cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7916/d8-pejd-bf71
title:  GRID3 Mali Settlement Extents Version 01, Alpha

description: NOTE: This initial version of the Mali settlement extents has been superseded by the final version, "GRID3 Mali Settlement Extents, Version 01", now available here: https://doi.org/10.7916/d8-yf1p-yg69 The database constitutes a comprehensive set of settlement polygons nationwide. It is in geodatabase format and consists of three feature classes for built up areas (BUA), small settlement areas (SSA), and hamlets (hamlets). This work has been undertaken as part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) initiative in Mali. GRID3 works with countries to generate, validate and use geospatial data on population, settlements, infrastructure, and subnational boundaries. For more information, see https://grid3.org/ Keywords: built up areas, BUA, small settlement areas, SSA, hamlets, settlement aggregation, settlement extent, CIESIN, GRID3, Novel-T
DOI: 10.17632/r25st3yfkk.1
title: Characterization of immune microenvironment infiltration and m6A regulator-mediated RNA methylation modification patterns in osteoarthritis

description: data from GEO
DOI: 10.6084/m9.figshare.20006764
title: Carotid intima-media thickness in spondyloarthritis patients

description: CONTEXT AND OBJECTIVE Accelerated atherosclerosis has become a major problem in rheumatic inflammatory disease. The aim here was to analyze carotid intima-media thickness (IMT) in spondyloarthritis (SpA) patients and correlate this with clinical parameters and inflammatory markers. DESIGN AND SETTING Cross-sectional analytical study at Rheumatology Outpatient Clinic, Evangelical University Hospital, Curitiba. METHODS IMTs (measured using Doppler ultrasonography) of 36 SpA patients were compared with controls. The IMT in SpA patients was associated with inflammatory markers, like erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI); and with clinical parameters, like axial or peripheral involvement, dactylitis, HLA B27, uveitis occurrence, Bath Ankylosing Spondylitis Functional Index (BASFI) and lipid profile. RESULTS The mean IMT in SpA patients was 0.72 ± 0.21 mm; in controls, 0.57 ± 0.13 mm (P = 0.0007). There were no associations with ESR, CRP, BASDAI or clinical data. In univariate analysis, greater IMT was seen in patients with longer disease duration (P = 0.014; Pearson R = 0.40; 95% confidence interval, CI = 0.06 to 0.65); higher triglycerides (P = 0.02; Spearman R = 0.37; 95% CI = 0.03 to 0.64); and older age (P = 0.0014; Pearson R 0.51; 95% CI = 0.21 to 0.72). CONCLUSION SpA patients have a higher degree of subclinical atherosclerosis than in controls, thus supporting clinical evidence of increased cardiovascular risk in rheumatic patients.
DOI: 10.6084/m9.figshare.20006764.v1
title: Carotid intima-media thickness in spondyloarthritis patients

description: CONTEXT AND OBJECTIVE Accelerated atherosclerosis has become a major problem in rheumatic inflammatory disease. The aim here was to analyze carotid intima-media thickness (IMT) in spondyloarthritis (SpA) patients and correlate this with clinical parameters and inflammatory markers. DESIGN AND SETTING Cross-sectional analytical study at Rheumatology Outpatient Clinic, Evangelical University Hospital, Curitiba. METHODS IMTs (measured using Doppler ultrasonography) of 36 SpA patients were compared with controls. The IMT in SpA patients was associated with inflammatory markers, like erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI); and with clinical parameters, like axial or peripheral involvement, dactylitis, HLA B27, uveitis occurrence, Bath Ankylosing Spondylitis Functional Index (BASFI) and lipid profile. RESULTS The mean IMT in SpA patients was 0.72 ± 0.21 mm; in controls, 0.57 ± 0.13 mm (P = 0.0007). There were no associations with ESR, CRP, BASDAI or clinical data. In univariate analysis, greater IMT was seen in patients with longer disease duration (P = 0.014; Pearson R = 0.40; 95% confidence interval, CI = 0.06 to 0.65); higher triglycerides (P = 0.02; Spearman R = 0.37; 95% CI = 0.03 to 0.64); and older age (P = 0.0014; Pearson R 0.51; 95% CI = 0.21 to 0.72). CONCLUSION SpA patients have a higher degree of subclinical atherosclerosis than in controls, thus supporting clinical evidence of increased cardiovascular risk in rheumatic patients.
DOI: 10.6084/m9.figshare.20006768
title: Expression of claudin, paxillin and FRA-1 in non-nodular breast lesions in association with microcalcifications

description: CONTEXT AND OBJECTIVE The possible role of adhesion molecules in early breast carcinogenesis has been shown in the literature. We aimed to analyze early adhesion imbalances in non-nodular breast lesions and their association with precursor lesions, in order to ascertain whether these alterations exist and contribute towards early carcinogenesis. DESIGN AND SETTING Retrospective cross-sectional study based on medical records at a private radiological clinic in São Paulo, Brazil. METHODS We retrospectively reviewed the medical records of all consecutive women attended between August 2006 and July 2007 who presented mammographic evidence of breast microcalcifications classified as Breast Imaging Reporting and Data System Atlas (BI-RADS) type 4. These women underwent stereotaxic biopsy. Clinical, radiological and pathological data were collected, and immunohistochemical assays searched for claudin, paxillin, FRA-1 and HER-2. RESULTS Over this period, 127 patients were evaluated. Previous BI-RADS diagnoses showed that 69 cases were in category 4A, 47 in 4B and 11 in 4C. Morphological assessment showed benign entities in 86.5%. Most of the benign lesions showed preserved claudin expression, associated with paxillin (P &lt; 0.001). Paxillin and HER-2 expressions were correlated. FRA-1 expression was also strongly associated with HER-2 expression (P &lt; 0.001). CONCLUSIONS Although already present in smaller amounts, imbalance of adhesion molecules is not necessarily prevalent in non-nodular breast lesions. Since FRA-1 expression reached statistically significant correlations with radiological and morphological diagnoses and HER-2 status, it may have a predictive role in this setting.
DOI: 10.6084/m9.figshare.20006768.v1
title: Expression of claudin, paxillin and FRA-1 in non-nodular breast lesions in association with microcalcifications

description: CONTEXT AND OBJECTIVE The possible role of adhesion molecules in early breast carcinogenesis has been shown in the literature. We aimed to analyze early adhesion imbalances in non-nodular breast lesions and their association with precursor lesions, in order to ascertain whether these alterations exist and contribute towards early carcinogenesis. DESIGN AND SETTING Retrospective cross-sectional study based on medical records at a private radiological clinic in São Paulo, Brazil. METHODS We retrospectively reviewed the medical records of all consecutive women attended between August 2006 and July 2007 who presented mammographic evidence of breast microcalcifications classified as Breast Imaging Reporting and Data System Atlas (BI-RADS) type 4. These women underwent stereotaxic biopsy. Clinical, radiological and pathological data were collected, and immunohistochemical assays searched for claudin, paxillin, FRA-1 and HER-2. RESULTS Over this period, 127 patients were evaluated. Previous BI-RADS diagnoses showed that 69 cases were in category 4A, 47 in 4B and 11 in 4C. Morphological assessment showed benign entities in 86.5%. Most of the benign lesions showed preserved claudin expression, associated with paxillin (P &lt; 0.001). Paxillin and HER-2 expressions were correlated. FRA-1 expression was also strongly associated with HER-2 expression (P &lt; 0.001). CONCLUSIONS Although already present in smaller amounts, imbalance of adhesion molecules is not necessarily prevalent in non-nodular breast lesions. Since FRA-1 expression reached statistically significant correlations with radiological and morphological diagnoses and HER-2 status, it may have a predictive role in this setting.
DOI: 10.17632/6vp9m5hcct.1
title: LINF_280032200

description: Eukaryotic translation initiation factor 4E-3 | eIF4E3
Leishmania infantum (strain JPCM5)
DOI: 10.17632/gnmbrzkshp.1
title: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 4 - dataset - Machine Learning Models Applied to Biomedical Engineering 

description: “Machine Learning (ML)” is a subset of “AI” that evolved from the study of “pattern recognition and computational learning theory.” “ML” has seven specific steps to follow to achieve its goal of obtaining a valid model for prediction. The “ML Model” to select to find the solution depends on the type of ML problem; generally, this can be “unsupervised learning,” “supervised learning,” “reinforcement learning,” “survival models,” “association rules,” and others. Please read this chapter at science direct: https://www.sciencedirect.com/science/article/pii/B9780128207185000027 or buy the book or eBook at https://www.elsevier.com/books/applied-biomedical-engineering-using-artificial-intelligence-and-cognitive-models/garza-ulloa/978-0-12-820718-5.
This data set is organized in 3 folders: Matlab_ML (ML using Matlab), SPSS_MODELER (ML using IBM Watson SPSS Modeler) and Watson_ML (ML using IBM Machine Learning Models) 
Section 4.12.2.1 Research 4.1 Tutorial IBM Watson SPSS Modeler Flow for “ML Model for Diabetes” (Folder SPSS_MODELER > SPSS_Clusters)
Section 4.12.2.2 Research tutorial 4.2 IBM Watson SPSS Modeler Flow for “Heart disease ML model and deployment” (folder SPSS_MODELER > SPSS_Classifier)
Section 4.12.2.3 Research tutorial 4.3 IBM Watson SPSS Modeler Flow for “Kidney disease ML Auto Classifiers Models and deploy the best model” (folder SPSS_MODELER > SPSS_AutoClassifier)
Section 4.12.2.4 Research tutorial 4.4 IBM Watson AutoAI experimenter for “Breast cancer ML model and deploy the best model” (folder Watson_ML)
Section 4.12.2.5 Research tutorial 4.5 MATLAB: Statistics and Machine Learning Toolbox for a “Diabetes dataset AI modeling for Classifier Model and a Regression Model” (folder Matlab_ML)
DOI: 10.17632/9k49p7t7zx.1
title: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 6 - dataset -Deep Learning Models Evolution Applied to Biomedical Engineering

description: In this chapter we focus on studying “Deep Learning Models Evolution” that combine midlevel elements with different connections “ANN” types to form more complex network types such as “Recurrent Neural Networks,” “Memory Augmented Neural Networks,” “Modular Neural Networks,” and “Evolutive Neural Networks". Please read this chapter at Science Direct: https://www.sciencedirect.com/science/article/pii/B978012820718500012X or buy the book/eBook at Elsevier, Amazon, and Bookstore worldwide.
Section 6.2.3.1 Research 6.1 LSTM to classify videos about human body movements and detect human falls (folder MATLAB_LTSM_videos)
Section 6.2.5.1 Research 6.2 Regional-CNN model for object detection of breast tumor in mammogram (folder MATLAB_R-CNN)
Section 6.2.6.1 Research 6.3 Hopfield Network model to reconstruct noisy chest X-ray images (folder MATLAB_HN)
Section 6.2.8.1 Research 6.4 Restricted Boltzmann Machine model to reconstruct noisy chest X-ray images (folder MATLAB_RBM)
Section 6.2.10.1 Research 6.5 Create a Reservoir Computing approach for a simulation of “Liquid State Machine (LSM)” of node neurons from “Spiking Neural Networks (SNN)” based on the “Izhikevich neuronal mathematical model” to differentiate normal and pneumonia on chest X-rays (folder MATLAB_LSM)
Section 6.3.2.1 Research 6.6 simulation of a Turing Machine (TM) using a recursive function to "analyze how the respiratory transmission of a COVID-19 infected person can spread a virus through droplets/mini-droplets emissions" (folder MATLAB_NTM)
Section 6.4.1.1 Research 6.7 Create a Deep Belief Network model to analyze and differentiate normal and pneumonia chest X-rays. “DBN model” from MATLAB based on node neurons from “Spiking Neural Networks (SNN)” applying the “Siegert Neurons mathematical model” to “differentiate normal and pneumonia chest X-rays” (folder MATLAB_DBN)
Note*: Please see an “Attention Networks” example at Chapter 7, Research 7.2 “Attention network using Long/Short-Term Memory to Classify Text of COVID-19 symptoms”.
DOI: 10.17632/nc9m5zm8st.1
title: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 5 - dataset - Deep Learning Models Principles Applied to Biomedical Engineering 

description: The underlying principle of “Deep Learning” is that of the compositional nature of “neural networks” inspired by the biological elements that forms the “human brain,” such as a collection of “nodes” emulating “brain neurons” and their “neuron synapses connections as primary elements, that combine to form midlevel elements identified as “Artificial Neural Networks (ANNs),” which in turn are combined with different architectures to form more complex networks. These “ANNs*” are organized based on their architectural type, and the way their different components are connected to one another, defining the specific learning goal in different types*. They are classified in families as “Feed Forward Neural Network,” “Backpropagation Neural Networks,” “Recurrent Neural Networks,” “Memory Augmented Neural Networks,” “Modular Neural Networks,” and “Evolutionary Neural Networks.” The first two “ANN types” are studied in depth in this chapter and the others are covered in the Chapter 6. Please read this chapter at Science Direct: https://www.sciencedirect.com/science/article/pii/B9780128207185000015, or the book - eBook available at Elsevier, Amazon or bookstores worldwide
Note*: There is no standard set of rules to classify the different types of “ANN,” 
Section 5.2.3 “RBF” to analyze “heart rhythm" (folder MATLAB_RBF)
Section 5.2.4 “Probabilistic neural network (PNN)” to classify the “types of FLU: A, B, and C.” (folder MATLAB_PNN)
Section 5.2.5 “Extreme Learning Machine (ELM)” for diabetes values prediction (folder MATLAB_ELM)
Section 5.3.1 5.3.1 Research 5.1 Feed Forward Neural Network to Analyze “Human Body Fat” (folder MATLAB_BFE)
Section 5.3.2 Research 5.2 Neural Network for clustering based on “Self-Organizing MAP* through a Shallow Neural Network” to analyze Surface Electromyography signals (folder MATLAB_CL)
Section 5.3.3 Research 5.3 Neural Network for Dynamic Time series based on a “NARX is a nonlinear autoregressive exogenous model” to analyze vertical Ground Reaction Forces signals (folder MATLAB_DTS)
Section 5.4 Back propagation neural network Auto Encoder Neural Network to reconstruct chest X-rays (folder MATLAB_AE)
Section 5.4.4.1 Research 5.4 Backpropagation Neural Network for Patterns Recognition and classification of “Breast Cancer” (folder MATLAB_PR)
Section 5.5.1 Research 5.5 “Pretrained Deep Convolutional Neural Network to obtain an AI model to classify Mammograms standard views types” (folder MATLAB_IMG)
Section 5.5.2 Research 5.6 modify a “Pretrained Deep Convolutional Neural Network” to obtain AI model to “classify Mammograms view type and suggest breast abnormalities as possible breast tumor” (folder MATLAB_DND)
Section 5.5.3 Research 5.7 “custom Deep Convolutional Neural Network” to obtain an AI model to “classify Cervical X-rays view types” (folder MATLAB_Build_DCN)
DOI: 10.6084/m9.figshare.20006771
title: Measuring spirituality and religiosity in clinical research: a systematic review of instruments available in the Portuguese language

description: CONTEXT AND OBJECTIVES Despite numerous spirituality and/or religiosity (S/R) measurement tools for use in research worldwide, there is little information on S/R instruments in the Portuguese language. The aim of the present study was to map out the S/R scales available for research in the Portuguese language. DESIGN AND SETTING Systematic review of studies found in databases. METHODS A systematic review was conducted in three phases. Phases 1 and 2: articles in Portuguese, Spanish and English, published up to November 2011, dealing with the Portuguese translation and/or validation of S/R measurement tools for clinical research, were selected from six databases. Phase 3: the instruments were grouped according to authorship, cross-cultural adaptation, internal consistency, concurrent and discriminative validity and test-retest procedures. RESULTS Twenty instruments were found. Forty-five percent of these evaluated religiosity, 40% spirituality, 10% religious/spiritual coping and 5% S/R. Among these, 90% had been produced in (n = 3) or translated to (n = 15) Brazilian Portuguese and two (10%) solely to European Portuguese. Nevertheless, the majority of the instruments had not undergone in-depth psychometric analysis. Only 40% of the instruments presented concurrent validity, 45% discriminative validity and 15% a test-retest procedure. The characteristics of each instrument were analyzed separately, yielding advantages, disadvantages and psychometric properties. CONCLUSION Currently, 20 instruments for measuring S/R are available in the Portuguese language. Most have been translated (n = 15) or developed (n = 3) in Brazil and present good internal consistency. Nevertheless, few instruments have been assessed regarding all their psychometric qualities.
DOI: 10.6084/m9.figshare.20006771.v1
title: Measuring spirituality and religiosity in clinical research: a systematic review of instruments available in the Portuguese language

description: CONTEXT AND OBJECTIVES Despite numerous spirituality and/or religiosity (S/R) measurement tools for use in research worldwide, there is little information on S/R instruments in the Portuguese language. The aim of the present study was to map out the S/R scales available for research in the Portuguese language. DESIGN AND SETTING Systematic review of studies found in databases. METHODS A systematic review was conducted in three phases. Phases 1 and 2: articles in Portuguese, Spanish and English, published up to November 2011, dealing with the Portuguese translation and/or validation of S/R measurement tools for clinical research, were selected from six databases. Phase 3: the instruments were grouped according to authorship, cross-cultural adaptation, internal consistency, concurrent and discriminative validity and test-retest procedures. RESULTS Twenty instruments were found. Forty-five percent of these evaluated religiosity, 40% spirituality, 10% religious/spiritual coping and 5% S/R. Among these, 90% had been produced in (n = 3) or translated to (n = 15) Brazilian Portuguese and two (10%) solely to European Portuguese. Nevertheless, the majority of the instruments had not undergone in-depth psychometric analysis. Only 40% of the instruments presented concurrent validity, 45% discriminative validity and 15% a test-retest procedure. The characteristics of each instrument were analyzed separately, yielding advantages, disadvantages and psychometric properties. CONCLUSION Currently, 20 instruments for measuring S/R are available in the Portuguese language. Most have been translated (n = 15) or developed (n = 3) in Brazil and present good internal consistency. Nevertheless, few instruments have been assessed regarding all their psychometric qualities.
DOI: 10.17632/ffb7bh9sw6.1
title: distal radius fracture

description: distal radius fracture
DOI: 10.17632/wkchkpbfhz.1
title: ideal ulnar shortening osteotomy level

description: ideal ulnar shortening osteotomy level
DOI: 10.6084/m9.figshare.20006773
title: Natriuria and calciuria levels in preeclampsia: a cross-sectional study

description: CONTEXT AND OBJECTIVE Sodium excretion abnormalities in preeclampsia have been studied in relation to several factors. The objective of this study was to compare natriuria (mEq/24 h) and calciuria levels (mg/24 h) in preeclamptic patients. DESIGN AND SETTING An analytical cross-sectional study with a control group was conducted in the obstetric center and the high-risk pregnancy outpatient clinic at a university hospital in southern Brazil, and in a primary healthcare unit in the same city, including pregnant women with mild preeclampsia, severe preeclampsia or chronic hypertension, and women with normal pregnancies (14 patients in each group). METHOD Natriuria was measured using an ion-selective electrode in an automated clinical chemistry analyzer (Hitache 917, Roche). All the patients collected 24-hour urine, at home or at the hospital, for analysis of proteins, creatinine, calcium, sodium and uric acid. Quantitative variables with asymmetrical distribution were described using the median, minimum and maximum, and were compared using the Kruskal-Wallis test. The results were logarithmically transformed, with one-way analysis of variance (ANOVA) by ranks and then the post-hoc Tukey test, and were analyzed by means of the Spearman correlation and receiver operating characteristic (ROC) curve. The significance level used was 0.05. RESULTS There were significant differences between the groups in comparing severe preeclampsia with chronic hypertension and severe preeclampsia with controls (P &lt; 0.0001 for both measurements). CONCLUSION Natriuria levels may be lower in preeclampsia when associated with calciuria. Natriuria assessment is an additional test for differential diagnosis of hypertensive diseases in pregnancy, but is a poor predictor when used alone.
DOI: 10.6084/m9.figshare.20006773.v1
title: Natriuria and calciuria levels in preeclampsia: a cross-sectional study

description: CONTEXT AND OBJECTIVE Sodium excretion abnormalities in preeclampsia have been studied in relation to several factors. The objective of this study was to compare natriuria (mEq/24 h) and calciuria levels (mg/24 h) in preeclamptic patients. DESIGN AND SETTING An analytical cross-sectional study with a control group was conducted in the obstetric center and the high-risk pregnancy outpatient clinic at a university hospital in southern Brazil, and in a primary healthcare unit in the same city, including pregnant women with mild preeclampsia, severe preeclampsia or chronic hypertension, and women with normal pregnancies (14 patients in each group). METHOD Natriuria was measured using an ion-selective electrode in an automated clinical chemistry analyzer (Hitache 917, Roche). All the patients collected 24-hour urine, at home or at the hospital, for analysis of proteins, creatinine, calcium, sodium and uric acid. Quantitative variables with asymmetrical distribution were described using the median, minimum and maximum, and were compared using the Kruskal-Wallis test. The results were logarithmically transformed, with one-way analysis of variance (ANOVA) by ranks and then the post-hoc Tukey test, and were analyzed by means of the Spearman correlation and receiver operating characteristic (ROC) curve. The significance level used was 0.05. RESULTS There were significant differences between the groups in comparing severe preeclampsia with chronic hypertension and severe preeclampsia with controls (P &lt; 0.0001 for both measurements). CONCLUSION Natriuria levels may be lower in preeclampsia when associated with calciuria. Natriuria assessment is an additional test for differential diagnosis of hypertensive diseases in pregnancy, but is a poor predictor when used alone.
DOI: 10.17632/g57436wy6j.1
title: Serum autoantibodyome reveals that healthy individuals share common autoantibodies. Shome et al.

description: Raw binary data used for the meta-analysis in the manuscript.