ResearchPad - learning https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[The effects of age and sex on cognitive impairment in schizophrenia: Findings from the Consortium on the Genetics of Schizophrenia (COGS) study]]> https://www.researchpad.co/article/elastic_article_13860 Recently emerging evidence indicates accelerated age-related changes in the structure and function of the brain in schizophrenia, raising a question about its potential consequences on cognitive function. Using a large sample of schizophrenia patients and controls and a battery of tasks across multiple cognitive domains, we examined whether patients show accelerated age-related decline in cognition and whether an age-related effect differ between females and males. We utilized data of 1,415 schizophrenia patients and 1,062 healthy community collected by the second phase of the Consortium on the Genetics of Schizophrenia (COGS-2). A battery of cognitive tasks included the Letter-Number Span Task, two forms of the Continuous Performance Test, the California Verbal Learning Test, Second Edition, the Penn Emotion Identification Test and the Penn Facial Memory Test. The effect of age and gender on cognitive performance was examined with a general linear model. We observed age-related changes on most cognitive measures, which was similar between males and females. Compared to controls, patients showed greater deterioration in performance on attention/vigilance and greater slowness of processing social information with increasing age. However, controls showed greater age-related changes in working memory and verbal memory compared to patients. Age-related changes (η2p of 0.001 to .008) were much smaller than between-group differences (η2p of 0.005 to .037). This study found that patients showed continued decline of cognition on some domains but stable impairment or even less decline on other domains with increasing age. These findings indicate that age-related changes in cognition in schizophrenia are subtle and not uniform across multiple cognitive domains.

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<![CDATA[Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis]]> https://www.researchpad.co/article/elastic_article_13837 In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

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<![CDATA[Insight into the protein solubility driving forces with neural attention]]> https://www.researchpad.co/article/elastic_article_13832 The solubility of proteins is a crucial biophysical aspect when it comes to understanding many human diseases and to improve the industrial processes for protein production. Due to its relevance, computational methods have been devised in order to study and possibly optimize the solubility of proteins. In this work we apply a deep-learning technique, called neural attention to predict protein solubility while “opening” the model itself to interpretability, even though Machine Learning models are usually considered black boxes. Thank to the attention mechanism, we show that i) our model implicitly learns complex patterns related to emergent, protein folding-related, aspects such as to recognize β-amyloidosis regions and that ii) the N-and C-termini are the regions with the highes signal fro solubility prediction. When it comes to enhancing the solubility of proteins, we, for the first time, propose to investigate the synergistic effects of tandem mutations instead of “single” mutations, suggesting that this could minimize the number of required proposed mutations.

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<![CDATA[Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019]]> https://www.researchpad.co/article/elastic_article_13811 The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters.MethodsIn this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province–located in northwestern Iran- over a period of 9 years (2010–2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R2 criteria.ResultsThe incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010–2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.ConclusionsThe multilayer perceptron neural network can be used as an effective method in detecting the behavioral trend of brucellosis over time. Nevertheless, further studies focusing on the application and comparison of these methods are needed to detect the most appropriate forecast method for this disease. ]]> <![CDATA[A model for the assessment of bluetongue virus serotype 1 persistence in Spain]]> https://www.researchpad.co/article/elastic_article_11225 Bluetongue virus (BTV) is an arbovirus of ruminants that has been circulating in Europe continuously for more than two decades and has become endemic in some countries such as Spain. Spain is ideal for BTV epidemiological studies since BTV outbreaks from different sources and serotypes have occurred continuously there since 2000; BTV-1 has been reported there from 2007 to 2017. Here we develop a model for BTV-1 endemic scenario to estimate the risk of an area becoming endemic, as well as to identify the most influential factors for BTV-1 persistence. We created abundance maps at 1-km2 spatial resolution for the main vectors in Spain, Culicoides imicola and Obsoletus and Pulicaris complexes, by combining environmental satellite data with occurrence models and a random forest machine learning algorithm. The endemic model included vector abundance and host-related variables (farm density). The three most relevant variables in the endemic model were the abundance of C. imicola and Obsoletus complex and density of goat farms (AUC 0.86); this model suggests that BTV-1 is more likely to become endemic in central and southwestern regions of Spain. It only requires host- and vector-related variables to identify areas at greater risk of becoming endemic for bluetongue. Our results highlight the importance of suitable Culicoides spp. prediction maps for bluetongue epidemiological studies and decision-making about control and eradication measures.

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<![CDATA[Status and situation of postgraduate medical students in China under the influence of COVID-19]]> https://www.researchpad.co/article/elastic_article_10264 <![CDATA[Using case-level context to classify cancer pathology reports]]> https://www.researchpad.co/article/elastic_article_7869 Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

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<![CDATA[Participant and informant memory-specific cognitive complaints predict future decline and incident dementia: Findings from the Sydney Memory and Ageing Study]]> https://www.researchpad.co/article/elastic_article_7842 Subjective Cognitive Complaints (SCCs) may represent one of the earliest stages of preclinical dementia. The objective of the present study was to extend previous work by our group to examine the relationship between participant-reported and informant-reported memory and non-memory SCCs, cognitive decline and incident dementia, over a six-year period. Participants were 873 community dwelling older adults (Mage = 78.65, SD = 4.79) without dementia and 843 informants (close friends or family) from the Sydney Memory and Ageing Study. Comprehensive neuropsychological testing and diagnostic assessments were carried out at baseline and biennially for six years. Linear mixed models and Cox proportional hazard models were performed to determine the association of SCCs, rate of cognitive decline and risk of incident dementia, controlling demographics and covariates of mood and personality. Participant and informant memory-specific SCCs were associated with rate of global cognitive decline; for individual cognitive domains, participant memory SCCs predicted decline for language, while informant memory SCCs predicted decline for executive function and memory. Odds of incident dementia were associated with baseline participant memory SCCs and informant memory and non-memory SCCs in partially adjusted models. In fully adjusted models, only informant SCCs were associated with increased risk of incident dementia. Self-reported memory-specific cognitive complaints are associated with decline in global cognition over 6-years and may be predictive of incident dementia, particularly if the individual is depressed or anxious and has increased neuroticism or decreased openness. Further, if and where possible, informants should be sought and asked to report on their perceptions of the individual’s memory ability and any memory-specific changes that they have noticed as these increase the index of diagnostic suspicion.

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<![CDATA[Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions]]> https://www.researchpad.co/article/elastic_article_7734 Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.

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<![CDATA[Adaptation to unstable coordination patterns in individual and joint actions]]> https://www.researchpad.co/article/elastic_article_7665 Previous research on interlimb coordination has shown that some coordination patterns are more stable than others, and function as attractors in the space of possible phase relations between different rhythmic movements. The canonical coordination patterns, i.e. the two most stable phase relations, are in-phase (0 degree) and anti-phase (180 degrees). Yet, musicians are able to perform other coordination patterns in intrapersonal as well as in interpersonal coordination with remarkable precision. This raises the question of how music experts manage to produce these unstable patterns of movement coordination. In the current study, we invited participants with at least five years of training on a musical instrument. We used an adaptation paradigm to address two factors that may facilitate producing unstable coordination patterns. First, we investigated adaptation in different coordination settings, to test the hypothesis that the lower coupling strength between individuals during joint performance makes it easier to achieve stability outside of the canonical patterns than the stronger coupling during individual bimanual performance. Second, we investigated whether adding to the structure of action effects may support achieving unstable coordination patterns, both intra- and inter-individually. The structure of action effects was strengthened by adding a melodic contour to the action effects, a measure that has been shown to improve the acquisition of bimanual coordination skills. Adaptation performance was measured both in terms of asynchrony and variability thereof. As predicted, we found that producing unstable patterns benefitted from the weaker coupling during joint performance. Surprisingly, the structure of action effects did not help with achieving unstable coordination patterns.

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<![CDATA[Emotional facial perception development in 7, 9 and 11 year-old children: The emergence of a silent eye-tracked emotional other-race effect]]> https://www.researchpad.co/article/elastic_article_7635 The present study examined emotional facial perception (happy and angry) in 7, 9 and 11-year-old children from Caucasian and multicultural environments with an offset task for two ethnic groups of faces (Asian and Caucasian). In this task, participants were required to respond to a dynamic facial expression video when they believed that the first emotion presented had disappeared. Moreover, using an eye-tracker, we evaluated the ocular behavior pattern used to process these different faces. The analyses of reaction times do not show an emotional other-race effect (i.e., a facility in discriminating own-race faces over to other-race ones) in Caucasian children for Caucasian vs. Asian faces through offset times, but an effect of emotional face appeared in the oldest children. Furthermore, an eye-tracked ocular emotion and race-effect relative to processing strategies is observed and evolves between age 7 and 11. This study strengthens the interest in advancing an eye-tracking study in developmental and emotional processing studies, showing that even a “silent” effect should be detected and shrewdly analyzed through an objective means.

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<![CDATA[PigLeg: prediction of swine phenotype using machine learning]]> https://www.researchpad.co/article/N823fa3cb-5286-4b44-9d39-27d7bb6cdb07

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

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<![CDATA[ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence]]> https://www.researchpad.co/article/Ncbcbfd8a-cc62-486f-9f91-198b1ae2a978

Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human ECM components was also established by applying ECMPride to all human entries in the SwissProt database, containing a significant number of putative ECM proteins as well as the abundant biological annotations. This dataset might serve as a valuable reference resource for ECM protein identification.

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<![CDATA[Reverse Causation, Physical Inactivity, and Dementia]]> https://www.researchpad.co/article/Nd4bf0fde-a835-47ab-ba11-d35e58689235

One variable may influence another as cause and effect. However, in situations in which a cause-effect relationship is scientifically plausible, reverse causation may also be possible. As an example, physical inactivity may predispose to dementia through cardiometabolic and other mechanisms. However, physical inactivity may also be a result of an ongoing dementia prodrome in which patients are physically slowed down during the years preceding the dementia diagnosis. This article examines reverse causation and how it was studied in a recent individual participant data meta-analysis of physical inactivity as a risk factor for dementia. This article also shows that other interpretations are possible when a finding suggests reverse causation.

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<![CDATA[Fingerprint evidence for the division of labour and learning pottery-making at Early Bronze Age Tell eṣ-Ṣâfi/Gath, Israel]]> https://www.researchpad.co/article/N5152a5b5-1b3f-41e8-b706-9ccd50f6a496

The organization of craft production has long been a marker for broader social, economic and political changes that accompanied urbanism. The identity of producers who comprised production groups, communities, or workshops is out of reach using conventional archaeological data. There has been some success using epidermal prints on artefacts to identify the age and sex of producers. However, forensic research indicates that a combination of ridge breadth and ridge density would best identify the age and sex of individuals. To this end, we combine mean ridge breadth (MRB) and mean ridge density (MRD) to distinguish the age and sex of 112 fingerprints on Early Bronze Age (EB) III pottery from the early urban neighbourhood at Tell eṣ-Ṣâfi/Gath, Israel, dating to a 100 year time span. Our analysis accounts for the shrinkage of calcareous fabrics used to make six type of vessels, applies a modified version of the Kamp et al. regression equation to the MRB for each individual print, and infers sex by correlating MRD data to appropriate modern reference populations. When the results are combined, our analyses indicate that most fingerprints were made by adult and young males and the remainder by adult and young females. Children’s prints are in evidence but only occur on handles. Multiple prints of different age and sex on the same vessels suggest they were impressed during the training of young potters. Production appears dominated by adult and young males working alone, together, and in cooperation with adult and/or young females. Vessels with prints exclusively by females of any age are rare. This male dominant cooperative labour pattern contrasts recent studies showing that adult women primarily made Neolithic figurines in Anatolia, and more females than males were making pottery prior to the rise of city-states in northern Mesopotamia.

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<![CDATA[Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics]]> https://www.researchpad.co/article/Na70a74a3-c5ef-43cc-b1b5-9a98f2c2696c

Heterogeneity in cognitive and academic abilities is a prominent feature of autism spectrum disorder (ASD), yet little is known about its underlying causes. Here we combine functional brain imaging during numerical problem-solving with hierarchical drift-diffusion models of behavior and standardized measures of numerical abilities to investigate neural mechanisms underlying cognitive variability in children with ASD, and their IQ-matched Typically Developing (TD) peers. Although the two groups showed similar levels of brain activation, the relation to individual abilities differed markedly in ventral temporal-occipital, parietal and prefrontal regions important for numerical cognition: children with ASD showed a positive correlation between functional brain activation and numerical abilities, whereas TD children showed the opposite pattern. Despite similar accuracy and response times, decision thresholds were significantly higher in the ASD group, suggesting greater evidence required for problem-solving. Critically, the relationship between individual abilities and engagement of prefrontal control systems anchored in the anterior insula was differentially moderated by decision threshold in subgroups of children with ASD. Our findings uncover novel cognitive and neural sources of variability in academically-relevant cognitive skills in ASD and suggest that multilevel measures and latent decision-making dynamics can aid in characterization of cognitive variability and heterogeneity in neurodevelopmental disorders.

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<![CDATA[Tsinghua facial expression database – A database of facial expressions in Chinese young and older women and men: Development and validation]]> https://www.researchpad.co/article/Nf679a1e8-67cb-47b3-95b4-f3d293b80761

Perception of facial identity and emotional expressions is fundamental to social interactions. Recently, interest in age associated changes in the processing of faces has grown rapidly. Due to the lack of older faces stimuli, most previous age-comparative studies only used young faces stimuli, which might cause own-age advantage. None of the existing Eastern face stimuli databases contain face images of different age groups (e.g. older adult faces). In this study, a database that comprises images of 110 Chinese young and older adults displaying eight facial emotional expressions (Neutral, Happiness, Anger, Disgust, Surprise, Fear, Content, and Sadness) was constructed. To validate this database, each image was rated on the basis of perceived facial expressions, perceived emotional intensity, and perceived age by two different age groups. Results have shown an overall 79.08% correct identification rate in the validation. Access to the freely available database can be requested by emailing the corresponding authors.

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<![CDATA[Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding]]> https://www.researchpad.co/article/N1f40719a-4631-45e6-bedb-5cf8a42ecf53

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

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<![CDATA[Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis]]> https://www.researchpad.co/article/N201121b9-bfe0-423d-91d1-e349ea424365

Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communication, a variety of parameters are employed to evaluate the well-being of an animal. Especially in the field of biomedical research, scientifically sound tools to assess pain, suffering, and distress for experimental animals are highly demanded due to ethical and legal reasons. For mice, the most commonly used laboratory animals, a valuable tool is the Mouse Grimace Scale (MGS), a coding system for facial expressions of pain in mice. We aim to develop a fully automated system for the surveillance of post-surgical and post-anesthetic effects in mice. Our work introduces a semi-automated pipeline as a first step towards this goal. A new data set of images of black-furred laboratory mice that were moving freely is used and provided. Images were obtained after anesthesia (with isoflurane or ketamine/xylazine combination) and surgery (castration). We deploy two pre-trained state of the art deep convolutional neural network (CNN) architectures (ResNet50 and InceptionV3) and compare to a third CNN architecture without pre-training. Depending on the particular treatment, we achieve an accuracy of up to 99% for the recognition of the absence or presence of post-surgical and/or post-anesthetic effects on the facial expression.

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<![CDATA[LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data]]> https://www.researchpad.co/article/Na533cb35-b26a-447b-bd62-8e125a165db4

Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources. To tackle this problem, we proposed an adaptive boosting method named LoAdaBoost that increases the efficiency of federated machine learning. Using intensive care unit data from hospitals, we investigated the performance of learning in IID and non-IID data distribution scenarios, and showed that the proposed LoAdaBoost method achieved higher predictive accuracy with lower computational complexity than the baseline method.

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