Gain and loss of function CFTR alleles modulate COVID-19 clinical outcome

Background/Objectives: We previously demonstrated that carrying a single pathogenic CFTR allele increases the risk for COVID-19 severity and mortality rate. We now aim to clarify the role of several uncharacterized rare alleles, including complex (cis) alleles, and in trans combinations. Method(s): LASSO logistic regression was used for the association of sets of variants, stratified by MAF, with severity. Immortalized cystic fibrosis bronchial epithelial cell lines and Fischer Rat Thyroid cells were transfected by plasmid carrying specific CFTR mutations. YFP-based assays were used to measure CFTR activity. Result(s): Here we functionally demonstrate that the rare (MAF=0.007) complex G576V/R668C allelemitigates the disease by a gain of function mechanism. Several novel CFTR ultra-rare (MAF <0.001) alleles were proved to have a reduced function;they are associated with disease severity either alone (single or complex alleles) or with another hypomorphic allele in the second chromosome, with a global reduction of CFTR activity between 40 to 72%. Conclusion(s): CFTR is a bidirectional modulator of COVID-19 outcome. At-risk subjects do not have open cystic fibrosis before viral infection and therefore are not easily recognisable in the general population unless a genetic analysis is performed. As the CFTR activity is partially retained, CFTR potentiator drugs could be an option as add-on therapy for at-risk patients.

Diagnostic Performance of Human Readers and Artificial Intelligence Model in Classification Task of Computed Tomography Images of Different Types of Viral Pneumonia

Purposes: Artificial Intelligence (AI) models are constantly developing to help clinicians in challenging tasks such as classification of images in radiological practice. The aim of this work was to compare the diagnostic performance of an AI classifier model developed in our hospital with the results obtained from the radiologists reading the CT images in discriminating different types of viral pneumonia. Material(s) and Method(s): Chest CT images of 1028 patients with positive swab for SARS-CoV-2 (n=646) and other respiratory viruses (n=382) were segmented automatically for lung extraction and Radiomic Features (RF) of first (n=18) and second (n=120) order were extracted using PyRadiomics tools. RF, together with patient age and sex, were used to develop a Multi-Layer Perceptron classifier to discriminate images of patients with COVID-19 and non-COVID-19 viral pneumonia. The model was trained with 808 CT images performing a LASSO regression (Least Absolute Shrinkage and Selection Operator), a hyper-parameter tuning and a final 4-fold cross validation. The remaining 220 CT images (n=151 COVID-19, n=69 non-COVID-19) were used as independent validation (IV) dataset. Four readers (three radiologists with >10 years of experience and one radiology resident with 3 years of experience) were recruited to blindly evaluate the IV dataset using the 5-points scale CO-RADS score. CT images with CO-RADS >=3 were considered "COVID-19". The same images were classified as "COVID-19" or "non-COVID-19" by applying the AI model with a threshold on the predicted values of 0.5. Diagnostic accuracy, specificity, sensibility and F1 score were calculated for human readers and AI model. Result(s): The AI model was trained using 24 relevant features while the Area under ROC curve values after 4-fold cross validation and its application to the IV dataset were, respectively, 0.89 and 0.85. Interreader agreement in assigning CO-RADS class, analyzed with Fleiss' kappa with ordinal weighting, was good (k=0.68;IC95% 0.63-0.72) and diagnostic performance were then averaged among readers. Diagnostic accuracy, specificity, sensibility and F1 score resulted 78.6%, 78.3%, 78.8% and 78.5% for AI model and 77.7%, 65.6%, 83.3% and 72.0% for human readers. The difference between specificity and sensitivity observed in human readers could be related to the higher rate of false positive due to the higher incidence of COVID-19 patients in comparison with other types of viral pneumonitis during the last 2 years. Conclusion(s): A model based on RF and artificial intelligence provides comparable results with human readers in terms of diagnostic performance in a classification task.Copyright © 2023 Southern Society for Clinical Investigation.

Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for the target knowledge-concept was first performed using Elastic Net and Random Forest algorithms, which are known to have the best performance in machine learning. Deep knowledge tracing (DKT) in the form of a dual-net, which is more efficient because of the already slimmer data, was then applied to increase the accuracy of feature selection. The new approach, termed the optimal knowledge component extracting (OKCE) model, was proven to be superior to a feature reduction approach using only Elastic Net and Random Forest using both open and commercial datasets. Finally, the OKCE model showed a meaningful knowledge-concept graph that could help teachers in adaptive and personalized learning. © 2013 IEEE.

Estimating the Reciprocal of a Binomial Proportion

The binomial proportion is a classic parameter with many applications and has also been extensively studied in the literature. By contrast, the reciprocal of the binomial proportion, or the inverse proportion, is often overlooked, even though it also plays an important role in various fields. To estimate the inverse proportion, the maximum likelihood method fails to yield a valid estimate when there is no successful event in the Bernoulli trials. To overcome this zero-event problem, several methods have been introduced in the previous literature. Yet to the best of our knowledge, there is little work on a theoretical comparison of the existing estimators. In this paper, we first review some commonly used estimators for the inverse proportion, study their asymptotic properties, and then develop a new estimator that aims to eliminate the estimation bias. We further conduct Monte Carlo simulations to compare the finite sample performance of the existing and new estimators, and also apply them to handle the zero-event problem in a meta-analysis of COVID-19 data for assessing the relative risks of physical distancing on the infection of coronavirus. © 2023 The Authors. International Statistical Review published by John Wiley & Sons Ltd on behalf of International Statistical Institute.

Detection of COVID-19 in hospitalised patients by exhaled breath analysis

Introduction: SARS-COV-2 is mainly transmitted through respiratory droplets. The standard diagnostic procedure is based on a reverse transcription polymerase chain reaction (RT-PCR). Aim(s): 1) To develop a safe and easy to perform breath test for the detection of COVID-19 in hospitalised patients based on the analysis of volatile organic compounds (VOCs) in exhaled breath. 2) To differentiate in hospitalised patients with respiratory symptoms those with and without COVID-19. Method(s): We performed a monocenter, cross-sectional, case-control study in 38 subjects (63% males, age 62+/-12.7 yrs) admitted at the pulmonology ward. Breath samples were taken using a home-made sampling system. Analysis of breath samples was performed by proton transfer high resolution mass spectrometry (PTR-HRMS). A lassoregression with leave-one-out cross-validation was performed to differentiate the groups and designate the most differentiating VOCs. Result(s): COVID-19 positive (n=22) and control respiratory patients (n=16) were similar with respect to baseline characteristics, except for lower blood neutrophil and lymphocyte counts and higher ferritin level in COVID+ve patients (p<0.05). Lasso-regression revealed 6 VOCs as potential biomarkers that differentiated between both groups with 84% accuracy, 100% specificity and 100% positive predictive value based on PTR-HRMS data. Conclusion(s): Breath analysis could identify a breathprint differentiating between hospitalised COVID-19 and nonCOVID-19 patients with respiratory symptoms with a good accuracy. Therefore, VOCs profiling could be integrated in sensors allowing a fast breathalyzer for COVID-19 for large-scale screening.

Rethinking population shrinkage in the Australian context: a new research agenda

Whilst the study on the impact of shrinkage is well documented in North America and Europe, the effects of population-driven shrinkage on rural and regional communities in Australia is comparatively under-researched. This is despite existing literature on the volatility of population change in regional and rural Australia. Therefore, there is cause for establishing a typology of shrinkage in the Australian context, unpacking the different and complex economic, social and environmental causes and consequences, and therefore impacts, and establishing a framework for ongoing research. In this paper, we set out the rationale for this typology, indicating how population drivers are not only extensive, but further complicated by the as-yet-unknown impacts of COVID-19 and teleworking. Regarding policy solutions, we suggest that while mindsets are increasingly changing from a need to reverse population trends to, instead, embracing opportunities and alternative futures for many regional and rural Australian towns, we need to first establish a typology of population shrinkage that is reflective of the Australian context to ensure policy responses are locally appropriate. Practitioner pointers - Mindsets around planning policies on the impacts of population-driven shrinkage are beginning to shift towards understanding the specific socio-economic circumstances of the localised area and adopting appropriate policy instruments accordingly. - To support this nascent shift, establishing a typology of shrinkage that is reflective of the Australian context is key. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data.

BACKGROUND: This study aims to show that including pairwise hierarchical interactions of covariates and combining forecasts from individual models improves prediction accuracy. METHODS: The least absolute shrinkage and selection operator via hierarchical pairwise interaction is used in selecting variables that are not correlated and with the greatest predictive power in single forecast models (Gradient boosting method [GBM], Generalized additive models [GAMs], Support vector regression [SVR]) are used in the analysis. The best model was selected based on the mean absolute error (MAE), the best key performance indicator for skewed data. Forecasts from the 5 models were combined using linear quantile regression averaging (LQRA). Box and Whiskers plots are used to diagnose the overall performance of fitted models. RESULTS: Single forecast models (GBM, GAMs, and SVRs) show that including pairwise interactions improves forecast accuracy. The SVR model with interactions based on the radial basis kernel function is the best from single forecast models with the lowest MAE. Combining point forecasts from all the single forecast models using the LQRA approach further reduces the MAE. However, based on the Box and Whiskers plot, the SVR model with pairwise interactions has the smallest range. CONCLUSIONS: Based on the key performance indicators, combining predictions from several individual models improves forecast accuracy. However, overall, the SVM with pairwise hierarchical interactions outperforms all the other models.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Property assessment of an eco-friendly mortar reinforced with recycled mask fiber derived from COVID-19 single-use face masks

Wearing a face mask is strongly advised to prevent the spread of the virus causing the COVID-19 pandemic, though masks have produced a tremendous amount of waste. As masks contain polypropylene and other plastics products, total degradation is not achievable, and masks may remain in the form of microplastics for several years in the environment. Therefore, this urgent issue ought to be addressed by properly handling waste face masks to limit their environmental impact. In relation to this goal, a novel application of recycled mask fiber (MF) derived from COVID-19 single-use surgical face masks (i.e., shredded mask fiber-SMF and cut mask fiber-CMF) has been undertaken. Eighteen mortar mixes (9 for water and 9 for 10% CO2 concentration curing) were fabricated at 0%, 0.5%, 1.0%, 1.5%, and 2.0% of both SMF and CMF by volume of ordinary Portland cement-based mortar. The compressive strength, flexural strength, ultrasonic pulse velocity, shrinkage, carbonation degree, permeable voids, and water absorption capabilities were assessed. The outcomes reveal that the compressive strength decreased with an increased percentage of MFs due to increased voids of the mixes with MFs as compared to a control mix. In contrast, significantly higher flexural strength was noted for the mortar with MFs, which is augmented with an increased percentage of MFs. Furthermore, the inclusion of MFs decreased the shrinkage of the mortar compared to the control mix. It was also found that MFs dramatically diminished the water absorption rate compared to the control mix, which reveals that MFs can enhance the durability of the mortar. © 2023 Elsevier Ltd

Analysis of product shrinkage and waste in a potato bagging plant

Nowadays, an average of 2 kg of waste per person are generated in Spain. Furthermore, the household consumption is rising and, as a consequence, the waste production is also increasing. This trend presents a direct impact in the environment. Moreover, after two years of COVID-19 pandemic, it has been detected a stronger rise in consumption per person, while consumption through professional commercial channels for hospitality industry has been lower. This paper analizes the waste generation and product shrinkage in a potato bagging plant, which addresses its production to both final consumers and retailers. The raw materials washing line, as well as the production line, are taken into consideration in the analysis, while new uses to the produced waste are proposed, aiming at providing new useful life, such as the production of bioplastics or the production of biodiesel. As a consequence, the environment impact is minimized and new products are obtained. © 2022 The Authors.

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients.

Estimation of age-dependent transmissibility of COVID-19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID-19 cases, we propose an ordinary differential equation model that considers age-dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi-source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age-structured data. The added values of accounting for age-dependent transmissibility are further evaluated through simulation studies. In real-data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients.

Development and Validation of Multivariable Prediction Models of Serological Response to SARS-CoV-2 Vaccination in Kidney Transplant Recipients

Background: Repeated vaccination against SARS-CoV-2 increases serological response in kidney transplant recipients (KTR) with high interindividual variability. Still, no decision support tool exists to predict SARS-CoV-2 vaccination response in KTR. Method(s): We developed, internally and externally validated five different multivariable prediction models of serological response after the third and fourth vaccine dose against SARS-CoV-2 in KTR. Using 27 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), LASSO LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 585 vaccinations were used. External validation was performed in four independent, international validation datasets comprising 191, 184, 254, and 321 vaccinations, respectively. Result(s): Internal validation using a rigorous resampling approach showed AUC-ROC of 0.825 for LASSO LR, which was then used for model fitting and external validation. LASSO LR performed on the whole development dataset yielded a 23- and 11-variable model, respectively. External validation showed ROC-AUC of 0.855, 0.749, 0.828, and 0.763 for the sparser 11-variable model, yielding an overall AUC-ROC of 0.809, and a negative predictive value of 0.752. The 23-variable model showed AUC-ROC of 0.853, 0.714, 0.844, and 0.778 in four independent validation sets, yielding an overall AUC-ROC of 0.818, and a negative predictive value of 0.795. Conclusion(s): Both, an 11- and 23-variable LASSO LR model predict vaccination response in KTR with good AUC-ROC. Implemented as an online tool at https://www.txvaccine. com, it can guide decisions when choosing between different immunization strategies to improve protection against COVID-19 in KTR. (Figure Presented).

EXTREMELY RARE CASE OF CARTILAGINOUS TUMOR IN THE POSTERIOR WALL OF THE TRACHEA

SESSION TITLE: Lung Cancer Case Report Posters 3 SESSION TYPE: Case Report Posters PRESENTED ON: 10/17/2022 12:15 pm - 01:15 pm INTRODUCTION: Tracheal tumor accounts for 0.4% of all tumors and only 10% of them are benign (1). We present, to our knowledge, the first case of a primary benign tracheal tumor with features of chondroid metaplasia arising from the posterior wall of the trachea. CASE PRESENTATION: 58-year-old male non—smoker with non-significant past medical history, presented to the Emergency department for COVID-19 pneumonia. CTA chest was done showing bilateral pulmonary embolism and a 12 mm polypoid tracheal mass arising from the posterior wall of the trachea extending into the lumen (Figure#1). The patient was asymptomatic prior to his COVID 19 infection;he denied any chest pain, hemoptysis, trauma, or prior intubation. After recovering from COVID-19, the patient was scheduled for an outpatient rigid bronchoscopy which revealed a tracheal polyp arising from the mid-distal posterior membranous trachea. (Figure#2). An electrocautery snare was used to simultaneously cut and cauterize the stalk using a lasso technique. The polyp was removed in its entirety without complication. Histopathology examination demonstrated a respiratory epithelium lined cyst with cartilaginous tissue, favoring chondroid metaplasia. DISCUSSION: Primary benign tracheal tumors with cartilaginous features are uncommon, especially in the posterior membrane of the trachea, which lacks cartilaginous support. Diagnosis of any benign tracheal tumor is usually delayed since most patients are asymptomatic. The majority of such tumors are found incidentally, as in this case. One of the most common benign tracheal tumors is hamartoma, which can have respiratory epithelium and cartilaginous tissue, however they do not have features of chondroid metaplasia, and are generally found in the lateral or anterior wall of the trachea. Furthermore, endobronchial lesions only account for 3% of all pulmonary hamartomas. (2) Reports of airway chondroid metaplasia are usually described in the larynx and are commonly associated with prior trauma or inflammation in the area which is not known to have occurred in this case (3). The histopathologic findings and unusual location of this tumor makes this case unique. CONCLUSIONS: The tracheal origin of this benign tumor, arising from the posterior membrane with cartilaginous features is extremely rare, and has not previously been described in the literature. Reference #1: Park CM, Goo JM, Lee HJ, Kim MA, Lee CH, Kang MJ. Tumors in the tracheobronchial tree: CT and FDG PET features. Radiographics. 2009 Jan-Feb;29(1):55-71. doi: 10.1148/rg.291085126. PMID: 19168836. Reference #2: Hurst IJ Jr, Nelson KG. Tracheal hamartoma. Chest. 1977 Nov;72(5):661-2. doi: 10.1378/chest.72.5.661. PMID: 913152. Reference #3: Orlandi A, Fratoni S, Hermann I, Spagnoli LG. Symptomatic laryngeal nodular chondrometaplasia: a clinicopathological study. J Clin Pathol. 2003 Dec;56(12):976-7. doi: 10.1136/jcp.56.12.976. PMID: 14645364;PMCID: PMC1770148. DISCLOSURES: No relevant relationships by Jorge Cedano Consultant relationship with Olympus America Please note: 8/1/21-present Added 04/18/2022 by Lucas Pitts, value=Consulting fee

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Association of Ventilator Settings and Lung Mechanics During VV-ECMO Treatment with Short Term Pulmonary Function and Lung Imaging Outcomes in COVID-19 Survivors, a Retrospective Cohort Study

Background: Lung protective ventilation plays a crucial role in the management of patients with COVID-19 ARDS treated with VV-ECMO. We hypothesized that increasingly protective ventilator settings may be associated with improved lung recovery by reducing ventilator induced lung injury. Method(s): We performed a retrospective cohort study of all patients treated with VV-ECMO for COVID-19 at NYU Langone Medical Center from March 2020 to June 2020. Ventilator data including tidal volume (cc/ kg predicted body weight), peak airway pressure, PEEP, Driving pressure (DP), Respiratory Rate, FiO2, lung compliance, and mechanical power were obtained. Pulmonary function test (PFT) results, 6-minute walk test results, and quantitative chest CT scores were obtained from the first outpatient follow up assessment. Bivariate and multivariate analysis correlating ventilator data with lung function and CT outcomes was performed. Result(s): 30 COVID-19 patients were treated with VV-ECMO during the study period, of which 26 survived without lung transplantation and 12 completed follow up assessment at a median of 106 days post ECMO decannulation. Multivariate LASSO regression model results;FEV1: DP (beta=-5.535), Respiratory Rate (beta=-0.370), compliance (beta=0.467), FVC: DP (beta=-4.08), compliance (beta=0.875), preECMO tidal volume (beta=-0.0008), TLC: DP (beta=-4.518), ECMO sweep (beta=-0.598), DLCO: peak airway presure (beta=-1.836), 6MWT distance: compliance (beta=1.436), Chest CT total opacity score: DP(beta=-0.60), preECMO tidal volume(beta=-0.0033). Conclusion(s): Driving pressure and peak airway pressure during VV-ECMO had the strongest associations with improved short-term follow up lung volumes, DLCO, and chest CT outcomes in VV-ECMO treated COVID-19 survivors.

A notched connection for clt-concrete composite slabs resisting to uplift without metallic connectors: Experimental investigation

In search for a solution of a sustainable construction with less impact on environment while maintaining a sufficient structural performance, CLT-concrete composite slabs/beams have been increasingly proposed for medium-to-large span structures. Different types of mechanical shear connectors have been studied in the literature for these composite elements. Among them, the notch type is the most preferable due to the high shear resistance contributed by the concrete. However, steel screw or bolt is needed in the connector to limit the uplift between the timber and the concrete. In this paper, a novel type of notched connectors with a particular shape that is able to limit the uplift without the need for steel bolts is proposed. The main objective of this paper is to determine the local and global behaviours of this new shear connector by experimental investigations. Two series of experimental tests were ordered by Thierry Soquet, an architect of Architecture Plurielle and an inventor of innovative construction systems directed by Horizon Bois. A series of three symmetrical push-out tests were performed on large-scale specimens in order to determine the shear resistance, the stiffness, the deformation capacity and the failure mode of the connector. The test results have shown high shear resistance and large stiffness of the connectors. However, the ductility of the connectors is still limited, as the failure mode was governed by the shear failure of the transverse layer of the CLT. In addition, the global behaviour of the CLT-concrete slab was assessed by a series of two full-scaled flexural tests on the slab specimens under a positive bending moment. It was shown in the test results that the design of the composite slab was not limited by the flexural bearing capacity as a high value of the maximum bending moment was obtained in the tests, but governed by the deflection of the composite slab. The delay in the tests caused by the Covid crisis has moreover set in evidence the importance of the shrinkage of concrete in the total deflection. © Fédération Internationale du Béton (fib) – International Federation for Structural Concrete.

USING MOBILE PHONES FOR COLLECTING HEALTH INFORMATION IN THE SUB SAHARAN AFRICA DURING THE COVID-19 PANDEMIC: TAKING LESSONS FROM THE WORLD DEVELOPMENT INDICATORS DATABASE

Objective The primary aim of this study was to portray the level of spread and the dynamic of diffusion of mobile phone technology in sub-Saharan Africa during the last two decades. The secondary aim was to investigate factors related to the use of mobile phone technology in sub-Saharan Africa and to derive profiles of the most suitable areas to conduct mobile phone technology-based research. Methods The present work was based on the data collected by the World Bank database;a collection of public access data derived from yearly surveys conducted at country level. Two methods were applied to perform the selection of variables related to the diffusion of mobile phones in sub-Saharan Africa. Firstly, a Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied. Afterwards, a system of simultaneous equation was applied to estimate the model coefficients and determine the joint statistical significance. Results The number of mobile phones subscriptions in relation to the population of sub-Saharan Africa has increased consistently during the period 2000 to 2010. The rate of mobile phones subscriptions in relation to the population ranged between less than 1% to more than 90%. Urban areas and having a lower number of people leaving in slums seems to be the most suitable places to conduct mobile phone-based interviews. This information is useful in identifying countries and macro areas to conduct mobile phone interviews;and this could be extended to smallest area within a country. Discussion More effort is required to better understand how to identify areas suitable for conducting research using mobile phones and other electronic-based tools. Such an effort should be based on individual level surveys to understand not only the material possibility but also the will to participate to research based on data capturing made by mobile phones and similar tools.

Macroeconomic attention and stock market return predictability

Our investigation evaluates the novel macroeconomic attention indices (MAI) of Fisher et al. (2021) in terms of their ability to predict stock market returns based on dimension reduction methods and shrinkage methods. Our results demonstrate that macroeconomic attention indices can predict stock market returns with a significant degree of accuracy. In addition, the components of MAI indices based on partial least squares (PLS) and the least absolute shrinkage and selection operator (LASSO) methods have a greater capacity to improve the accuracy of the prediction of stock market returns than the components of the traditional macroeconomic variables. Moreover, we find that shrinkage methods can generate performances superior to those of the other models for forecasting stock market returns. We further demonstrate that macroeconomic attention indices embody superior predictive ability during the COVID-19 pandemic and over longer periods of time. Our study sheds new light on stock market returns’ prediction from the perspective of macroeconomic fundamentals.