Nygaardsosa4508

The objective of this work was to verify the efficacy of a treatment based on myofunctional therapy techniques which aimed to improve the tongue strength, precision, and speed of a ten-year-old girl with nemaline myopathy (NM) and the repercussions of this therapy on her speech intelligibility. NM is a rare congenital muscle disorder that causes extreme muscle weakness, especially in the face and neck, as well as severe dysarthria and dysphagia, although this does not affect the nervous system or cognitive development.

This was a single-subject experimental study which used an interrupted pre- and post-treatment time-series design, and which applied autoregressive integrated moving-average predictive models and Holt exponential smoothing. During the treatment phases, the participant's tongue strength and the rate of speech diadochokinesia and voluntary lingual movements were estimated and the repercussions of the intervention in terms of speech intelligibility were ascertained via an experiment with 'naïvbility of her speech and communication.

NM and other rare primary muscle disorders allow us to estimate the effects of severe muscle weakness in people with dysarthria without cognitive impairment or alterations in central nervous system, peripheral nervous system or in gap junction. In this case, the treatment did not increase the patient's lingual and articulatory movement speed but did increase her tongue strength from 5 % to 10 % of the levels otherwise expected for her age and significantly improved the intelligibility of her speech and communication.Microplastics (MPs) are becoming an environmental growing concern, being the sewage sludge applied to agriculture fields one of the most important inputs to the environment. To date, there is no standardized protocol for their extraction and changes in vegetative growth and fruit maturation on cultivated plants induced by sludge containing MPs have not been studied yet. Sewage sludge from three different wastewater treatment plants located in Murcia, Spain, were studied. First, the microplastic concentration was estimated and, then, the effects of the sewage sludge in the development of tomato plants and fruit production was analyzed. The measured parameters in tomato plants were both, biomass and length, for shoot and root part, as well as, stem diameter and tomato production. The present work has developed and validated a protocol for the extraction and quantification of MPs comprising several shapes, materials and sizes from samples of sewage sludges, which offers a good compromise for the extraction of different types of microplastic. The protocol used for MPs extraction had a recovery efficiency of 80 ± 3% (mean ± SE) and used bicarbonate, to maximize MPs extraction. The mean abundance of MPs in the studied sewage sludge samples was 30,940 ± 8589 particles kg-1 dry weight. Soils with sludge containing MPs fostered the growth of tomato plants, while delaying and diminished fruit production. However, other factors or their interactions with MPs could have influenced the outcomes. Further studies are necessary to corroborate these findings and explain the mechanisms of possible effects of MPs on plants.Toxoplasmosis is one of the common chronic infections caused by the parasite Toxoplasma gondii. Even though its infection in healthy non-pregnant women is self-limited and largely asymptomatic, the main concern is the risk to the fetus by vertical transmission in pregnancy. Congenital toxoplasmosis can result in permanent neurological damage and even serious morbidity such as blindness. Screening programs are implemented in various countries depending on the prevalence and virulence of the parasite in the respective regions. Upon diagnosis of infection, appropriate antibiotic therapy should be initiated as it has been proven to reduce the risk of fetal transmission. Primary prevention remains the key intervention to avoid the infection and hence patient education is an important aspect of the management.The overall numbers of precancerous lesions are expected to fall as human papillomavirus (HPV) vaccinated women enter the cervical screening programme. Juxtaposed against an increase in referrals from the introduction of primary high-risk HPV screening, colposcopists expect to see a decreasing incidence of high-grade cervical intraepithelial neoplasia (CIN). Correct identification of lesions will become more challenging, as the prevalence of high-grade lesions becomes minimal and conventional colposcopy is subject to a lower sensitivity. In this review, we explore the scenarios where adjunct technologies could support colposcopists to manage referrals and diagnose treatable lesions with more confidence.What kind of memory representations do word learners use when they learn the meaning of words cross-situationally? This study leverages the measure of the relationship between confidence and performance to explore the nature of memory representations in word learning. In the recognition memory literature, studies have shown that explicit memory can be used when subjects can semantically encode the study material. However, when the study material is chosen to be unverbalizable, implicit memory is used but is presumed to be only detectable under certain experimental conditions. In the current paper, five cross-situational word learning experiments manipulated the type of word referents with varying experimental paradigms that were designed to probe different types of memory under an implicit learning paradigm. When word referents were line drawings of familiar concepts, memory in cross situational learning was explicit. Implicit memory was found where referents were objects that cannot be encoded semantically (e.g., unverbalizable images). These findings have implications for different theoretical perspectives on early word learning, which differ in the extent to which existing semantic category information, as opposed to perceptual information, contributes to the word meaning process.In this work we present a technique to deal with one of the biggest problems for the application of convolutional neural networks (CNNs) in the area of computer assisted endoscopic image diagnosis, the insufficient amount of training data. Based on patches from endoscopic images of colonic polyps with given label information, our proposed technique acquires additional (labeled) training data by tracking the area shown in the patches through the corresponding endoscopic videos and by extracting additional image patches from frames of these areas. So similar to the widely used augmentation strategies, additional training data is produced by adding images with different orientations, scales and points of view than the original images. However, contrary to augmentation techniques, we do not artificially produce image data but use real image data from videos under different image recording conditions (different viewpoints and image qualities). By means of our proposed method and by filtering out all extracted images with insufficient image quality, we are able to increase the amount of labeled image data by factor 39. We will show that our proposed method clearly and continuously improves the performance of CNNs.Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis. In this work, we evaluate the use of conditional Generative Adversarial Networks (cGANs) as a robust and potentially improved method for semantic segmentation compared to other extensively used convolutional neural network, such as the U-Net. see more As cGANs have not yet been widely explored for semantic medical image segmentation, we analysed the effect of training with different objective functions and discriminator receptive field sizes on the segmentation performance of the cGAN. Additionally, we evaluated the possibility of using transfer learning to improve the segmentation accuracy. The networks were trained on i) the SKI10 dataset which comes from the MICCAI grand challenge "Segmentation of Knee Images 2010″, ii) the OAI ZIB dataset containing femoral and tibial bone and cartilage segmentations of the Osttuned dataset, but also increased the network's capacity to preserve segmentation capabilities for the pretrained dataset. cGAN machine learning can generate automated semantic maps of multiple tissues within the knee joint which could increase the accuracy and efficiency for evaluating joint health.The structure of the O-antigen from Escherichia coli reference strain O188 (E. coli O188H10) has been investigated. The lipopolysaccharide shows a typical nonrandom modal chain-length distribution and the sugar and absolute configuration analysis revealed d-Man, d-Glc, d-GlcN and d-GlcA as major components. The structure of the O-specific polysaccharide was determined using one- and two-dimensional 1H and 13C NMR spectroscopy experiments, where inter-residue correlations were identified by 1H,13C-heteronuclear multiple-bond correlation and 1H,1H-NOESY experiments, which revealed that it consists of pentasaccharide repeating units with the following structure Biosynthetic aspects and NMR analysis are consistent with the presented structure as the biological repeating unit. The O-antigen of Shigella boydii type 16 differs only in that it carries O-acetyl groups to ~50% at O6 of the branch-point mannose residues. A molecular model of the E. coli O188 O-antigen containing 20 repeating units extends ~100 Å, which is similar to the height of the periplasmic portion of polysaccharide co-polymerase Wzz proteins that regulate the O-antigen chain length of lipopolysaccharides in the Wzx/Wzy biosynthetic pathway.

Currently, the diagnosis of neural antibody-mediated epilepsy/seizure (NAME/S)relies heavily on neural antibody testing, which is time-consuming, costly and introduces diagnostic delays. A statistical tool to predict the probability of a patient with NAME/S is lacking. We aimed to construct a predictive model to help clinicians expedite the diagnostic process.

We retrospectively recruited subjects (206 in the development group and 62 in the validation group) with new-onset seizures or established epilepsy suspected to have presented with antibody-mediated seizures between January 2014 and December 2019. We collected data about demographics, medical history, clinical manifestations and follow up. Binary logistic regression was used to select potential predictors for the construction of a predictive model. Five-fold cross and bootstrap validation were applied to avoid overfitting. Concordance index, calibration plots and decision curve analysis were used to assess its performance.

The model, incorporatingume and selection bias in subject enrolment, this model may be used to estimate the individualized probability of having NAME/S, deserving further exploration and validation.

To compare epilepsy-related injuries in untreated or inadequately treated patients and patients on adequate treatment.

In a cross-sectional case-control study, seizure-related injuries in patients who were either on no treatment or inadequate treatment were compared with another group of patients receiving appropriate evidence-based epilepsy treatment. The inadequately treated patients or 'cases' were drawn from an outreach epilepsy clinic while the adequately treated patients or 'controls' were recruited from a tertiary care facility providing comprehensive epilepsy management.

The odds of injury were eight times higher in inadequately treated patients or cases compared to the adequately treated patients or controls. After adjusting for gender, epilepsy duration, seizure frequency, current medication, and number of AEDs, the odds of injury were 15. 8 times higher in the cases. Major injuries such as burns, fractures, and tooth injuries were also higher in the cases.

Untreated or inadequately treated epilepsy patients have a significantly higher risk of injuries.