Incorrect analysis in "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study" has resulted in incorrect conclusions of demonstrated effects where no such effects have been demonstrated.

In their 2023 Nutrition and Health paper "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study", Giacomello et al. investigated the effects of an educational intervention based on the Dietary Guidelines for the Brazilian Population among obese women. The authors concluded that the intervention significantly improved weight loss, quality of life, components of metabolic syndrome, and pain. However, we believe the statistical analysis employed in the study was flawed. The authors used within-group changes to draw conclusions, which is known as a difference in nominal significance error. This error has the potential to inflate Type I error rates substantially. To address this issue, we re-analyzed the data obtained from the authors. We focused on body mass and hip circumference and replicated the incorrectly chosen within-group analyses, which remained significant. However, to properly evaluate the intervention's effectiveness, it is essential to compare the differences between the groups directly. Therefore, we calculated change scores for each participant and used independent samples t-tests and linear mixed models to compare between-group differences. Both methods yielded similar non-significant p-values, indicating that there is no significant effect of treatment on body mass or hip circumference. The original paper's conclusions regarding the effectiveness of the intervention are not supported by the proper statistical analysis. The data should be re-analyzed using appropriate between-group comparisons, and the corrected results should be published, or the incorrect results and original paper should be retracted.

Towards efficient verifiable multi-keyword search over encrypted data based on blockchain.

Searchable symmetric encryption (SSE) provides an effective way to search encrypted data stored on untrusted servers. When the server is not trusted, it is indispensable to verify the results returned by it. However, the existing SSE schemes either lack fairness in the verification of search results, or do not support the verification of multiple keywords. To address this, we designed a multi-keyword verifiable searchable symmetric encryption scheme based on blockchain, which provides an efficient multi-keyword search and fair verification of search results. We utilized bitmap to build a search index in order to improve search efficiency, and used blockchain to ensure fair verification of search results. The bitmap and hash function are combined to realize lightweight multi-keyword search result verification, compared with the existing verification schemes using public key cryptography primitives, our scheme reduces the verification time and improves the verification efficiency. In addition, our scheme supports the dynamic update of files and realizes the forward security in update. Finally, formal security analysis proves that our scheme is secure against Chosen-Keyword Attacks (CKA), experimental analysis demonstrations that our scheme is efficient and viable in practice.

Asymmetric activation spreading in the multiplication associative network due to asymmetric overlap between numerosities semantic representations?

Simple multiplication facts are thought to be organised in a network structure in which problems and solutions are associated. Converging evidence suggests that the ability for solving symbolic arithmetic problems is based on an approximate number system (ANS). Most theoretical stances concerning the metric underlying the ANS converge on the assumption that the representational overlap between two adjacent numbers increases as the numerical magnitude of the numbers increases. Given a number N, the overlap between N and N+1 is larger than the overlap between N and N-1. Here, we test whether this asymmetric overlap influences the activation spreading within the multiplication associative network (MAN). When verifying simple multiplication problems such as 8×4 participants were slower in rejecting false but related outcomes that were larger than the actual outcome (e.g., 8×4=36) than rejecting smaller related outcomes (e.g., 8×4=28), despite comparable numerical distance from the correct result (here: 4). This effect was absent for outcomes which are not part of either operands table (e.g., 8×4=35). These results suggest that the metric of the ANS influences the activation spreading within the MAN, further substantiating the notion that symbolic arithmetic is grounded in the ANS.