Browse Articles

Advanced solar radiation prediction using combined satellite imagery and tabular data processing

Abstract Accurate solar radiation prediction is crucial for optimizing solar energy systems. There are two types of data that can be used to predict solar radiation, such as satellite images and tabular satellite data. This research focuses on enhancing solar radiation prediction by integrating data from two distinct sources: satellite imagery and ground-based measurements. By combining these datasets, the study improves the accuracy of solar radiation forecasts, which is crucial for renewable energy applications. This research presents a hybrid methodology to predict the solar radiation from both satellite images and satellite data. The methodology basis on two datasets; the first data set contains tabular data, and the second dataset contains satellite images. The framework divides into two paths; the first path take the input as the satellite images; this stages contains three steps; the first step is removing noise using latent diffusion model, the second step is about pixel imputation using a modified RF + Identity GAN (this model contains two modification the first modification is adding the identity block to solve mode collapse problem in the GANs and the second modification is to add the 8-connected pixel to generate a value of missing pixel near to the real missed pixel. The third step in the first path is about using the self-organizing map to identify the special informative in the satellite image. The second path take the input as tabular data and use the diffusion model to impute the missing data in the tabulated data. Finally, we merge the two path and use feature selection to be as input for the LSTM for solar radiation predictions. The experiments done prove the efficiency of the used stage such as missing pixel imputation, removing noise, missing data imputation and prediction using LSTM when compared with other available techniques. The experiments also prove the enhancement of all prediction model after adding two paths before the prediction step.

Three-dimensional genome structures of single mammalian sperm

Research on object detection and recognition in remote sensing images based on YOLOv11

Lunar primitive mantle olivine returned by Chang’e-6

Abstract The lunar mantle is important for unraveling the Moon’s formation and early differentiation processes. Here, we identify primitive lunar olivines in soils returned by the Chang’e-6 mission. These olivines have oxygen isotopic compositions plotting along the terrestrial fractionation line, and are characterized by high forsterite contents up to 95.6, and a broad range of nickel abundances from zero to 682 ppm. While the low-nickel (zero to 251 ppm), forsteritic olivines align with a Mg-suite origin, the most primitive, high-nickel olivines (337 to 682 ppm) have a different origin. They could be either the first olivine crystallized from the Lunar Magma Ocean (LMO) with an Earth-like initial composition, or crystallized from a hitherto unrecognized ultra-magnesian lava produced by extensive melting of the early LMO cumulate. The exposure of these mantle olivines was facilitated by their entrainment in ascending high-Mg lavas and conveyed to the surface at the South Pole-Aitken Basin.

Visual and refractive outcome, higher order aberrations and complications following intraocular lens exchange in eyes without comorbidities

Abstract Intraocular lens (IOL) exchange is performed to treat severe IOL-related complications. There is little published data on the impact of this procedure on the refractive outcomes and intra- and postoperative complications, especially in otherwise healthy eyes. We investigated the refractive outcome of IOL exchange surgery, including corneal aberrations; additionally, we assessed the influence of different IOL characteristics on intraoperative and postoperative complications. This prospective clinical study included 35 eyes with homogenous IOL calcification without other ocular pathologies. Using Pentacam AXL Wave (Oculus Optikgeräte GmbH, Wetzlar, Germany), corrected distance visual acuity (CDVA), target refraction compared to the postoperative spherical equivalent, corneal topography and lower and higher-order aberrations were assessed preoperatively and from 3 months after surgery. Intraoperative and postoperative complications were recorded and compared between different IOL characteristics. The secondary IOL in 53% of cases, was a retropupillary iris-fixated Artisan Aphakia (Ophtec BV, Groningen, Netherlands), 37% had a sulcus-fixated AR40e (Johnson & Johnson Vision, Irvine, USA), and 10% had a capsular bag IOL. The CDVA improved from 0.16 ± 0.14 to 0.07 ± 0.14 logMAR (p = 0.04). In most cases, the target refraction was within ± 1.0 D (Artisan: 71%, AR40e: 90%, Capsular: 100%). IOL exchange did not induce relevant change in corneal aberrations. Anterior (81%) and posterior (78%) vitrectomy were performed in most cases. The haptic design of the primary IOL did not impact intra- or postoperative complications. Although exchanging an IOL involves greater surgery compared to the initial IOL implantation, visual and refractive outcomes are good, and the exchange does not cause relevant change in aberrations. Intra- and postoperative complications are mostly mild and resolve without sequelae.

Single-Atom Saturation: A Fundamental Principle for Single-Atom-Site Catalyst Design

Border-zone cardiomyocytes and macrophages regulate extracellular matrix remodeling to promote cardiomyocyte protrusion during cardiac regeneration

Abstract Despite numerous advances in our understanding of zebrafish cardiac regeneration, an aspect that remains less studied is how regenerating cardiomyocytes invade and replace the collagen-containing injured tissue. Here, we provide an in-depth analysis of the process of cardiomyocyte invasion. We observe close interactions between protruding border-zone cardiomyocytes and macrophages, and show that macrophages are essential for extracellular matrix remodeling at the wound border zone and cardiomyocyte protrusion into the injured area. Single-cell RNA-sequencing reveals the expression of mmp14b, encoding a membrane-anchored matrix metalloproteinase, in several cell types at the border zone. Genetic mmp14b mutation leads to decreased macrophage recruitment, collagen degradation, and subsequent cardiomyocyte protrusion into injured tissue. Furthermore, cardiomyocyte-specific overexpression of mmp14b is sufficient to enhance cardiomyocyte invasion into the injured tissue and along the apical surface of the wound. Altogether, our data provide important insights into the mechanisms underlying cardiomyocyte invasion of the collagen-containing injured tissue during cardiac regeneration.

Author Correction: DNA sequencing at the picogram level to investigate life on Mars and Earth

Make-or-break prime editing for genome engineering in Streptococcus pneumoniae

Abstract CRISPR-Cas9 has revolutionized genome engineering by allowing precise introductions of DNA double-strand breaks (DSBs). However, genome engineering in bacteria is still a complex, multi-step process requiring a donor DNA template for repair of DSBs. Prime editing circumvents this need as the repair template is indirectly provided within the prime editing guide RNA (pegRNA). Here, we developed make-or-break Prime Editing (mbPE) that allows for precise and effective genetic engineering in the opportunistic human pathogen Streptococcus pneumoniae. In contrast to traditional prime editing in which a nicking Cas9 is employed, mbPE harnesses wild type Cas9 in combination with a pegRNA that destroys the seed region or protospacer adjacent motif. Since most bacteria poorly perform template-independent end joining, correctly genome-edited clones are selectively enriched during mbPE. We show that mbPE is RecA-independent and can be used to introduce point mutations, deletions and targeted insertions, including protein tags such as a split luciferase, at selection efficiencies of over 93%. mbPE enables sequential genome editing, is scalable, and can be used to generate pools of mutants in a high-throughput manner. The mbPE system and pegRNA design guidelines described here will ameliorate future bacterial genome editing endeavors.

Multifilament spinning of mechanically recycled polypropylene from post-consumer sources for a circular economy in textile applications

Abstract The use of polypropylene (PP) waste in the textile industry has been limited to low-value applications, as it is difficult to produce high-quality recycled fibers. Recent advances in recycling and purification technologies have enabled the production of recycled polypropylene (rPP) suitable for textile-grade fibers. This study focuses on the melt spinning of rPP multifilament yarns obtained from post-consumer waste and evaluates the influence of critical spinning parameters such as godet temperature, quenching conditions, draw ratio and draw down speed on yarn properties. The test results show that highly purified rPP can be successfully spun into multifilament yarns with a tensile strength of up to 4.2 cN/dtex and an elongation at break of 20%. These values demonstrate the potential of using post-consumer PP in high-performance applications, overcoming the traditional limitations associated with recycled PP. This work provides a pathway for expanding the use of post-consumer waste in the production of high value textile fibers and promotes a more sustainable approach to polypropylene waste management.

Frictiotaxis underlies focal adhesion-independent durotaxis

Abstract Cells move directionally along gradients of substrate stiffness — a process called durotaxis. In the situations studied so far, durotaxis relies on cell-substrate focal adhesions to sense stiffness and transmit forces that drive directed motion. However, whether and how durotaxis can take place in the absence of focal adhesions remains unclear. Here, we show that confined cells can perform durotaxis despite lacking focal adhesions. This durotactic migration depends on an asymmetric myosin distribution and actomyosin retrograde flow. We propose that the mechanism of this focal adhesion-independent durotaxis is that stiffer substrates offer higher friction. We put forward a physical model that predicts that non-adherent cells polarise and migrate towards regions of higher friction — a process that we call frictiotaxis. We demonstrate frictiotaxis in experiments by showing that cells migrate up a friction gradient even when stiffness is uniform. Our results broaden the potential of durotaxis to guide any cell that contacts a substrate, and they reveal a mode of directed migration based on friction. These findings have implications for cell migration during development, immune response and cancer progression, which usually takes place in confined environments that favour adhesion-independent amoeboid migration.

Using deep learning models to decode emotional states in horses

Abstract In this study, we explore machine learning models for predicting emotional states in ridden horses. We manually label the images to train the models in a supervised manner. We perform data exploration and use different cropping methods, mainly based on Yolo and Faster R-CNN, to create two new datasets: 1) the cropped body, and 2) the cropped head dataset. We train various convolutional neural network (CNN) models on both cropped and uncropped datasets and compare their performance in emotion prediction of ridden horses. Despite the cropped head dataset lacking important regions like the tail (commonly annotated by experts), it yields the best results with an accuracy of 87%, precision of 79%, and recall of 97%. Furthermore, we update our models using various techniques, such as transfer learning and fine-tuning, to further improve their performance. Finally, we employ three interpretation methods to analyze the internal workings of our models, finding that LIME effectively identifies features similar to those used by experts for annotation.

Costs of transitioning the livestock sector to net-zero emissions under future climates

Abstract Land managers are challenged with the need to balance priorities in production, greenhouse gas (GHG) abatement, biodiversity and social license to operate. Here, we develop a transdisciplinary approach for prioritising land use, illustrated by co-designing pathways for transitioning farming systems to net-zero emissions. We show that few interventions enhanced productivity and profitability while reducing GHG emissions. Antimethanogenic feed supplements and planting trees afforded the greatest mitigation, while revenue diversification with wind turbines and adoption of livestock genotypes with enhanced feed-conversion efficiency (FCE) were most conducive to improving profit. Serendipitously, the intervention with the lowest social licence—continuing the status quo and purchasing carbon credits to offset emissions—was also the most costly pathway to transition to net-zero. In contrast, stacking several interventions to mitigate enteric methane, improve FCE and sequester carbon entirely negated enterprise emissions in a profitable way. We conclude that costs of transitioning to net-zero are lower when interventions are bundled and/or evoke productivity co-benefits.

Identifying the richness and evolutionary relationships of Ranunculus sect. Batrachium in its diversity centre in south-western Europe

Polymer model integrates imaging and sequencing to reveal how nanoscale heterochromatin domains influence gene expression

Abstract Chromatin organization regulates gene expression, with nanoscale heterochromatin domains playing a fundamental role. Their size varies with microenvironmental stiffness and epigenetic interventions, but how these factors regulate their formation and influence transcription remains unclear. To address this, we developed a sequencing-informed copolymer model that simulates chromatin evolution through diffusion and active epigenetic reactions. Our model predicts the formation of nanoscale heterochromatin domains and quantifies how domain size scales with epigenetic reaction rates, showing that epigenetic and compaction changes primarily occur at domain boundaries. We validated these predictions via Hi-C and super-resolution imaging of hyperacetylated melanoma cells and identified differential expression of metastasis-related genes through RNA-seq. We validated our findings in hMSCs, where epigenetic reaction rates respond to microenvironmental stiffness. Conclusively, our simulations reveal that heterochromatin domain boundaries regulate gene expression and epigenetic memory. These findings demonstrate how external cues drive chromatin organization and transcriptional memory in development and disease.

Synthesis of polyaniline zirconium for purification of barberry anthocyanins and stabilization via copigmentation against thermal and gastrointestinal conditions

Nkx2.7 is a conserved regulator of craniofacial development

MiR-27a-3p protects against NaAsO2-induced neuronal apoptosis in HT22 cells

A 430 kyr record of ice-sheet dynamics and organic-carbon burial in the central Eurasian Arctic Ocean

Abstract Late Quaternary central Arctic Ocean paleoclimatic records going beyond the last climate cycle, are still very rare. Here, we present data from a well-dated deep-sea sediment core that allow for the first time to reconstruct in detail the interrelationship between ice-sheet dynamics and organic-carbon (OC) burial in the Eurasian Basin during the last 430 kyr, and to correlate marine and terrestrial records of the Eurasian Ice Sheet (EIS) history. Using a multi-proxy approach, we demonstrate that within pulses of EIS advance and retreat during glacial to subsequent deglacial times, erosion of ancient (petrogenic) OC-rich sedimentary rocks and soils deliver sediments, further supplied onto the shelf and beyond. Down-slope and long-distance transport of the fine-grained OC-rich suspension by turbidity and contour currents cause elevated burial of terrestrial OC and anoxic sedimentary conditions in the deep Eurasian Basin that allow preservation of labile algae-type OC.

Heterogeneous quantization regularizes spiking neural network activity

Abstract The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a capacity mediated by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities using a plastic network that requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy, based on the biological system architecture, that normalizes and quantizes analog data into spike-phase representations, thereby transforming uncontrolled sensory input into a regular form with minimal information loss. Normalized input is delivered to a column of spiking principal neurons via heterogeneous synaptic weights; this gain diversification strategy regularizes neuronal utilization, yoking total activity to the network’s operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. To dynamically optimize resource utilization while balancing activity regularization and resolution, we supplement this mechanism with a data-aware calibration strategy in which the range and density of the quantization weights adapt to accumulated input statistics.