Even with the presence of AI technology, numerous ethical questions arise, encompassing concerns about individual privacy, data security, reliability, issues related to copyright/plagiarism, and the question of AI's capacity for independent, conscious thought. The recent surfacing of racial and sexual bias issues in AI has raised serious concerns about the reliability and dependability of AI. Late 2022 and early 2023 witnessed a surge in cultural awareness surrounding numerous issues, notably the rise of AI art programs (and accompanying copyright concerns stemming from their deep-learning training) and the popularity of ChatGPT, particularly due to its capacity to mimic human output, especially within academic contexts. Errors in AI applications can be life-threatening in fields like healthcare where accuracy is paramount. As AI permeates nearly every sector of our lives, we must continually ask ourselves: how much can we trust AI, and to what extent is it truly reliable? This editorial advocates for transparency and openness in the creation and application of artificial intelligence, ensuring all users understand both the positive and negative aspects of this pervasive technology, and explains how the Artificial Intelligence and Machine Learning Gateway on F1000Research facilitates this understanding.
Plant life significantly influences the exchange between the biosphere and atmosphere. This influence is particularly notable through the release of biogenic volatile organic compounds (BVOCs), which are precursors in the formation of secondary pollutants. The BVOC emissions from succulent plants, often selected for urban greening projects on building structures, are not fully understood. Using proton transfer reaction-time of flight-mass spectrometry, we investigated the CO2 absorption and BVOC release characteristics of eight succulents and one moss in a controlled laboratory environment. CO2 uptake by leaf dry weight fluctuated from 0 to 0.016 moles per gram per second, and concurrently, the net emission of biogenic volatile organic compounds (BVOCs) ranged from -0.10 to 3.11 grams per gram of dry weight per hour. Plant-to-plant variations were observed in the emission and removal of specific biogenic volatile organic compounds (BVOCs); methanol emerged as the dominant emitted BVOC, and acetaldehyde showed the greatest removal. When compared with other urban trees and shrubs, the isoprene and monoterpene emissions of the examined plants were relatively low, ranging from 0 to 0.0092 grams per gram of dry weight per hour for isoprene, and 0 to 0.044 grams per gram of dry weight per hour for monoterpenes. Succulents and moss species exhibited calculated ozone formation potentials (OFP) with a range of 410-7 to 410-4 grams of O3 per gram of dry weight daily. This research's outcomes can shape the selection criteria for plants utilized in urban greening initiatives. Based on per-leaf-mass analysis, Phedimus takesimensis and Crassula ovata demonstrate lower OFP values than numerous currently classified low OFP plants, presenting them as possible candidates for urban greening in ozone-prone areas.
The novel coronavirus, designated as COVID-19 and linked to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) family, was found in Wuhan, Hubei province, China, in November 2019. By the 13th of March in 2023, the disease had already infiltrated and infected more than 681,529,665,000,000 people. Therefore, early detection and diagnosis of COVID-19 are of paramount importance. To diagnose COVID-19, radiologists leverage medical imagery, such as X-rays and CT scans. The application of traditional image processing methods to automate radiologists' diagnostic procedures presents substantial hurdles for researchers. Finally, a novel deep learning model, utilizing artificial intelligence (AI), is designed for detecting COVID-19 from chest X-ray images. An automated COVID-19 detection system, WavStaCovNet-19, employing a wavelet transform and a stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19), analyzes chest X-ray images. Testing of the proposed work on two publicly accessible datasets yielded accuracies of 94.24% and 96.10% across 4 and 3 classes, respectively. Based on the experimental findings, we are confident that the proposed research will prove valuable in the healthcare sector for faster, more economical, and more precise COVID-19 detection.
When diagnosing coronavirus disease, chest X-ray imaging method takes the lead among all other X-ray imaging techniques. find more Among the body's organs, the thyroid gland stands out as particularly sensitive to radiation, especially in the context of infants and children. Consequently, chest X-ray imaging necessitates its protection. While the use of a thyroid shield in chest X-ray procedures holds both advantages and disadvantages, its application is currently a subject of discussion. Hence, this study aims to clarify the necessity of employing this protection during chest X-ray imaging. Employing both silica beads (thermoluminescent dosimeter) and an optically stimulated luminescence dosimeter, the study was conducted within an adult male ATOM dosimetric phantom. A portable X-ray machine was used to irradiate the phantom, employing thyroid shielding in a comparative manner, both with and without. The dosimeter readings confirmed a 69% reduction in radiation exposure to the thyroid gland using a shield, coupled with an additional 18% reduction without detriment to the radiographic image. In the context of chest X-ray imaging, the use of a protective thyroid shield is considered a prudent measure, as the benefits considerably exceed the potential risks.
For enhancing the mechanical properties of Al-Si-Mg casting alloys utilized in industrial applications, scandium proves to be the premier alloying element. Literature reviews frequently discuss the search for optimal scandium additions in a variety of commercially available aluminum-silicon-magnesium casting alloys with specific compositional characteristics. Optimization of the Si, Mg, and Sc components was not attempted, due to the daunting task of simultaneously analyzing a high-dimensional compositional space with constrained experimental data points. Within this paper, a novel alloy design methodology has been proposed and implemented to accelerate the discovery of hypoeutectic Al-Si-Mg-Sc casting alloys spanning a high-dimensional composition space. Calculations for phase diagrams using CALPHAD, aimed at establishing the quantitative link between composition, processing, and microstructure, were carried out for solidification simulations of hypoeutectic Al-Si-Mg-Sc casting alloys over a wide range of compositions. Secondly, a method of active learning combined with carefully structured experiments generated from CALPHAD and Bayesian optimization samplings elucidated the microstructural-mechanical properties relationship in Al-Si-Mg-Sc hypoeutectic casting alloys. A comparative assessment of A356-xSc alloys guided the design approach for high-performance hypoeutectic Al-xSi-yMg alloys, incorporating optimal levels of Sc, which were later corroborated experimentally. The present strategy was successfully broadened to select the ideal concentrations of Si, Mg, and Sc throughout the multifaceted hypoeutectic Al-xSi-yMg-zSc composition range. The integration of active learning with high-throughput CALPHAD simulations and key experiments in the proposed strategy is anticipated to be widely applicable for the effective design of high-performance multi-component materials within a high-dimensional compositional space.
A considerable portion of genomic material consists of satellite DNAs. find more Heterochromatic regions are often characterized by the presence of tandemly organized sequences, capable of amplification to create numerous copies. find more The atypical heterochromatin distribution of the *P. boiei* frog (2n = 22, ZZ/ZW), dwelling in the Brazilian Atlantic forest, presents sizable pericentromeric blocks on all chromosomes, unlike other anuran amphibians. Furthermore, Proceratophrys boiei females possess a metacentric sex chromosome W, exhibiting heterochromatin throughout its entirety. This study employed high-throughput genomic, bioinformatic, and cytogenetic approaches to examine the satellitome of P. boiei, driven by the substantial presence of C-positive heterochromatin and the marked heterochromatinization of the W sex chromosome. Upon completing the analyses, the satellitome of P. boiei stands out as remarkably composed of a high number of satDNA families (226), making P. boiei the frog species with the highest number of described satellite sequences currently known. The *P. boiei* genome contains a high proportion of repetitive DNAs, particularly satellite DNA, mirroring the observation of substantial centromeric C-positive heterochromatin blocks; this represents 1687% of the genome's composition. Through the use of fluorescence in situ hybridization, we accurately determined the chromosomal distribution of the two most prevalent repeats, PboSat01-176 and PboSat02-192, throughout the genome. The localization of these satDNA sequences in strategic regions like the centromere and pericentromere points to their essential contributions to genomic structure and function. A remarkable variety of satellite repeats, as revealed by our study, are instrumental in shaping the genomic organization of this frog species. Insights gleaned from the characterization and study of satDNAs in this frog species supported established principles in satellite biology and potentially connected their evolutionary trajectory to sex chromosome development, notably in anuran amphibians such as *P. boiei*, previously unexplored.
In head and neck squamous cell carcinoma (HNSCC), a significant feature of the tumor microenvironment is the abundant infiltration of cancer-associated fibroblasts (CAFs), which are critical to HNSCC's progression. Remarkably, some clinical trials aimed at targeting CAFs ultimately failed, and, counterintuitively, accelerated the progression of the cancer.