Miracle-dz/UniFAR-FTUs huggingface dataset:
Intervenções para elevar a adesão dos profissionais de saúde à higiene de mãos: revisão integrativa

The objective of this study was to identify the main strategies used to improve the compliance of health care professionals to hand washing. This is an integrative literature review, which search included journals in English, Spanish and Portuguese. Twenty—three articles were included. An electronic tool was developed on Microsoft Office Excel and the main results were submitted to descriptive analysis. Of the total studies, 87.1% had before and after designs and several methods were used to monitor compliance rate (direct observation, supply use and self-reported rates). Multimodal interventions were used in 87.0%, and the most often employed were: education, feedback, alcohol being available and posters. The largest challenge identified was not only improving the compliance rates to hand washing, but, most of all, keeping them high. It was observed there is a need to use multimodal strategies that contribute to behavior change considering the local setting. Descriptors: Hand Disinfection; Health Personnel; Cross Infection; Nursing.
Epileptic kinetopsia localizes to superior parietal lobule and intraparietal sulcus

Visual hallucinations are common symptoms of seizures affecting primary and association cortices, and can provide vital information about the ictal onset zone. Epileptic kinetopsia, defined as illusionary movement of stationary objects in the visual field, was reported in a patient with a tumor in the temporal-parietal-occipital (TPO) junction. Intracranial stimulation of TPO junction did not evoke kinetopsia and the site of onset of this phenomenon is unknown.1 We describe a patient with ictal kinetopsia whose seizure onset zone was localized with intracranial EEG.
Structure and Healing Capacity of the Rat Tympanic Membrane After Eustachian Tube Occlusion

Obstruction of the Eustachian tube in rats results in an accumulation of serous fluid in the middle ear cavity. the fluid was first detected in the attic, and subsequently the whole cavity became filled. However, when a perforation of the tympanic membrane was performed in connection with experimental tube obstruction, the mesotympanon was found free from fluid, but not the attic, as long as the perforation remained open. After 9-11 days of obstruction the pars flaccida was found to be maximally retracted, forming a non-adhesive clean retraction pocket. in addition, marked ultrastructural changes were found to have taken place in the pars flaccida following tube obstruction. Finally, in spite of the occluded Eustachian tube, traumatic tensa perforation was found to heal rapidly.
Peptide Bond Formations through Flow Chemistry

Ple a s e no t e: Ch a n g e s m a d e a s a r e s ul t of p u blishing p roc e s s e s s uc h a s copy-e di ting, for m a t ting a n d p a g e n u m b e r s m ay no t b e r eflec t e d in t his ve r sion. For t h e d efini tive ve r sion of t his p u blica tion, ple a s e r ef e r to t h e p u blish e d sou rc e. You a r e a dvise d to cons ul t t h e p u blish e r’s ve r sion if you wish to ci t e t his p a p er.
Neural Networks, Principal Components, and Subspaces

A single neuron with Hebbian-type learning for the connection weights, and with nonlinear internal feedback, has been shown to extract the statistical principal components of its stationary input pattern sequence. A generalization of this model to a layer of neuron units is given, called the Subspace Network, which yields a multi-dimensional, principal component subspace. This can be used as an associative memory for the input vectors or as a module in nonsupervised learning of data clusters in the input space. It is also able to realize a powerful pattern classifier based on projections on class subspaces. Some classification results for natural textures are given.
Should genetic testing for SCAs be included in the diagnostic workup for MSA?

Objective: To examine the prevalence of mutations in spinocerebellar ataxia (SCA) genes in patients who were clinically diagnosed with multiple system atrophy (MSA). Methods: Genetic tests for SCA were performed in 302 of 528 patients who met the diagnostic criteria for MSA based on clinical features. Generally, when a patient had cerebellar symptoms or cerebellar atrophy on neuroimaging, genetic tests for SCA types 1, 2, 3, 6, 7, and 17, and dentatorubropallidoluysian atrophy were done, and when a patient had parkinsonism without cerebellar symptoms, genetic tests for SCA types 2, 3, and 17 were done. Results: Mutations in SCA genes were found in 22 of the 302 patients (7.3%) with SCA17 comprising more than half of the mutation-positive cases. The age at disease onset in these 22 patients was not different compared with the 280 patients without mutations (55.9 ± 9.3 vs 59.2 ± 8.9, p = 0.102). All patients had urinary symptoms, and 10 patients also had orthostatic dizziness or orthostatic hypotension. A family history was reported in only 3 patients. Of note, dream enactment behavior suggesting REM sleep behavior disorder was reported in 9 of the 11 patients (81.8%) asked. Conclusions: The high proportion of patients with SCA mutations in this study indicates that genetic testing for SCA should be included for patients with MSA, especially for patients with cerebellar dysfunctions.
Learning with Delayed Reinforcement Through Attention-Driven Buffering

Learning with delayed reinforcement refers to situations where the reinforcement to a learning system occurs only at the end of a string of actions or outputs, and it must then be assigned back to the relevant actions. A method for accomplishing this is presented which buffers a small number of past actions based on the unpredictability of or attention to each as it occurs. This approach allows for the buffer size to be small, and yet learning can reach indefinitely far back into the past; it also allows the system to learn when reinforcement is not only delayed but also reinforcements from other unrelated actions may arrive during this delay. An example of a simulated food-finding creature is used to show the system at work in a predictive application where reinforcements show this interleaving behaviour.
Analog Cmos Neural Circuits - In Situ Learning

This paper presents a tutorial review of artificial neurons and synapses which are implemented as analog CMOS circuits and which employ in situ learning of the synaptic weights. In situ learning implies that circuits local to the synapses perform computation of the weight updates according to built-in learning rules. This makes these synapses less flexible than those with external learning which do not commit to predetermined rules. On the other hand, in situ learning has potential advantages in increased learning rates in compensating for component inaccuracies and in adapting to nonstationary tasks. Both supervised and unsupervised learning rules and their implementations are described. The discussion focusses on synapses whose weights are stored on capacitors or in binary registers but also includes EEPROMs and CCDs. The emphasis is on synapses implemented using analog multipliers but other circuit techniques such as pulse streams are also briefly mentioned.
Entropy Minimization: a Design Principle for Sensory Perception?

We speculate on how computational strategies suggested by Information theory could be useful for knowledge acquisition in sensory systems. The focus of our exploration is the idea that perception is a data reduction problem and as such sensory transformations should be predictable from the data reduction problem and as such sensory transformations should be predictable terms of variational principles involving the minimization of two types of entropy. The analysis suggests a scheme in which the two can be combined to produce a predictive design principle for sensory pathways.
Recognition of telugu characters using neural networks

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.
An intervention to preschool children for reducing screen time: a randomized controlled trial.

BACKGROUND Screen time, defined as time spent watching television, DVDs, or videos or playing computer or video games, has been related to serious health consequences in children, such as impaired language acquisition, violent behaviour, tobacco smoking and obesity. Our aim was to determine if a simple intervention aimed at preschool-aged children, applied at the health maintenance visits, in the primary care setting, would be effective in reducing screen time. METHODS We used a two group randomized controlled trial design. Two- to 6-year-old children and their parents were randomly assigned to receive an intervention to reduce their screen time, BMI and parental report of aggressive behaviour. At the end of the intervention we made home visits at 2, 6 and 9 months and the parents completed questionnaire. RESULTS Parents in the intervention group reported less screen time and less aggressive behaviour than those in the control group but there were no differences in BMI z scores. CONCLUSIONS This study shows that a preschool-based intervention can lead to reductions in young children's television/video viewing.
Functional imaging to uncover willful brain behavior in noncommunicative patients

Functional neuroimaging probes the regional distribution of neuronal activity, opening a window on brain dysfunction. This opportunity is particularly suited to the evaluation of disorders of consciousness (DOC), which are not reliably assessed by clinical observation and behavioral testing. The article by Monti et al.1 in this issue of Neurology® illustrates this potential; it reports the application of fMRI to uncover signs of high-level cognitive processes in a sample of 28 patients. The authors employ a simple target detection task that requires both selective and sustained attention to be completed accurately in healthy participants (from whom subjective conscious reports can validate performance). Patients were instructed to count the number of times a target word was presented. The target words chosen were neutral (by standardized criteria for English language words), and individualized and randomized for each patient, eliminating any systematic influence of the word itself outside of a contingent relationship to the spoken instruction, “count. ” The counting condition was compared to a passive listening condition.
Thermodynamics Energy for both Supervised and Unsupervised Learning Neural Nets at a Constant Temperature

Unified Lyaponov function is given for the first time to prove the learning methodologies convergence of artificial neural network (ANN), both supervised and unsupervised, from the viewpoint of the minimization of the Helmholtz free energy at the constant temperature. Early in 1982, Hopfield has proven the supervised learning by the energy minimization principle. Recently in 1996, Bell & Sejnowski has algorithmically demonstrated Independent Component Analyses (ICA) generalizing the Principal Component Analyses (PCA) that the continuing reduction of early vision redundancy happens towards the "sparse edge maps" by maximization of the ANN output entropy. We explore the combination of both as Lyaponov function of which the proven convergence gives both learning methodologies. The unification is possible because of the thermodynamics Helmholtz free energy at a constant temperature. The blind de-mixing condition for more than two objects using two sensor measurement. We design two smart cameras with short term working memory to do better image de-mixing of more than two objects. We consider channel communication application that we can efficiently mix four images using matrices [AO] and [Al] to send through two channels.
Knowledge Extraction: A Comparison between Symbolic and Connectionist Methods

The use of a linguistic representation for expressing knowledge acquired by learning systems is an important issue as regards to user understanding. Under this assumption, and to make sure that these systems will be welcome and used, several techniques have been developed by the artificial intelligence community, under both the symbolic and the connectionist approaches. This work discusses and investigates three knowledge extraction techniques based on these approaches. The first two techniques, the C4.5 and CN2 symbolic learning algorithms, extract knowledge directly from the data set. The last technique, the TREPAN algorithm extracts knowledge from a previously trained neural network. The CN2 algorithm induces if.then rules from a given data set. The C4.5 algorithm extracts decision trees, although it can also extract ordered rules, from the data set. Decision trees are also the knowledge representation used by the TREPAN algorithm.
Subclinical cardiac dysfunction increases the risk of stroke and dementia

Objective: To investigate the association between cardiac function and the risk of stroke and dementia in elderly free of clinical cardiac disease. Additionally, we investigated the relation between cardiac function and MRI markers of subclinical cerebrovascular disease. Methods: This study was conducted within the population-based Rotterdam Study. A total of 3,291 participants (60.8% female, age-range 58–98 years) free of coronary heart disease, heart failure, atrial fibrillation, stroke, and dementia underwent echocardiography in 2002–2005 to measure cardiac function. Follow-up finished in 2012. In 2005–2006, a random subset of 577 stroke-free people without dementia underwent brain MRI on which infarcts and white matter lesion volume were assessed. Results: During 21,785 person-years of follow-up, 164 people had a stroke and during 19,462 person-years of follow-up, 208 people developed dementia. Measures of better diastolic function, such as higher E/A ratio, were associated with a lower risk of stroke (hazard ratio [HR] 0.82; 95% confidence interval [CI] 0.69; 0.98) and dementia (HR 0.82; 95% CI 0.70; 0.96). Better systolic function, measured as higher fractional shortening, was only associated with a lower risk of stroke (HR 0.84; 95% CI 0.72; 0.98). Better diastolic function was related to a lower prevalence of silent infarcts on MRI, especially lacunar infarcts. Conclusions: In elderly free of clinical cardiac disease, worse diastolic function is associated with clinical stroke, dementia, and silent infarcts on MRI, whereas worse systolic function is related only to clinical stroke. These findings can form the basis for future research on the utility of cardiac function as potential intervention target for prevention of neurologic diseases.
Dynamic Load Balancers for a Multithreaded Multiprocessor System

Designing multi-processor systems that deliver a reasonable price-performance ratio using off-the-shelf processor and compiler technologies is a major challenge. For an important class of applications, it is critical to explore fine-grain parallelism to achieve reasonable performance. In such parallel systems it is essential to efficiently manage communication latencies, bandwidth, and synchronization overheads. In this paper we study load balancing strategies for the runtime system of a multi-threaded system. EARTH (Efficient Architecture for Running Threads) is a multi-threaded programming and execution model that supports fine-grain, non-preemptive, threads in a distributed memory environment. We describe the design and implementation of a set of dynamic load balancing algorithms, and study their performance in divide-and-conquer, regular, and irregular applications. Our experimental study on the distributed memory multi-processor IBP SP-2 indicate that a randomized load balancer perform as well as, and often better than, history based load balancers.
A Graph Theoretical Model for Scheduling Simultaneous I/O Operations on Parallel and Distributed Environments

The motivation for the research presented here is to develop an approach for scheduling I/O operations in distributed/parallel computer systems. First, a general model for specifying the parallel I/O scheduling problem is developed. The model defines the I/O bandwidth for different parallel/distributed architectures. Then the model is used to establish an algorithm for scheduling I/O operations on these architectures.
OC198: Should an ectopic pregnancy always be diagnosed using transvaginal ultrasonography in the first trimester prior to surgery?

16/37 (43%) had a normal scan with mobile ovaries and no pelvic tenderness. 8/37 (22%) patients had a normal scan with no abnormality but tenderness. 5/37 (14%) had an ovarian cyst rupture. 8/37 had a normal scan with a non-gynaecological diagnosis. 27/37 (73%) of patients returned the follow up questionnaire. 22/35 (63%) of the patients had satisfactory resolution of their symptoms. Conclusion: The majority of patients attending an acute gynaecology unit with acute pelvic pain will have no gross pathology. In the majority of patients the symptoms will resolve satisfactorily within 7 days.
Self‐efficacy, coping, and difficulties interacting with health care professionals among women living with breast cancer in rural communities

Social support is frequently cited as having a positive influence on cancer outcomes. Understanding the impact of social and geographical isolation is therefore critical to providing optimal standards of care to rural communities. Collie et al. have explored the psychological strategies used by women in rural communities and their potential impact on interactions with health professionals. The authors argue that ‘self-distraction is frequently regarded as a maladaptive coping strategy with regard to chronic illness’ (p. 18) and one of their hypotheses is that women who use this strategy may perceive medical encounters as problematic. I found this hypothesis difficult to justify. The relations among distraction techniques, health care interactions and other cancer outcomes are complex. While there is limited data on the impact of self-distraction directly on medical communication, there is a lot of data suggesting that closely related concepts, such as denial (Hackett and Cassem, 1974; Scheier and Carver, 1992) and minimisation (Butow et al., 1999, 2000) are associated with better disease outcomes. In situations where people have control over events around them denial (and possibly selfdistraction) is often not adaptive; but in situations where there is nothing the patient can do, they may be positive, palliative adaptations. This has been demonstrated in studies of male patients in coronary care units where deniers had shorter admissions, used fewer analgesics, were discharged home more rapidly and got back into the work force more quickly. However, the deniers also tended to deny that lifestyle factors like high cholesterol, poor diet, inadequate exercise, obesity and smoking were the factors that put them into hospital with cardiac problems in the first place. In a longitudinal study of women with metastatic breast cancer, and men and women with metastatic melanoma, Butow et al. (1999, 2000) found that minimisation was associated with significantly longer survival. Minimisation involves elements of self-distraction. If patients use denial as a protective coping mechanism then intervening to rectify self-distraction may be undesirable. Indeed, to do so may undermine the therapeutic role that optimism is widely assumed to play in fostering psychological and physical health (Scheier and Carver, 1992). The critical point is that psychological strategies are associated with cancer outcomes at a variety of levels beyond health care interactions. Studies of medical communication need to recognise this broader context. The inclusion criterion chosen by Collie et al., namely women within 3 months of initial diagnosis or within 6 months of ending treatment, is unusual. Available data suggest that psychosocial needs and issues change through the course of cancer treatment, as do coping strategies, and it is
Quality improvement in neurology

Epilepsy is a common, debilitating, and costly disease. It is estimated that 2.2 million people in the United States are diagnosed with epilepsy, and 150,000 new cases of epilepsy are diagnosed in the United States annually.1 However, epilepsy prevalence might be underestimated due to numerous social issues that accompany a diagnosis of epilepsy.2 People with epilepsy have poorer overall health status, impaired intellectual and physical functioning, and a greater risk for accidents and injuries.1–3 It is estimated that the annual direct medical cost of epilepsy in the United States is $9.6 billion, and this estimate does not include indirect costs from losses in quality of life or productivity.1
Eridis: Energy-Efficient Reservation Infrastructure for Large-Scale Distributed Systems

At the age of petascale machines, cloud computing and peer-to-peer systems, large-scale distributed systems need an ever-increasing amount of energy. These systems urgently require effective and scalable solutions to manage and limit their electrical consumption. As of now, most efforts are focused on energy-efficient hardware designs. Thus, the challenge is to coordinate all these low-level improvements at the middleware level to improve the energy efficiency of the overall systems. Resource-management solutions can indeed benefit from a broader view to pool the resources and to share them according to the needs of each user. In this paper, we propose ERIDIS, an Energy-efficient Reservation Infrastructure for large-scale DIstributed Systems. It provides a unified and generic framework to manage resources from Grids, Clouds and dedicated networks in an energy-efficient way.