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This paper addresses the complementarity and potential disparities between single-molecule and ensemble-average approaches to probe the binding mechanism of oligopeptides on inorganic solids. Specifically, we explore the peptide/gold interface owing to its significance in various topics and its suitability to perform experiments both in model and real conditions. Experimental results show that the studied peptide adopts a lying configuration upon adsorption on the gold surface and interacts through its peptidic links and deprotonated thiolate extremities, in agreement with theoretical predictions. Single-molecule force spectroscopy (SMFS) measurements revealed the existence of a wide panel of adhesion forces, resulting from the interaction between individual peptide moieties and the abundant surface sites. We therefore propose methodological developments for sorting the events of interest to understand the peptide adsorption mechanism. Thermodynamic and kinetic aspects of the peptide adsorption are probed using both static and dynamic force spectroscopy measurements. Specifically, we show the possibility of providing a reasonable estimate of the peptide free energy of adsorption Δ ads G ° by exploring the fluctuations of the adhesion work, based on the Jarzynski equality, and by using a parametric Gamma estimator. The proposed approach offers a relevant method for studying the different factors influencing the peptide adsorption and evaluating their impact on Δ ads G ° as an alternative to exploring adhesion forces that may lead to misinterpretations. This is illustrated by the comparison of the adsorption of two peptides with specific amino acids substitution. Our method provides insights into the overall mechanism by which peptides interact with the surface and allows an integration of the single-molecule versus ensemble-average points of view.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population., Competing Interests: Conflicts of interest. The authors declare no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) recommends an upper limit of 10% for atypia of undetermined significance (AUS). Recent data suggest that this category might be overused when the rate of cases with molecular positive results is low. As a quality metric, the AUS and positive call rates for this facility's cytology laboratory and each cytopathologist (CP) were calculated., Methods: A retrospective analysis of all thyroid cytology cases in a 4.5-year period was performed. Cases were stratified by TBSRTC, and molecular testing results were collected for indeterminate categories. The AUS rate was calculated for each CP and the laboratory. The molecular positive call rate (PCR) was calculated with and without the addition of currently negative to the positive results obtained from the ThyroSeq report., Results: A total of 7535 cases were classified as nondiagnostic, 7.6%; benign, 69%; AUS, 17.5%; follicular neoplasm/suspicious for follicular neoplasm, 1.4%; suspicious for malignancy, 0.7%; and malignant, 3.8%. The AUS rate for each CP ranged from 9.9% to 36.8%. The overall PCR was 24% (range, 13%-35.6% per CP). When including cases with currently negative results, the PCR increased to 35.5% for the cytology laboratory (range, 13%-42.6% per CP). Comparison analysis indicates a combination of overcalling benign cases and, less frequently, undercalling of higher TBSRTC category cases., Conclusions: The AUS rate in the context of PCR is a useful metric to assess cytology laboratory and cytopathologists' performance. Continuous feedback on this metric could help improve the overall quality of reporting thyroid cytology., (© 2024 The Authors. Cancer Cytopathology published by Wiley Periodicals LLC on behalf of American Cancer Society.)

Biological processes are intrinsically noisy, and yet, the result of development-like the species-specific size and shape of organs-is usually remarkably precise. This precision suggests the existence of mechanisms of feedback control that ensure that deviations from a target size are minimized. Still, we have very limited understanding of how these mechanisms operate. Here, we investigate the problem of organ size precision using the Drosophila eye. The size of the adult eye depends on the rates at which eye progenitor cells grow and differentiate. We first find that the progenitor net growth rate results from the balance between their proliferation and apoptosis, with this latter contributing to determining both final eye size and its variability. In turn, apoptosis of progenitor cells is hampered by Dpp, a BMP2/4 signaling molecule transiently produced by early differentiating retinal cells. Our genetic and computational experiments show how the status of retinal differentiation is communicated to progenitors through the differentiation-dependent production of Dpp, which, by adjusting the rate of apoptosis, exerts a feedback control over the net growth of progenitors to reduce final eye size variability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Navarro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM 2.5 , has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM 2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a "double jeopardy" for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain "hotspot" areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM 2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

We argue that theories and methods drawn from complexity science are urgently needed to guide the development and use of digital twins for cities. The theoretical framework from complexity science takes into account both the short-term and the long-term dynamics of cities and their interactions. This is the foundation for a new approach that treats cities not as large machines or logistic systems but as mutually interwoven self-organizing phenomena, which evolve, to an extent, like living systems., (© 2023. Springer Nature America, Inc.)

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

The main aim of the present paper is threefold. First, it aims at presenting an extended contact-based model for the description of the spread of contagious diseases in complex networks with consideration of asymptomatic evolutions. Second, it presents a parametrization method of the considered model, including validation with data from the actual spread of COVID-19 in Germany, Mexico and the United States of America. Third, it aims at showcasing the fruitful combination of contact-based network spreading models with a modern state estimation and filtering technique to (i) enable real-time monitoring schemes, and (ii) efficiently deal with dimensionality and stochastic uncertainties. The network model is based on an interpretation of the states of the nodes as (statistical) probability densities samples, where nodes can represent individuals, groups or communities, cities or countries, enabling a wide field of application of the presented approach., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier Ltd. All rights reserved.)