Relationships Between Mathematics Performance and Attitude to Mathematics: Influences of Gender, Test Anxiety, and Working Memory.

Many studies have indicated that mathematics anxiety, and other negative attitudes and emotions toward mathematics, are pervasive and are associated with lower mathematical performance. Some previous research has suggested that working memory is related to both mathematics anxiety and mathematics. Moreover, both gender and chosen course of study (sciences vs. humanities) appeared likely to influence students' attitudes to mathematics. In the present study, 40 university undergraduates completed a battery of assessments investigating working memory, attitude to mathematics, test anxiety. and mental and written arithmetic. Attitudes to mathematics were significantly associated with the other variables: working memory, test anxiety, and both measures of mathematical performance. The other variables were not strongly associated with one another. There were no gender differences in mathematical performance, but females exhibited more negative attitudes to mathematics and higher test anxiety than males. After controlling for test anxiety, there ceased to be significant gender differences in attitudes to mathematics. Science students had more positive attitudes to mathematics than humanities students, but the groups did not differ in test anxiety, Science students were better at written but not mental arithmetic. They were also better at working memory, but this was not a significant covariate when the groups were compared on mathematical performance and attitudes to mathematics The results are discussed, with particular focus on implications for future research on influences on mathematics anxiety.

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders.

Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how a sample's constituent molecules are arranged in space. Each tissue section/component is defined by a unique combination of spatial and spectral features, but given the high dimensionality of HSI datasets, extracting and utilising them to segment images is non-trivial. Here, we show how networks based on deep convolutional autoencoders (CAEs) can perform this task in an end-to-end fashion by first detecting and compressing relevant features from patches of the HSI into low-dimensional latent vectors, and then performing a clustering step that groups patches containing similar spatio-spectral features together. We showcase the advantages of using this end-to-end spatio-spectral segmentation approach compared to i) the same spatio-spectral technique not trained in an end-to-end manner, and ii) a method that only utilises spectral features (spectral k-means) using simulated HSIs of porcine tissue as test examples. Secondly, we describe the potential advantages/limitations of using three different CAE architectures: a generic 2D CAE, a generic 3D CAE, and a 2D convolutional encoder-decoder architecture inspired by the recently proposed UwU-net that is specialised for extracting features from HSI data. We assess their performance on IR HSIs of real colon samples. We find that all architectures are capable of producing segmentations that show good correspondence with HE stained adjacent tissue slices used as approximate ground truths, indicating the robustness of the CAE-driven spatio-spectral clustering approach for segmenting biomedical HSI data. Additionally, we stress the need for more accurate ground truth information to enable a precise comparison of the advantages offered by each architecture.

Dietary supplements for consideration in elite female footballers.

The physical demands of professional female football have intensified in recent years. Supplements are only advised in addition to a healthy, balanced diet, but may warrant a greater prevalence in the professional game to support well-being, recovery and performance. Supplements used by players should be safe, legal and scientifically proven to be effective. An individual approach should be taken to using supplements dependant on the needs and goals of the player. Female players should aim to improve the frequency of protein intake throughout the day, whilst tailoring doses to individual body mass. Vitamin D supplementation is vital throughout the winter months in countries with limited sun exposure, however doses should be administered based on individual blood test results. Iron is likely to be important to the well-being of female athletes throughout the season, in particular during the menses. Omega-3 and collagen may be of greater benefit to female than male athletes during recovery from soft tissue injury, whilst probiotics and creatine are beneficial throughout the season for reducing risk of illness and optimising recovery, respectively. Ergogenic supplements for football include beta-alanine, nitrate and caffeine. Caution should be taken with caffeine use due to the varying tolerance of difference athletes and sleep impairments that can follow.

Liquid Biopsies in Lung Cancer: Four Emerging Technologies and Potential Clinical Applications.

Background: Liquid biopsies offer a promising alternative to tissue samples, providing non-invasive diagnostic approaches or serial monitoring of disease evolution. However, certain challenges remain, and the full potential of liquid biopsies has yet to be reached. Here we report several methodological approaches to interrogate liquid biopsies using circulating tumour cell (CTC) enumeration and characterisation, transcriptomics, Raman spectroscopy, and copy number instability (CNI) scores using blood samples of lung cancer (LC) patients. Methods: We choose LC; since it still is the most common cause of cancer-related mortality worldwide, and therefore there is a need for development of new non-invasive diagnostic/prognostic technologies. Changes in gene expression were assessed using RNA-seq, and in CTCs using ImageStream, an imaging flow-cytometer. CNI scores, from paired tissue/ctDNA were also explored. Raman spectroscopy was used to provide chemical fingerprints of plasma samples. Results: CTCs were detected in all LC patients (n = 10). We observed a significant increase in CTC levels in LC patients (n = 10) compared to controls (n = 21). A similar CNI was noted in the tissue and plasma of 2 patients, where higher CNI scores corresponded with poorer outcome. Significant changes in Raman spectra (carotenoid concentrations) were noted in LC patients (n = 20) compared to controls (n = 10). RNA-seq revealed differential expression of 21 genes between LC cases and controls in both LC tissue and blood samples. Conclusions: Liquid biopsies can potentially provide a more comprehensive picture of the disease compared to a single tissue biopsy. CTC enumeration is feasible and sensitive for LC patients. Molecular profiling of CTCs is also possible from total blood. CNI scores and Raman spectra require further investigation. Further work is being undertaken to explore these methods of detection in a larger LC cohort.

Determination of Depth in Transmission Raman Spectroscopy in Turbid Media Using a Beam Enhancing Element.

This study demonstrates experimentally a method to enable prediction of depth of a chemical species buried in a turbid medium by using transmission Raman spectroscopy alone. The method allows the prediction of the depth of a single, chemically distinct layer within a turbid matrix by performing two measurements, with and without a beam enhancing element, or "photon diode." The samples employed consisted of two different polymers, of total thickness 3.6 mm, whose optical properties are loosely relevant to pharmaceutical applications. A polymer layer of low-density polyethylene (LDPE) was placed at different positions within multiple layers of the polytetrafluoroethylene (PTFE) matrix and Raman spectra were recorded in each case. Both univariate and multivariate analyses were utilized to determine whether the depth of the LDPE layer could be predicted using the obtained data. The best-achieved RMSE of prediction was 4.2% of the total sample size (i.e., +/- 0.15 mm) with the multivariate approach.

Does Diet Matter? The Use of Polyunsaturated Fatty Acids (PUFAs) and Other Dietary Supplements in Inflammation-Associated Depression.

An increasingly pertinent issue in psychiatry in recent years is that of the limitations of conventional antidepressants, which are not effective in a large number of patients with major depressive disorder (MDD). Coupled with emerging hypotheses about the role of inflammation in depression, it would appear that it is time to look for alternative treatments for these symptoms.This review will examine an emerging area in psychiatry, that of dietary supplements and the diet in general to treat depressive symptoms, and inflammation in depression. In particular, polyunsaturated fatty acids (PUFAs), probiotics and folic acid are three supplements that demonstrate the ability to target inflammation and other underlying systems in depression. While there is a definite need for more research in all these supplements to determine true efficacy, dosage and target populations, they can be used as mono- or adjunctive therapies to good effect, and show superior safety profiles when compared with more traditional alternatives.