Development of Paper-Based Fluorescent Molecularly Imprinted Polymer Sensor for Rapid Detection of Lumpy Skin Disease Virus.

Lumpy Skin Disease (LSD) is a notifiable viral disease caused by Lumpy Skin Disease virus (LSDV). It is usually associated with high economic losses, including a loss of productivity, infertility, and death. LSDV shares genetic and antigenic similarities with Sheep pox virus (SPV) and Goat pox (GPV) virus. Hence, the LSDV traditional diagnostic tools faced many limitations regarding sensitivity, specificity, and cross-reactivity. Herein, we fabricated a paper-based turn-on fluorescent Molecularly Imprinted Polymer (MIP) sensor for the rapid detection of LSDV. The LSDV-MIPs sensor showed strong fluorescent intensity signal enhancement in response to the presence of the virus within minutes. Our sensor showed a limit of detection of 101 log10 TCID50/mL. Moreover, it showed significantly higher specificity to LSDV relative to other viruses, especially SPV. To our knowledge, this is the first record of a paper-based rapid detection test for LSDV depending on fluorescent turn-on behavior.

Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology.

Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.

Power-integrated, wireless neural recording systems on the cranium using a direct printing method for deep-brain analysis.

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation.

How to solve novel problems: the role of associative learning in problem-solving performance in wild great tits Parus major.

Although problem-solving tasks are frequently used to assess innovative ability, the extent to which problem-solving performance reflects variation in cognitive skills has been rarely formally investigated. Using wild breeding great tits facing a new non-food motivated problem-solving task, we investigated the role of associative learning in finding the solution, compared to multiple other non-cognitive factors. We first examined the role of accuracy (the proportion of contacts made with the opening part of a string-pulling task), neophobia, exploration, activity, age, sex, body condition and participation time on the ability to solve the task. To highlight the effect of associative learning, we then compared accuracy between solvers and non-solvers, before and after the first cue to the solution (i.e., the first time they pulled the string opening the door). We finally compared accuracy over consecutive entrances for solvers. Using 884 observations from 788 great tits tested from 2010 to 2015, we showed that, prior to initial successful entrance, solvers were more accurate and more explorative than non-solvers, and that females were more likely to solve the task than males. The accuracy of solvers, but not of non-solvers, increased significantly after they had the opportunity to associate string pulling with the movement of the door, giving them a first cue to the task solution. The accuracy of solvers also increased over successive entrances. Our results demonstrate that variations in problem-solving performance primarily reflect inherent individual differences in associative learning, and are also to a lesser extent shaped by sex and exploratory behaviour.

Linking fine root lifespan to root chemical and morphological traits-A global analysis.

Fine root lifespan is a critical trait associated with contrasting root strategies of resource acquisition and protection. Yet, its position within the multidimensional "root economics space" synthesizing global root economics strategies is largely uncertain, and it is rarely represented in frameworks integrating plant trait variations. Here, we compiled the most comprehensive dataset of absorptive median root lifespan (MRL) data including 98 observations from 79 woody species using (mini-)rhizotrons across 40 sites and linked MRL to other plant traits to address questions of the regulators of MRL at large spatial scales. We demonstrate that MRL not only decreases with plant investment in root nitrogen (associated with more metabolically active tissues) but also increases with construction of larger diameter roots which is often associated with greater plant reliance on mycorrhizal symbionts. Although theories linking organ structure and function suggest that root traits should play a role in modulating MRL, we found no correlation between root traits associated with structural defense (root tissue density and specific root length) and MRL. Moreover, fine root and leaf lifespan were globally unrelated, except among evergreen species, suggesting contrasting evolutionary selection between leaves and roots facing contrasting environmental influences above vs. belowground. At large geographic scales, MRL was typically longer at sites with lower mean annual temperature and higher mean annual precipitation. Overall, this synthesis uncovered several key ecophysiological covariates and environmental drivers of MRL, highlighting broad avenues for accurate parametrization of global biogeochemical models and the understanding of ecosystem response to global climate change.

Outside Europe.

This chapter illustrates that cranial surgery was not limited to Europe. There was however no contribution to improving knowledge outside Europe until medical science departed for the Arab world.

MAM-E: Mammographic Synthetic Image Generation with Diffusion Models.

Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high quality of the generated images, and their relatively less complex training process compared with Generative Adversarial Networks. Still, the implementation of such models in the medical domain remains at an early stage. In this work, we propose exploring the use of diffusion models for the generation of high-quality, full-field digital mammograms using state-of-the-art conditional diffusion pipelines. Additionally, we propose using stable diffusion models for the inpainting of synthetic mass-like lesions on healthy mammograms. We introduce MAM-E, a pipeline of generative models for high-quality mammography synthesis controlled by a text prompt and capable of generating synthetic mass-like lesions on specific regions of the breast. Finally, we provide quantitative and qualitative assessment of the generated images and easy-to-use graphical user interfaces for mammography synthesis.