One of the least understood properties of chromatin is the ability of its similar regions to recognize each other through weak interactions. Theories based on electrostatic interactions between helical macromolecules suggest that the ability to recognize sequence homology is an innate property of the non-ideal helical structure of DNA. However, this theory does not account for the nucleosomal packing of DNA. Can homologous DNA sequences recognize each other while wrapped up in the nucleosomes? Can structural homology arise at the level of nucleosome arrays? Here, we present a theoretical model for the recognition potential well between chromatin fibres sliding against each other. This well is different from the one predicted for bare DNA; the minima in energy do not correspond to literal juxtaposition, but are shifted by approximately half the nucleosome repeat length. The presence of this potential well suggests that nucleosome positioning may induce mutual sequence recognition between chromatin fibres and facilitate the formation of chromatin nanodomains. This has implications for nucleosome arrays enclosed between CTCF-cohesin boundaries, which may form stiffer stem-like structures instead of flexible entropically favourable loops. We also consider switches between chromatin states, e.g. through acetylation/deacetylation of histones, and discuss nucleosome-induced recognition as a precursory stage of genetic recombination.The spider major ampullate (MA) silk exhibits high tensile strength and extensibility and is typically a blend of MaSp1 and MaSp2 proteins with the latter comprising glycine-proline-glycine-glycine-X repeating motifs that promote extensibility and supercontraction. The MA silk from Darwin’s bark spider (Caerostris darwini) is estimated to be two to three times tougher than the MA silk from other spider species. Previous research suggests that a unique MaSp4 protein incorporates proline into a novel glycine-proline-glycine-proline motif and may explain C. darwini MA silk’s extraordinary toughness. However, no direct correlation has been made between the silk’s molecular structure and its mechanical properties for C. darwini. Here, we correlate the relative protein secondary structure composition of MA silk from C. darwini and four other spider species with mechanical properties before and after supercontraction to understand the effect of the additional MaSp4 protein. Our results demonstrate that C. darwini MA silk possesses a unique protein composition with a lower ratio of helices (31%) and β-sheets (20%) than other species. Before supercontraction, toughness, modulus and tensile strength correlate with percentages of β-sheets, unordered or random coiled regions and β-turns. However, after supercontraction, only modulus and strain at break correlate with percentages of β-sheets and β-turns. Our study highlights that additional information including crystal size and crystal and chain orientation is necessary to build a complete structure-property correlation model.Rapid and widespread implementation of infectious disease surveillance is a critical component in the response to novel health threats. Molecular assays are the preferred method to detect a broad range of viral pathogens with high sensitivity and specificity. click here The implementation of molecular assay testing in a rapidly evolving public health emergency, such as the ongoing COVID-19 pandemic, can be hindered by resource availability or technical constraints. We present a screening strategy that is easily scaled up to support a sustained large volume of testing over long periods of time. This non-adaptive pooled-sample screening protocol employs Bayesian inference to yield a reportable outcome for each individual sample in a single testing step (no confirmation of positive results required). The proposed method is validated using clinical specimens tested using a real-time reverse transcription polymerase chain reaction test for SARS-CoV-2. This screening protocol has substantial advantages for its implementation, including higher sample throughput, faster time to results, no need to retrieve previously screened samples from storage to undergo retesting, and excellent performance of the algorithm’s sensitivity and specificity compared with the individual test’s metrics.Optical flow algorithms have seen poor adoption in the biological community compared with particle image velocimetry for quantifying cellular dynamics because of the lack of proper validation and an intuitive user interface. To address these challenges, we present OpFlowLab, an integrated platform that integrates our motion estimation workflow. Using routines in our workflow, we demonstrate that optical flow algorithms are more accurate than PIV in simulated images of the movement of nuclei. Qualitative assessment with actual nucleus images further supported this finding. Additionally, we show that refinement of the optical flow velocities is possible with a simple object-matching procedure, opening up the possibility of obtaining reasonable velocity estimates under less ideal imaging conditions. To visualize velocity fields, we employ artificial tracers to allow for the drawing of pathlines. Through the adoption of OpFlowLab, we are confident that optical flow algorithms will allow for the exploration of dynamic biological systems in greater accuracy and detail.Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.