Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset

In this work, we compare two simple methods of tagging scientific publications with labels reflecting their content. As a first source of labels Wikipedia is employed, second label set is constructed from the noun phrases occurring in the analyzed corpus. We examine the statistical properties and the effectiveness of both approaches on the dataset consisting of abstracts from 0.7 million of scientific documents deposited in the ArXiv preprint collection. We believe that obtained tags can be later on applied as useful document features in various machine learning tasks (document similarity, clustering, topic modelling, etc.)

Functional Amyloid Formation within Mammalian Tissue

Amyloid is a generally insoluble, fibrous cross-β sheet protein aggregate. The process of amyloidogenesis is associated with a variety of neurodegenerative diseases including Alzheimer, Parkinson, and Huntington disease. We report the discovery of an unprecedented functional mammalian amyloid structure generated by the protein Pmel17. This discovery demonstrates that amyloid is a fundamental nonpathological protein fold utilized by organisms from bacteria to humans. We have found that Pmel17 amyloid templates and accelerates the covalent polymerization of reactive small molecules into melanin—a critically important biopolymer that protects against a broad range of cytotoxic insults including UV and oxidative damage. Pmel17 amyloid also appears to play a role in mitigating the toxicity associated with melanin formation by sequestering and minimizing diffusion of highly reactive, toxic melanin precursors out of the melanosome. Intracellular Pmel17 amyloidogenesis is carefully orchestrated by the secretory pathway, utilizing membrane sequestration and proteolytic steps to protect the cell from amyloid and amyloidogenic intermediates that can be toxic. While functional and pathological amyloid share similar structural features, critical differences in packaging and kinetics of assembly enable the usage of Pmel17 amyloid for normal function. The discovery of native Pmel17 amyloid in mammals provides key insight into the molecular basis of both melanin formation and amyloid pathology, and demonstrates that native amyloid (amyloidin) may be an ancient, evolutionarily conserved protein quaternary structure underpinning diverse pathways contributing to normal cell and tissue physiology

Indulgent thinking?:Ecological momentary assessment of overweight and healthy-weight participants' cognitions and emotions

Cognitions and emotions are considered important determinants of eating behaviour in cognitive behavioural models of obesity. Ecological data on these determinants is still limited. The present study investigated cognitions and emotions of overweight (n = 57) and healthy-weight (n = 43) participants via Ecological Momentary Assessment. It was found that eating-related cognitions mainly focused on desire and taste. Unexpectedly, dysfunctional cognitions (i.e., thoughts that may promote overeating) did not occur more often for overweight participants in almost all cases. So, the present EMA study provides no evidence for a role of dysfunctional cognitions in obesity-promoting eating behaviour when assessing eating-related cognitions immediately prior to eating events using a free-text format assessment. Right before eating events, participants mostly reported feeling calm/relaxed and cheerful/happy. Overweight participants scored higher on negative emotions, both at eating events and non-eating moments, than did healthy-weight participants. In addition, scores on standard questionnaires assessing emotional eating were positively associated with negative emotions reported at both eating and non-eating moments. As such, negative emotions, as assessed in the present study, do not seem to be specific triggers for food consumption

Problem drinking recognition among UK military personnel: prevalence and associations

PurposeDespite the higher prevalence of problem drinking in the UK military compared to the general population, problem recognition appears to be low, and little is known about which groups are more likely to recognise a problem. This study examined prevalence of problem drinking recognition and its associations.MethodsWe analysed data from 6400 regular serving and ex-serving personnel, collected in phase 3 (2014–2016) of the King's Centre for Military Health Research cohort study.MeasurementsParticipants provided sociodemographic, military, health and impairment, life experiences, problem drinking, and problem recognition information. Problem drinking was categorised as scores ≥ 16 in the AUDIT questionnaire. Associations with problem recognition were examined with weighted logistic regressions.FindingsAmong personnel meeting criteria for problem drinking, 49% recognised the problem. Recognition was most strongly associated (ORs ≥ 2.50) with experiencing probable PTSD (AOR = 2.86, 95% CI = 1.64–5.07), social impairment due to physical or mental health problems (AOR = 2.69, 95% CI = 1.51–4.79), adverse life events (AOR = 2.84, 95% CI = 1.70–4.75), ever being arrested (AOR = 2.99, CI = 1.43–6.25) and reporting symptoms of alcohol dependence (AOR = 3.68, 95% CI = 2.33–5.82). To a lesser extent, recognition was also statistically significantly associated with experiencing psychosomatic symptoms, feeling less healthy, probable common mental health disorders, and increased scores on the AUDIT.ConclusionHalf of UK military personnel experiencing problem drinking does not self-report their drinking behaviour as problematic. Greater problem drinking severity, poorer mental or physical health, and negative life experiences facilitate problem recognition

Prediction of Snacking Behavior Involving Snacks Having High Levels of Saturated Fats, Salt, or Sugar Using Only Information on Previous Instances of Snacking: Survey- and App-Based Study

Background: Consuming high amounts of foods or beverages with high levels of saturated fats, salt, or sugar (HFSS) can be harmful for health. Many snacks fall into this category (HFSS snacks). However, the palatability of these snacks means that people can sometimes struggle to reduce their intake. Machine learning algorithms could help in predicting the likely occurrence of HFSS snacking so that just-in-time adaptive interventions can be deployed. However, HFSS snacking data have certain characteristics, such as sparseness and incompleteness, which make snacking prediction a challenge for machine learning approaches. Previous attempts have employed several potential predictor variables and have achieved considerable success. Nevertheless, collecting information from several dimensions requires several potentially burdensome user questionnaires, and thus, this approach may be less acceptable for the general public. Objective: Our aim was to consider the capacity of standard (unmodified in any way; to tailor to the specific learning problem) machine learning algorithms to predict HFSS snacking based on the following minimal data that can be collected in a mostly automated way: day of the week, time of the day (divided into time bins), and location (divided into work, home, and other). Methods: A total of 111 participants in the United Kingdom were asked to record HFSS snacking occurrences and the location category over a period of 28 days, and this was considered the UK dataset. Data collection was facilitated by a purpose-specific app (Snack Tracker). Additionally, a similar dataset from the Netherlands was used (Dutch dataset). Both datasets were analyzed using machine learning methods, including random forest regressor, Extreme Gradient Boosting regressor, feed forward neural network, and long short-term memory. We additionally employed 2 baseline statistical models for prediction. In all cases, the prediction problem was the time to the next HFSS snack from the current one, and the evaluation metric was the mean absolute error. Results: The ability of machine learning methods to predict the time of the next HFSS snack was assessed. The quality of the prediction depended on the dataset, temporal resolution, and machine learning algorithm employed. In some cases, predictions were accurate to as low as 17 minutes on average. In general, machine learning methods outperformed the baseline models, but no machine learning method was clearly better than the others. Feed forward neural network showed a very marginal advantage. Conclusions: The prediction of HFSS snacking using sparse data is possible with reasonable accuracy. Our findings offer a foundation for further exploring how machine learning methods can be used in health psychology and provide directions for further research

Sleep health among people with severe mental ill health during the COVID-19 pandemic: Results from a linked UK population cohort study

Objectives: Sleep problems are a transdiagnostic feature of nearly all psychiatric conditions, and a strong risk factor for initial and recurrent episodes. However, people with severe mental ill health (SMI) are often excluded from general population surveys, and as such the extent and associates of poor sleep in this population are less well understood. This study explores sleep health in an SMI sample during the COVID-19 pandemic, using multiple regression to identify risk factors, including daily routine, wellbeing and demographics. Methods: An existing cohort of people with an SMI diagnosis were sampled. Participants were invited to complete a self-report survey about their health and the impacts of COVID-19 and associated public health measures. Sleep duration, efficiency, and quality were measured using items from the Pittsburgh Sleep Quality Index (PSQI). Results: Two hundred forty-nine adults (aged 21–84 years) completed the survey. Mean sleep duration and efficiency were similar to general population estimates, at 7 h 19 min and 78%, respectively. However, 43% reported “bad” sleep quality that was associated with being younger in age as well as disturbed routine and declined wellbeing. Indeed, 37% reported a disturbed routine during the pandemic. Conclusions: High estimates of perceived poor sleep quality in the SMI population align with previous findings. Supporting people with SMI to maintain routine regularity may work to protect sleep quality and wellbeing. Future research should more closely examine sleep health in people with SMI, using accessible and scalable measures of objective and subjective sleep, examining longitudinal trends

Evidence Based Selection of Housekeeping Genes

For accurate and reliable gene expression analysis, normalization of gene expression data against housekeeping genes (reference or internal control genes) is required. It is known that commonly used housekeeping genes (e.g. ACTB, GAPDH, HPRT1, and B2M) vary considerably under different experimental conditions and therefore their use for normalization is limited. We performed a meta-analysis of 13,629 human gene array samples in order to identify the most stable expressed genes. Here we show novel candidate housekeeping genes (e.g. RPS13, RPL27, RPS20 and OAZ1) with enhanced stability among a multitude of different cell types and varying experimental conditions. None of the commonly used housekeeping genes were present in the top 50 of the most stable expressed genes. In addition, using 2,543 diverse mouse gene array samples we were able to confirm the enhanced stability of the candidate novel housekeeping genes in another mammalian species. Therefore, the identified novel candidate housekeeping genes seem to be the most appropriate choice for normalizing gene expression data

Comparative analytical performance of multiple plasma Aβ42 and Aβ40 assays and their ability to predict positron emission tomography amyloid positivity

INTRODUCTION: This report details the approach taken to providing a dataset allowing for analyses on the performance of recently developed assays of amyloid beta (Aβ) peptides in plasma and the extent to which they improve the prediction of amyloid positivity. METHODS: Alzheimer's Disease Neuroimaging Initiative plasma samples with corresponding amyloid positron emission tomography (PET) data were run on six plasma Aβ assays. Statistical tests were performed to determine whether the plasma Aβ measures significantly improved the area under the receiver operating characteristic curve for predicting amyloid PET status compared to age and apolipoprotein E (APOE) genotype. RESULTS: The age and APOE genotype model predicted amyloid status with an area under the curve (AUC) of 0.75. Three assays improved AUCs to 0.81, 0.81, and 0.84 (P < .05, uncorrected for multiple comparisons). DISCUSSION: Measurement of Aβ in plasma contributes to addressing the amyloid component of the ATN (amyloid/tau/neurodegeneration) framework and could be a first step before or in place of a PET or cerebrospinal fluid screening study. HIGHLIGHTS: The Foundation of the National Institutes of Health Biomarkers Consortium evaluated six plasma amyloid beta (Aβ) assays using Alzheimer's Disease Neuroimaging Initiative samples. Three assays improved prediction of amyloid status over age and apolipoprotein E (APOE) genotype. Plasma Aβ42/40 predicted amyloid positron emission tomography status better than Aβ42 or Aβ40 alone

Immune signatures in human PBMCs of idiotypic vaccine for HCV-related lymphoproliferative disorders

Hepatitis C virus (HCV) is one of the major risk factors for chronic hepatitis, which may progress to cirrhosis and hepatocellular carcinoma, as well as for type II mixed cryoglobulinemia (MC), which may further evolve into an overt B-cell non-Hodgkin's lymphoma (NHL)

Validation of Endogenous Control Genes for Gene Expression Studies on Human Ocular Surface Epithelium

PURPOSE: To evaluate a panel of ten known endogenous control genes (ECG) with quantitative reverse transcription PCR (qPCR), for identification of stably expressed endogenous control genes in the ocular surface (OS) epithelial regions including cornea, limbus, limbal epithelial crypt and conjunctiva to normalise the quantitative reverse transcription PCR data of genes of interest expressed in above-mentioned regions. METHOD: The lasermicrodissected (LMD) OS epithelial regions of cryosectioned corneoscleral buttons from the cadaver eyes were processed for RNA extraction and cDNA synthesis to detect genes of interest with qPCR. Gene expression of 10 known ECG--glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta actin (ACTB), peptidylprolyl isomerase (PPIA), TATA-box binding protein (TBP1), hypoxanthine guanine phosphoribosyl transferase (HPRT1), beta glucuronidase (GUSB), Eucaryotic 18S ribosomal RNA (18S), phosphoglycerate kinase (PGK1), beta-2-microglobulin (B2M), ribosomal protein, large, P0 (RPLP0)--was measured in the OS epithelial regions by qPCR method and the data collected was further analysed using geNorm software. RESULTS: The expression stability of ecgs in the os epithelial regions in increasing order as determined with genorm software is as follows: ACTB<18S<TBP<B2M<PGK1<HPRT1<GUSB<GAPDH<PPIA-RPLP0. In this study, geNorm analysis has shown the following ECGs pairs to be most stably expressed in individual OS epithelial regions: HPRT1-TBP in cornea, GUSB-PPIA in limbus, B2M-PPIA and RPLP0-TBP in LEC and conjunctiva respectively. However, across the entire ocular surface including all the regions mentioned above, PPIA-RPLP0 pair was shown to be most stable. CONCLUSION: This study has identified stably expressed ECGs on the OS epithelial regions for effective qPCR results in genes of interest. The results from this study are broadly applicable to quantitative reverse transcription PCR studies on human OS epithelium and provide evidence for the use of PPIA-RPLP0 ECGs pair in quantitative reverse transcription PCR across the OS epithelium