Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Model Applied on Chest X-Ray Images Enables Automatic Detection of COVID-19 Cases with High Accuracy

Purpose This research was designed to investigate the application of artificial intelligence (AI) in the rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) using digital chest X-ray images, and to develop a robust computer-aided application for the automatic classification of COVID-19 pneumonia from other pneumonia and normal images. Materials and Methods A total of 1100 chest X-ray images were randomly selected from three different open sources, containing 300 X-ray images of confirmed COVID-19 patients, 400 images of other pneumonia patients, and 400 normal X-ray images. In this study, a classical machine learning approach was employed. The model was built using the support vector machine (SVM) classifier algorithm. The SVM was trained by 630 features obtained from the HOG descriptor, which was quantized into 30 orientation bins in the range between 0 and 360. The model was validated using a 10-fold cross-validation method. The performance of the model was evaluated using appropriate classification metrics, including sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, kappa, and accuracy. Results The multi-level classification model was able to distinguish COVID-19 patients with a sensitivity of 97.92% and specificity of 98.91%, for the internal testing or cross-validation. For the independent external testing, the model showed sensitivity of 95% and specificity of 98.13%, for distinguishing COVID-19 from other pneumonia and no-findings. The binary classification model was able to distinguish COVID-19 patients with a sensitivity of 99.58% and specificity of 99.69%, for the internal testing. For the independent external testing, the model showed a sensitivity of 98.33% and specificity of 100%, for distinguishing COVID-19 from normal images. Conclusion The model can achieve the rapid and accurate identification of COVID-19 patients from chest X-rays with more than 97% accuracy. This high accuracy and very rapid computer-aided diagnostic approach would be very helpful to control the pandemic.
Association Between the Neutrophil Percentage-to-Albumin Ratio and Outcomes in Cardiac Intensive Care Unit Patients

Background The neutrophil percentage-to-albumin ratio (NPAR) is a systemic inflammation-based predictor associated with many diseases’ outcomes. Nevertheless, there are few studies on the relationship between NPAR and inflammatory markers, and more importantly, the prognostic value of NPAR in critically ill patients with cardiovascular disease (CVD) remains unknown. Methods The data of this retrospective cohort study were from the Medical Information Mart data for Intensive Care III database (MIMIC-III) and the Second Affiliated Hospital of Wenzhou Medical University. Linear regression, logistic regression model, and Cox regression model were used to assess the associations between NPAR levels and length of stay, renal replacement therapy (RRT) use, and 30-day, 90-day and one-year mortality, respectively. The Pearson correlation coefficient was used to present the correlation between NPAR and C-reactive protein (CRP). Results Our study included 1599 patients in MIMIC-III and 143 patients in the Second Affiliated Hospital of Wenzhou Medical University. The elevated NPAR was independently associated with increased 30-day, 90-day, and one-year all-cause mortality (adjusted HR, 95% CI:1.51 (1.02–2.24); 1.61 (1.14–2.28); 1.53 (1.15–2.03); P trend = 0.0297; 0.0053; 0.0023; respectively), and it was also associated with increase the length of stay in hospital and ICU (β, 95% CI: 2.76 (1.26–4.27); 1.54 (0.62–2.47), respectively, both P trend <0.001). We found that patients with higher NPAR were more likely to receive RRT (OR, 95% CI: 2.50 (1.28–4.89), P trend =0.0023). Moreover, we confirmed that NPAR was statistically positively correlated with CRP (correlation coefficient r = 0.406, P < 0.0001). Conclusion Elevated NPAR on admission was independently associated with increased all-cause mortality and length of stay among CICU patients. The results showed that CICU patients with higher NPAR were more likely to receive RRT. Besides, we also provided the evidence that there is a positive correlation between NPAR and inflammatory indicators (ie, CRP).
Predicting affinity ties in a surname network

From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago.
Endogenous Taurine Downregulation Is Required for Renal Injury in Salt-Sensitive Hypertensive Rats via CBS/H2S Inhibition

Although taurine is known to exert an antihypertensive effect, it is unclear whether it is involved in the mechanism for hypertension-related target organ injury. To reveal the role of endogenous taurine in renal injury formation during salt-sensitive hypertension and clarify its mechanisms, both salt-sensitive Dahl rats and salt-resistant SS-13BN rats were fed a high-salt diet (8% NaCl) and given 2% taurine for 6 weeks. Rat systolic blood pressure (SBP) was measured by the tail-cuff method and artery catheterization. Kidney ultrastructure was observed under an electron microscope. Taurine content and mRNA and protein levels of taurine synthases, cysteine dioxygenase type 1 (CDO1) and cysteine sulfinic acid decarboxylase (CSAD), were decreased in Dahl rats fed a high-salt diet. However, taurine supplementation and the resulting increase in renal taurine content reduced the increased SBP and improved renal function and structural damage in high-salt diet-fed Dahl rats. In contrast, taurine did not affect SS-13BN SBP and renal function and structure. Taurine intervention increased the renal H2S content and enhanced cystathionine-β-synthase (CBS) expression and activity in Dahl rats fed a high-salt diet. Taurine reduced the renin, angiotensin II, and aldosterone contents and the levels of oxidative stress indices in Dahl rat renal tissues but increased antioxidant capacity, antioxidant enzyme activity, and protein expression. However, taurine failed to achieve this effect in the renal tissue of SS-13BN rats fed a high-salt diet. Pretreatment with the CBS inhibitor HA or renal CBS knockdown inhibited H2S generation and subsequently blocked the effect of taurine on renin, superoxide dismutase 1 (SOD1), and superoxide dismutase 2 (SOD2) levels in high-salt-stimulated Dahl renal slices. In conclusion, the downregulation of endogenous taurine production resulted in a decrease in the renal CBS/H2S pathway. This decrease subsequently promoted renin-angiotensin-aldosterone system (RAAS) activation and oxidative stress in the kidney, ultimately contributing to renal injury in salt-sensitive Dahl rats.
Exploring the topology and dynamic growth properties of co-invention networks and technology fields

This study investigates the topology and dynamics of collaboration networks that exist between inventors and their patent co-authors for patents granted by the USPTO from 2007–2019 (2,241,201 patents and 1,879,037 inventors). We study changes in the configurations of different technology fields via the power-law, small-world, preferential attachment, shrinking diameter, densification law, and gelling point hypotheses. Similar to the existing literature, we obtain mixed results. Based on network statistics, we argue that the sudden rise of large networks in six technology sectors can be understood as a phase transition in which small, isolated networks form one giant component. In two other technology sectors, such a transition occurred much later and much less dramatically. The examination of inventor networks over time reveals the increased complexity of all technology sectors, regardless of the individual characteristics of the network. Therefore, we introduce ideas associated with the technological diversification of inventors to complement our analysis, and we find evidence that inventors tend to diversify into new fields that are less mature. This behavior appears to be correlated with the compliance of some of the expected network rules and has implications for the emerging patterns among the different collaboration networks under consideration here.
Pharmacist Provided Spirometry Services: A Scoping Review

Purpose Despite international guidelines’ recommendations, spirometry is underutilized in the diagnosis and management of asthma and COPD. Spirometry may be an opportunity for trained pharmacists to meet the needs of patients with suspected or diagnosed lung conditions. The aim of this scoping review is to describe the literature including pharmacist provided spirometry services, specifically to identify: 1) the models of pharmacist provided spirometry services, and additional services commonly offered alongside spirometry, 2) pharmacist training and capability to obtain quality results, and (3) pharmacist, physician, and patient perspectives. Methods In September 2020, a comprehensive literature search in PubMed and EMBASE was conducted to identify all relevant literature on the topic of pharmacist provided spirometry services using the search term: “pharmacist or pharmacy” and “spirometry or pulmonary function test or lung function test. ” Literature was screened using inclusion/exclusion criteria and selected articles were charted and analyzed using the themes above. Results A total of 27 records were included. The scoping review found that pharmacist provided spirometry has been conducted around the world in community pharmacies and clinic settings. Community pharmacists may increase access to spirometry screening; the lack of communication with primary care providers and remuneration are barriers that need to be overcome to optimize the utility of the service. Clinic-based services are interprofessional and collaborative, allowing a patient to receive the test, results, diagnosis, and medication changes in one visit. Following comprehensive training, pharmacists felt confident in their ability to perform spirometry and met quality standards at acceptable rates. Conclusion Spirometry is an opportunity for pharmacists to improve evidence-based practice for screening and diagnosing lung conditions along with providing comprehensive services to complement testing. Data around provider and patient perspectives is limited and should be further investigated to determine if providers and patients would value and collaborate with pharmacists providing spirometry services.
Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants’ responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.
Risk factors associated with the development of delirium in general ICU patients. A prospective observational study

Objective We aimed to analyze risk factors related to the development of delirium, aiming for early intervention in patients with greater risk. Material and methods Observational study, including prospectively collected patients treated in a single general ICU. These were classified into two groups, according to whether they developed delirium or not (screening performed using CAM-ICU tool). Demographics and clinical data were analyzed. Multivariate logistic regression analyses were performed to quantify existing associations. Results 1462 patients were included. 93 developed delirium (incidence: 6.3%). These were older, scored higher on the Clinical Frailty Scale, on the risk scores on admission (SAPS-3 and SOFA), and had a greater number of organ failures (OF). We observed more incidence of delirium in patients who (a) presented more than two OF (20.4%; OR 4.9; CI95%: 2.9–8.2), and (b) were more than 74 years old albeit having <2 OF (8.6%; OR 2.1; CI95%: 1.3–3.5). Patients who developed delirium had longer ICU and hospital length-of-stays and a higher rate of readmission. Conclusions The highest risk observed for developing delirium clustered in patients who presented more than 2 OF and patients over 74 years old. The detection of patients at high risk for developing delirium could imply a change in management and improved quality of care.
Predictors of Health-Related Quality of Life in Patients with Ankylosing Spondylitis in Southwest China

Purpose The purpose of this study was to evaluate health-related quality of life (QoL) and explore its predictors in patients with ankylosing spondylitis (AS) in Southwest China. Patients and Methods We recruited AS patients from a tertiary hospital in Chengdu, China. Data were collected by self-reported questionnaires, including sociodemographic and disease-related variables, the Medical Outcomes Study 36-Item Short Form (SF-36), Beck Depression Inventory-Second Edition (BDI-II), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), and Bath Ankylosing Spondylitis Global score (BAS-G). Stepwise multiple linear regression analysis was used to determine the factors affecting physical component summary (PCS) and mental component summary (MCS) of SF-36. Results A total of 125 patients with AS were included in the current study. The PCS, MCS scores of SF-36 were 41.06±9.12, 47.82±9.84, respectively. Stepwise multiple linear regression analysis showed that higher educational level (β=0.237, P<0.001) and income (β=0.141, P=0.017), lower BASDAI (β=−0.195, P=0.006), BASFI (β=−0.317, P<0.001) and BAS-G (β=−0.288, P<0.001) scores were associated with higher PCS scores of SF-36. Higher BDI-II (β=−0.444, P<0.001) and fatigue (β=−0.293, P<0.001) scores were associated with worse MCS scores of SF-36. Conclusion AS patients in Southwest China had impaired health-related QoL. Healthcare providers should take effective strategies to modify the factors affecting health-related QoL, which may prompt disease management and increase QoL.
Mathematically aggregating experts’ predictions of possible futures

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the “best” final prediction. When experts’ performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts’ estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.
Physic-chemical characteristics and sensory evaluation of cakes with Nile tilapia (Oreochromis niloticus) waste flour

The fish processing industry generates a large amount of waste, that can be a nutrients source to the foods enrichment, such as cake that has low proteins, and consumed for different social classes. This study aimed to produced cakes with Nile tilapia (Orechromis niloticus) waste flour (NWF), formed by head, fins and the spine with meat remaining from filleting process, and evaluate its chemical and sensory properties. The formulations cakes with NWF obtained products with higher protein contents, essential fatty acids, and minerals, especially calcium and phosphorus, and lower car-bohydrates contents in relation to Control formulation (without NWF). The cakes with NWF had sensory acceptance (scores higher than 7.0 from 9.0 points) and purchase intention (scores higher than 3.8 from 5.0 points) and did not differ from the Control. The cakes with NWF can be a way to increase the supply of essential nutrients to the population through a popular and sensorial accepted product. This study showed that Nile tilapia waste flour (NWF) heads stands out a promising source for producing cake with high nutritional quality, positive economic potential, better physicochemical properties and, food industry solid waste reduction by used in food.
A study on the extraction of characteristics of compound faults of rolling bearings based on ITD-AF-CAF

In view of the cyclostationary characteristics of vibration signals from aero-engine, the combination of cyclic autocorrelation function and intrinsic timescale decomposition (ITD) has been proposed. According to the proposed method, vibration signals are decomposed by ITD algorithm to obtain the autocorrelation function of proper rotation components (PRC), based on which characteristic extraction and identification of compound faults of rolling bearings is made possible. To validate the effectiveness of method, an analysis has been given to the vibration signals of rolling bearings collected by sensors of different positions in different compound fault modes. As shown by results, the method combining ITD and cyclostationary theory can precisely and effectively extract the characteristic frequency relative to the type of faults and identify the compound faults.
Electrochemical Techniques as a Tool for Catalysts' Characterization: VIMP and SECM – A Provocative Approach

Two recently developed electrochemical techniques are presented as tools for the characterization of solid surfaces with possible catalytic or electrocatalytic capacities. The techniques are the voltammetry of immobilized micro particles (VIMP) and the electrochemical scanning microscopy (SECM). The VIMP is a simple and economical technique that only requires basic electrochemical instrumentation. Higher sophistication is possible with relatively low investments for non-specialized laboratories. The SECM requires more investment and technical advice because it requires a somewhat sophisticated instrumentation not usually found in an electrochemical laboratory. However, associated costs are relatively low compared to other solid surface exploration techniques. The historical development, improvements over time, and some applications for surface characterization are presented for each technique. Finally, some cases of coupling with other techniques allowing the expansion of the capacities for the topographical and reactivity characterization of solid surfaces are briefly presented.
How the occupational balance of healthcare professionals changed in the COVID‐19 pandemic: A mixed design study

Abstract Background Although it has been possible for many people to make a gradual transition to normal life or routine activities, the same seems far off for healthcare professionals. The current study examines in depth how the occupational balance of healthcare professionals has changed in the COVID‐19 pandemic. Methods The current study has a mixed design, which involves collecting both qualitative and quantitative data. In the first stage of the study, which is the quantitative one, Turkish Occupational Balance Questionnaire (OBQ11‐T) was used to collect data, whereas the second stage of the study, which is the qualitative one, was designed to explore occupational balance and the related issues via using semi‐structured interviews. The Mann–Whitney U test was used to compare parameters between the groups (working in active–passive contact with COVID patients). On the other hand, qualitative data were evaluated via consensual qualitative data analysis. Results The level of occupational balance of healthcare professionals included in the study group was found to be significantly lower than the level of occupational balance of healthcare professionals included in the control group (P = .005). Although there was no clear problem in self‐care activities of healthcare professionals, the balance between productivity and leisure time activities was disrupted. Conclusions Occupational balance and leisure time use of healthcare professionals, especially those who have active contact with COVID patients, have been affected. A further investigation can be carried out according to gender, age, and other demographic qualities.