perceive the accountancy profession.Design/methodology/approach – The paper designs a survey based on Osgood semantic differential theory of attitude assessment (30 bipolar pairs). The survey is administrated to 400 respondents. Factor analysis reduces variables to five and analysis of variance is employed to study the main differences between groups.Findings – Findings show that professionals and students, taken together, report a fairly positive image of the accountancy profession. However, students' perceptions are more positive compared to professionals. Among students, other demographics affect how students view the accountancy profession.Research limitations/implications – The sample is limited. Larger samples would confirm or otherwise the findings of this study. Future research could enlarge the scope by including other actors such as educators. Also, exploring other Arab countries would.
The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning

Machine learning is usually defined in behaviourist terms, where external validation is the primary mechanism of learning. In this paper, I argue for a more holistic interpretation in which finding more probable, efficient and abstract representations is as central to learning as performance. In other words, machine learning should be extended with strategies to reason over its own learning process, leading to so-called meta-cognitive machine learning. As such, the de facto definition of machine learning should be reformulated in these intrinsically multi-objective terms, taking into account not only the task performance but also internal learning objectives. To this end, we suggest a "model entropy function" to be defined that quantifies the efficiency of the internal learning processes. It is conjured that the minimization of this model entropy leads to concept formation. Besides philosophical aspects, some initial illustrations are included to support the claims.
Single-Tube Nested PCR for Detection of Tritrichomonasfoetus in Feline Feces

ABSTRACT Tritrichomonas foetus, a venereal pathogen of cattle, was recently identified as an inhabitant of the large intestine in young domestic cats with chronic diarrhea. Recognition of the infection in cats has been mired by unfamiliarity with T. foetus in cats as well as misdiagnosis of the organisms as Pentatrichomonashominis or Giardia sp. when visualized by light microscopy. The diagnosis of T. foetus presently depends on the demonstration of live organisms by direct microscopic examination of fresh feces or by fecal culturing. As T. foetus organisms are fastidious and fragile, routine flotation techniques and delayed examination and refrigeration of feces are anticipated to preclude the diagnosis in numerous cases. The objective of this study was to develop a sensitive and specific PCR test for the diagnosis of feline T. foetus infection. A single-tube nested PCR was designed and optimized for the detection of T. foetus in feline feces by using a combination of novel (TFITS-F and TFITS-R) and previously described (TFR3 and TFR4) primers. The PCR is based on the amplification of a conserved portion of the T. foetus internal transcribed spacer (ITS) region (ITS1 and ITS2) and the 5.8S rRNA gene. The absolute detection limit of the single-tube nested PCR was 1 organism, while the practical detection limit was 10 organisms per 200 mg of feces. Specificity was examined by using P. hominis, Giardialamblia, and feline genomic DNA. Our results demonstrate that the single-tube nested PCR is ideally suited for (i) diagnostic testing of feline fecal samples that are found negative by direct microscopy and culturing and (ii) definitive identification of microscopically observable or cultivated organisms.
La escala Cisneros como herramienta de valoración del mobbing

Cisneros scale to assess psychological harassment or mobbing at work. The Cisneros scale is a selfmanaged questionnaire developed by Inaki Pinuel that contains 43 items in a seven-point Likert response scale. The range is from never (0) to daily (6). Items deal with 43 different behaviours related to bullying, harassment and emotional abuse in the workplace. The scale forms part of a more extensive instrument, the CISNEROS Barometer ™ (Cuestionario Individual sobre PSicoterror, Negacion, Estigmatizacion y Rechazo en Organizaciones Sociales; Individual Questionnaire on Psychoterror, Mobbing, Stigmatization and Rejection in Social Organizations), which includes 3 scales for exploring periodically the content and development of violence and harassment in the workplace in Spain. In this paper we analyze the reliability, validity and dimensionality of the Cisneros scale using data from a sample of 1.303 workers from different sectors. Results show that Cisneros has very high reliability (0.96) and a bidimensional structure that includes «Locus of bullying behaviour» and «Type of bullying behaviour», and that it presents expected and statistically significant correlations with scales measuring Self-esteem, Burnout, Depression, Intention to leave, and Post-traumatic Stress. It is concluded that Cisneros is capable of discriminating between different levels of bullying and harassment at work and can be considered a valuable tool to examine and establish the frequency and damage of mobbing at organizational or individual level.
Early Detection of Brain Pathology Suggestive of Early AD Using Objective Evaluation of FDG-PET Scans

The need for early detection of AD becomes critical as disease-modifying agents near the marketplace. Here, we present results from a study focused on improvement in detection of metabolic deficits related to neurodegenerative changes consistent with possible early AD with statistical evaluation of FDG-PET brain images. We followed 31 subjects at high risk or diagnosed with MCI/AD for 3 years. 15 met criteria for diagnosis of MCI, and five met criteria for AD. FDG-PET scans were completed at initiation and termination of the study. PET scans were read clinically and also evaluated objectively using Statistical Parametric Mapping (SPM). Using standard clinical evaluation of the FDG-PET scans, 11 subjects were detected, while 18 were detected using SPM evaluation. These preliminary results indicate that objective analyses may improve detection; however, early detection in at-risk normal subjects remains tentative. Several FDA-approved software packages are available that use objective analyses, thus the capacity exists for wider use of this method for MCI/AD.
Binary tree of SVM: a new fast multiclass training and classification algorithm

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N-1 binary classifiers in the best situation (N is the number of classes), while it has log4/3((N+3)/4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number
A new class of mosaic color encoding patterns for single-chip cameras

A simple method for generating an infinite number of different mosaic color-encoding patterns by variation of a few parameters is described, In a particular limit they approach a special type of random color mosaic. A universal decoding scheme suitable for computer optimization has been developed for evaluating these patterns. Optimal patterns for use with frame-transfer CCD's are discussed in detail.
Recursive MUSIC: A framework for EEG and MEG source localization

The multiple signal classification (MUSIC) algorithm can be used to locate multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetocncephalography (MEG) data. The algorithm scans a single-dipole model through a three-dimensional (3-D) head volume and computes projections onto an estimated signal subspace. To locate the sources, the user must search the head volume for multiple local peaks in the projection metric. This task is time consuming and subjective. Here, the authors describe an extension of this approach which they refer to as recursive MUSIC (R-MUSIC). This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections. The new method is also able to locate synchronous sources through the use of a spatio-temporal independent topographies (IT) model. This model defines a source as one or more nonrotating dipoles with a single time course. Within this framework, the authors are able to locate fixed, rotating, and synchronous dipoles. The recursive subspace projection procedure that they introduce here uses the metric of canonical or subspace correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace, by recursively computing subspace correlations, the authors build up a model for the sources which account for a given set of data. They demonstrate here how R-MUSIC can easily extract multiple asynchronous dipolar sources that are difficult to find using the original MUSIC scan. The authors then demonstrate R-MUSIC applied to the more general IT model and show results for combinations of fixed, rotating, and synchronous dipoles.
Finding Related Pages Using Green Measures: An Illustration with Wikipedia

We introduce a new method for finding nodes semantically related to a given node in a hyperlinked graph: the Green method, based on a classical Markov chain tool. It is generic, adjustment-free and easy to implement. We test it in the case of the hyperlink structure of the English version of Wikipedia, the on-line encyclopedia. We present an extensive comparative study of the performance of our method versus several other classical methods in the case of Wikipedia. The Green method is found to have both the best average results and the best robustness.
An adaptive P300 model for controlling a humanoid robot with mind

This paper presents a P300 model for controlling a humanoid robot with mind, including an off-line phase with a fixed trial number for training the model and an on-line phase with an adaptive strategy for generating commands to the humanoid robot. Our control scheme includes a procedure of acquiring P300 signals, topographical distribution analysis of P300 signals, and a classification approach to identifying subjects' mental activities regarding robot-walking behavior. Our study shows that the adaptive model is fast and practical to control humanoid robot via brainwaves.
Omega-3 polyunsaturated fatty acid profiles and relationship with cardiometabolic risk factors in Cree (Eeyouch) of Northern Québec

Background n-3 long-chain polyunsaturated fatty acids (LC-PUFAs) from fish are known modulators of cardiometabolic risk factors. Objective To examine fatty acids (FAs) status and the relationship between n-3 LC-PUFA and cardiometabolic risk factors in Cree participants. Design We analyzed data from a cross-sectional study (n=829) conducted in Cree adults (aged 18–74 years) from 7 communities of the James Bay territory of Quebec (Canada) in 2005–2009. Sociodemographic, lifestyle, clinical and anthropometric data were collected. FAs were quantified in red blood cells (RBCs) under fasting conditions. Results A total of 89% of the participants were overweight (with 69% obesity), 33% had hypertriglyceridemia, 44% had low plasma HDL-c and 77% had fasting plasma insulin ≥90 pmol/l. Total n-3 PUFAs accounted for 6% of total FAs and were higher among older participants, while n-6 PUFAs accounted for 31% of total FAs and were higher among younger participants. According to the adjusted multiple linear regression models, n-3 LC-PUFA was associated (p<0.05) with higher total cholesterol, LDL-c and apo B-100, and was also associated (p<0.05) with lower blood glucose. Conclusion Overall, this study showed that n-3 LC-PUFA levels measured in the RBCs of the Cree adults are relatively low and tend towards lower levels among youth. These levels might be insufficient to offset the prevalence of cardiometabolic risk factors.
Minimum-Weight Spanning Tree Construction in O(log log n) Communication Rounds

We consider a simple model for overlay networks, where all n processes are connected to all other processes, and each message contains at most O(log n) bits. For this model, we present a distributed algorithm which constructs a minimum-weight spanning tree in O(log log n) communication rounds, where in each round any process can send a message to every other process. If message size is $\Theta(n^\epsilon)$ for some $\epsilon>0$, then the number of communication rounds is $O(\log{1\over\epsilon})$.