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Nymphal feeding suppresses oviposition-induced indirect plant defense in rice
Identifying preeclampsia-associated key module and hub genes via weighted gene co-expression network analysis
Bright and stable anti-counterfeiting devices with independent stochastic processes covering multiple length scales
AbstractPhysical unclonable functions (PUFs) are considered the most promising approach to address the global issue of counterfeiting. Current PUF devices are often based on a single stochastic process, which can be broken, especially since their practical encoding capacities can be significantly lower than the theoretical value. Here we present stochastic PUF devices with features across multiple length scales, which incorporate semiconducting polymer nanoparticles (SPNs) as fluorescent taggants. The SPNs exhibit high brightness, photostability and size tunability when compared to the current state-of-the-art taggants. As a result, they are easily detectable and highly resilient to UV radiation. By embedding SPNs in photoresists, we generate PUFs consisting of nanoscale (distribution of SPNs within microspots), microscale (fractal edges on microspots), and macroscale (random microspot array) designs. With the assistance of a deep-learning model, the resulting PUFs show both near-ideal performance and accessibility for general end users, offering a strategy for next-generation security devices.
Unpacking multidimensional effects of economic restructuring and technological progress on carbon emission performance: the moderating role of environmental regulation
Dual-targeting CRISPR-CasRx reduces C9orf72 ALS/FTD sense and antisense repeat RNAs in vitro and in vivo
AbstractThe most common genetic cause of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) is an intronic G4C2 repeat expansion in C9orf72. The repeats undergo bidirectional transcription to produce sense and antisense repeat RNA species, which are translated into dipeptide repeat proteins (DPRs). As toxicity has been associated with both sense and antisense repeat-derived RNA and DPRs, targeting both strands may provide the most effective therapeutic strategy. CRISPR-Cas13 systems mature their own guide arrays, allowing targeting of multiple RNA species from a single construct. We show CRISPR-Cas13d variant CasRx effectively reduces overexpressed C9orf72 sense and antisense repeat transcripts and DPRs in HEK cells. In C9orf72 patient-derived iPSC-neuron lines, CRISPR-CasRx reduces endogenous sense and antisense repeat RNAs and DPRs and protects against glutamate-induced excitotoxicity. AAV delivery of CRISPR-CasRx to two distinct C9orf72 repeat mouse models significantly reduced both sense and antisense repeat-containing transcripts. This highlights the potential of RNA-targeting CRISPR systems as therapeutics for C9orf72 ALS/FTD.
Influence of selected dosages of plastic microparticles on the porcine fecal microbiome
AbstractStudies conducted so far have shown that nano- and microplastic may disturb the intestinal microenvironment by interacting with the intestinal epithelium and the gut microbiota. Depending on the research model used, the effect on the microbiome is different—an increase or decrease in selected taxa resulting in the development of dysbiosis. Dysbiosis may be associated with intestinal inflammation, development of mental disorders or diabetes. The aim of the study was to analyze the intestinal microbiome in 15 gilts divided into 3 research groups (n = 5; control group, receiving micropartices at a dose 0.1 g/day (LD) and 1 g/day (HD)). Feaces were collected before and after 28 days of exposure to PET microplastics. The analysis of the intestinal microbiome was performed using next-generation sequencing. Alpha and beta diversity indices were compared, showing, that repetition affected only the abundance indices in the control and LD groups, but not in the HD group. The relationships between the number of reads at the phylum, genus and species level and the microplastic dose were calculated using statistical methods (r-Pearson correlation, generalized regression model, analysis of variance). The statistical analysis revealed, that populations of Family XIII AD3011 group, Coprococcus, V9D2013 group, UCG-010 and Sphaerochaeta increased with increasing MP-PET dose. The above-mentioned taxa are mainly responsible for the production of short-chain fatty acids (SCFA). It may be assumed, that SCFA are one of the mechanisms involved in the response to oral exposure to MP-PET.
Pegmatite lithium deposits formed within low-temperature country rocks
AbstractThe global climate crisis is likely to lead to a potential supply risk of lithium (Li) over the coming decades. More than half of the world’s production of Li is derived from Li-bearing pegmatites. Although pegmatites are widespread, only a small fraction host economically relevant Li mineralization. Revealing which factors cause some pegmatites to be enriched in Li and others to be barren is critical for understanding Li pegmatite formation and for guiding exploration for new Li resources. In this study, we used an approach involving the analysis of natural samples from the Jiajika pegmatite deposit (China), combined with thermal and diffusion modeling. Here we show that Li contents in pegmatites are controlled not only by the initial Li contents in pegmatite melts but also by the temperature of the surrounding country rocks at the time of pegmatite emplacement. Lithium-mineralized pegmatites form preferentially when Li-rich pegmatite melts intrude low-temperature country rocks.
Task relevant autoencoding enhances machine learning for human neuroscience
Abstract In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects’ behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects’ behavior rather than noise or other irrelevant factors. We thus developed a Task-Relevant Autoencoder via Classifier Enhancement (TRACE) designed to identify behaviorally-relevant target neural patterns. We benchmarked TRACE against a standard autoencoder and other models for two severely truncated machine learning datasets (to match the data typically available in functional magnetic resonance imaging [fMRI] data for an individual subject), then evaluated all models on fMRI data from 59 subjects who observed animals and objects. TRACE outperformed alternative models nearly unilaterally, showing up to 12% increased classification accuracy and up to 56% improvement in discovering “cleaner”, task-relevant representations. These results showcase TRACE’s potential for a wide variety of data related to human behavior.
Small variant benchmark from a complete assembly of X and Y chromosomes
Abstract The sex chromosomes contain complex, important genes impacting medical phenotypes, but differ from the autosomes in their ploidy and large repetitive regions. To enable technology developers along with research and clinical laboratories to evaluate variant detection on male sex chromosomes X and Y, we create a small variant benchmark set with 111,725 variants for the Genome in a Bottle HG002 reference material. We develop an active evaluation approach to demonstrate the benchmark set reliably identifies errors in challenging genomic regions and across short and long read callsets. We show how complete assemblies can expand benchmarks to difficult regions, but highlight remaining challenges benchmarking variants in long homopolymers and tandem repeats, complex gene conversions, copy number variable gene arrays, and human satellites.
Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory
Genetic code expansion reveals site-specific lactylation in living cells reshapes protein functions
Abstract Protein lactylation is an emerging field. To advance the exploration of its biological functions, here we develop a comprehensive workflow that integrates proteomics to identify lactylated sites, genetic code expansion (GCE) for the expression of site-specifically lactylated proteins in living cells, and an integrated functional analysis (IFA) platform to evaluate their biological effects. Using a combined wet-and-dry-lab proteomics strategy, we identify a conserved lactylation at ALDOA-K147, which we hypothesize plays a significant biological role. Expression of this site-specifically lactylated ALDOA in mammalian cells reveals that this modification not only inhibits enzymatic activity but also induces gain-of-function effects. These effects reshaped ALDOA functionality by enhancing protein stability, promoting nuclear translocation, regulating adhesion-related gene expression, altering cell morphology and modulating ALDOA-interacting proteins. Our findings highlight the utility of the GCE-based workflow in establishing causal relationships between specific lactylation events and both target-specific and cell-wide changes, advancing our understanding of protein lactylation’s functional impact.
hsa_circ_0008305 facilitates the malignant progression of hepatocellular carcinoma by regulating AKR1C3 expression and sponging miR-379-5p
Interconnected pathways link faecal microbiota plasma lipids and brain activity to childhood malnutrition related cognition
Artificial intelligence empowered voice generation for amyotrophic lateral sclerosis patients
Stereo-seq of the prefrontal cortex in aging and Alzheimer’s disease
Copper and nickel doped carbon dots for rapid and sensitive fluorescent turn-off detection of bilirubin
A tellurium iodide perovskite structure enabling eleven-electron transfer in zinc ion batteries
Characterizing Grover search algorithm on large-scale superconducting quantum computers
AbstractQuantum computing is on the cusp of transforming the way we tackle complex problems, and the Grover search algorithm exemplifying its potential to revolutionize the search for unstructured large datasets, offering remarkable speedups over classical methods. Here, we report results for the implementation and characterization of a three-qubit Grover search algorithm using the state-of-the-art scalable quantum computing technology of superconducting quantum architectures. To delve into the algorithm’s scalability and performance metrics, our investigation spans the execution of the algorithm across all eight conceivable single-result oracles, alongside nine two-result oracles, employing IBM Quantum’s 127-qubit quantum computers. Moreover, we conduct five quantum state tomography experiments to precisely gauge the behavior and efficiency of our implemented algorithm under diverse conditions – ranging from noisy, noise-free environments to the complexities of real-world quantum hardware. By connecting theoretical concepts with real-world experiments, this study not only shed light on the potential of Noisy Intermediate-Scale Quantum Computers in facilitating large-scale database searches but also offer valuable insights into the practical application of the Grover search algorithm in real-world quantum computing applications.