Model-based evaluation of alternative reactive class closure strategies against COVID-19

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0–26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Implementation of a Noise-Shaped Signaling System through Software-Defined Radio

Along with the development of electromagnetic weapons, Electronic Warfare (EW) has been rising as the future form of war. Especially in the area of wireless communications, high security defense systems such as Low Probability of Detection (LPD), Low Probability of Interception (LPI), and Low Probability of Exploitation (LPE) communication algorithms are being studied to prevent military force loss. One LPD, LPI, and LPE communication algorithm, physical-layer security, has been discussed and studied. We propose a noise signaling system, a type of physical-layer security, which modifies conventionally modulated I/Q data into a noise-like shape. To suggest the possibility of realistic implementation, we use Software-Defined Radio (SDR). Since there are certain hardware limitations, we present the limitations, requirements, and preferences of practical implementation of the noise signaling system. The proposed system uses ring-shaped signaling, and we present a ring-shaped signaling system algorithm, SDR implementation methodology, and performance evaluations of the system using the metrics of Bit Error Rate (BER) and Probability of Modulation Identification (PMI), which we obtain by using a Convolutional Neural Network (CNN) algorithm. We conclude that the ring-shaped signaling system can perform high LPI/LPE communication functioning because an eavesdropper cannot obtain the correct modulation scheme information. However, the performance can vary with the configurations of the I/Q data-modifying factors.

Probability of Reaction Pathways of Amine With Epoxides in the Reagent Ratio of 1:1 and 1:2

The algorithm for generating and estimating the probability of possible reaction pathways for multichannel bimolecular interactions was used to predict the reaction products in the reagent ratio of 1:1 and 1:2. Here we have considered the possible reaction pathways of the reaction of amine ((1S,2S,4S)-bicyclo[2.2.1]hept-5-en-2-ylmethanamine (1) with epoxides (2-((cyclohexyloxy)methyl)oxirane (2), 2-(phenoxymethyl)oxirane (3), (N-(oxiran-2-ylmethyl)-N-phenylbenzenesulfonamide 8) in order to explain experimental observed data, which indicate differences in the reactivity of glycidyl ethers and glycidylsulfonamide with framework amines. Based on the proposed algorithm [39], we have investigated the reaction in the reagent ratio of 1:1 and 1: 2. Calculated values of activation barriers indicate a low probability of formation of interaction products of amine (1) with epoxide (8) with a (1:2) reagent ratio due to steric hindrances in the reaction center.

Perceptions of Catastrophic Climate Risks

Many climate change-related risks, such as more frequent and severe natural disasters, can be characterised as low-probability/high-consequence (LP/HC) events. Perceptions of LP/HC risks are often associated with biases which hamper taking action to limit these risks, such as underestimation of risk, myopia, and the adoption of simplified decision heuristics. This chapter discusses these biases and outlines key elements of policies to overcome them in order to enhance climate action.

Information Asymmetry Among Investors and Strategic Bidding in Peer-to-Peer Lending

Peer-to-peer (P2P) lending became a global phenomenon in recent years. Despite their prominence in the “FinTech” era, P2P platforms remain a risky investment because of the high default rate of unsecured personal loans funded on such platforms. In contrast, the rate of return can be much higher than that of other investments if P2P loans are repaid. Therefore, investors of P2P loans need information about borrowers’ ability to repay. An important channel is to learn from other investors who may have information advantages. We argue that, because collective effort from investors is required in P2P lending, it could be optimal for informed investors to bid early in projects with the purpose of signaling the quality. With a unique data set from Prosper.com, we find that informed investors are indeed more likely to bid in the early stage of a project with a low probability of being funded, whereas uninformed investors will follow. The “squatting” behavior (early bidding) of informed investors facilitates information spillover to uninformed investors, benefitting the investors and borrowers who otherwise may not raise sufficient funding. Our findings also have implications for P2P lending platforms on how to manage the information asymmetry and strategic behaviors of investors.

Increasing transplantability in Brazil: time to discuss Kidney Paired Donation

Introduction: Kidney transplantation (KT) is the best treatment for chronic kidney disease. In Brazil, there are currently more than 26 thousand patients on the waitlist. Kidney Paired Donation (KPD) offers an incompatible donor-recipient pair the possibility to exchange with another pair in the same situation, it is a strategy to raise the number of KT. Discussion: KPD ceased being merely an idea over 20 years ago. It currently accounts for 16.2% of living donors KT (LDKT) in the USA and 8% in Europe. The results are similar to other LDKT. It is a promising alternative especially for highly sensitized recipients, who tend to accumulate on the waitlist. KPD is not limited to developed countries, as excellent results were already published in India in 2014. In Guatemala, the first LDKT through KPD was performed in 2011. However, the practice remains limited to isolated cases in Latin America. Conclusion: KPD programs with different dimensions, acceptance rules and allocation criteria are being developed and expanded worldwide to meet the demands of patients. The rise in transplantability brought about by KPD mostly meets the needs of highly sensitized patients. The Brazilian transplant program is mature enough to accept the challenge of starting its KPD program, intended primarily to benefit patients who have a low probability of receiving a transplant from a deceased donor.

Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models

The linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals (CIs) based on two bootstrap methods, applied to three robust estimation methods. We carried out a simulation experiment to compare CIs under three different conditions: data 1) without contamination, 2) contaminated by within-, or 3) between-participant outliers. Results showed that the semi-parametric bootstrap associated to the composite tau-estimator leads to valid inferential decisions with both uncontaminated and contaminated data. This being the most comprehensive study of CIs applied to robust estimators of the LMM, we provide fully commented R code for all methods applied to a popular example.

A Parametric Single-Case Analysis and Social Validation of the High-Probability Request Sequence

Noncompliance with adult instruction is a common problem exhibited by individuals diagnosed with intellectual and developmental disabilities. The high-probability (high-p) request sequence was designed to increase compliance with low-probability (low-p) instructions by rapidly presenting high-p instructions immediately prior to the targeted low-p instruction. This study evaluated the use of three different levels of the high-p request sequence (i.e., one instruction, three instructions, and six instructions) to increase the compliance of five children who were diagnosed with autism spectrum disorder (ASD). Results indicated that all three levels of the high-p request sequence were often successful in increasing compliance with low-p instructions; however, when given the opportunity to choose, participants and caregivers (i.e., mothers and therapists) tended to prefer the high-p request sequence with three instructions.

Monitoring of cyanobacteria using derivative spectrophotometry and improvement of the method detection limit by changing pathlength

Effective monitoring tools and methods are needed for the early detection and management of cyanobacteria in water bodies to minimize their harmful impacts on the environment and public health. This research investigated changing the cuvette pathlength (10-, 50-, and 100-mm) to improve the detection of cyanobacteria using UV-Vis spectrophotometry with subsequent application of derivative spectrophotometry and Savitzky-Golay (S-G) transformation. A non-toxigenic strain of blue-green cyanobacteria, Microcystis aeruginosa (CPCC 632), and a green algae strain for comparison, Chlorella vulgaris (CPCC 90), were studied in a wide range of concentrations (955,000–1,855 cells/mL). In each concentration range, method detection limits were established with absorbance measurements and S-G first derivative of absorbance using 10-, 50-, and 100-mm cuvette pathlengths. Increasing the cuvette pathlength from 10 to 100 mm resulted in a 15-fold improvement in sensitivity with absorbance and a 13-fold improvement with S-G first derivative of absorbance for M. aeruginosa. Overall, adoption of 100 mm pathlength and application of S-G derivative spectra improved the method detection limit for M. aeruginosa from 337,398 cells/mL to 4,916 cells/mL, which is below the WHO guideline for low probability of adverse health effects (<20,000 cells/mL). Similarly, the detection limit for C. vulgaris was improved from 650,414 cells/mL to 11,661 cells/mL. The results also showed that spectrophotometry could differentiate M. aeruginosa from C. vulgaris based on the variations in their pigment absorbance peaks.

Prenatal Diagnosis of Aortic Coarctation: Prediction Algorithm according to Gestational Age

<b><i>Objectives:</i></b> The aim of the study was first to quantify the diagnostic accuracy of predictive anatomical factors of aortic coarctation (CoA) and second to design a postnatal CoA probability algorithm according to gestational age (GA) in prenatal period. <b><i>Methods:</i></b> Global and according to GA diagnostic performance of cardiac anatomical variables using the ROC curve were evaluated in a retrospective cohort of fetuses with suspicion of CoA (2004–2020). A serial testing strategy to predict postnatal CoA by fetal echocardiography was designed. <b><i>Results:</i></b> 114 fetuses were included. Isthmus-to-ductal (I/D) ratio provided the best discrimination between healthy fetuses and those with CoA (AUC 0.91, 95% CI: 0.86–0.96, I/D &#x3c; 0.74 sensitivity 96.3%, I/D &#x3c; 0.6, specificity 92.5%) with good classification capacity in both the second and third trimesters of gestation. Isthmus <i>z</i>-score and pulmonary/aortic valve ratio increased accuracy in fetuses &#x3e;28 and tricuspid/mitral valve ratio (TV/MV) in fetuses ≤28 weeks. Study of I/D plus TV/MV ratio in fetuses ≤28 and I/D ratio plus isthmus <i>z</i>-scores in fetuses &#x3e;28 weeks allowed to correctly classify 91.8% of fetuses as high or low probability of postnatal CoA. <b><i>Conclusions:</i></b> Diagnostic discrimination of anatomic predictive factors for CoA varies according to GA. Specific algorithms according to GA increase accuracy in CoA’s prenatal prediction.