Search Results (12)

Continental rift zones on the early Earth provided essential conditions for the emergence of the first cells. These conditions included an abundant supply of raw materials, cyclic fluctuations in pressure and temperature over millions of years, and transitions of gases between supercritical and subcritical phases. While evidence supports vesicle formation and the chemical evolution of peptides, the mechanism by which information was stored remains unresolved. This study proposes a model illustrating how interactions among organic molecules may have enabled the encoding of amino acid sequences in RNA. The model highlights the interplay between three key molecular components: a proto-tRNA, the vesicle membrane, and short peptides. The vesicle membrane acted as a reservoir for hydrophobic amino acids and facilitated their attachment to proto-tRNA. As a single strand, proto-tRNA also served as proto-mRNA, enabling it to be read by charged tRNAs. By replicating this information and arranging RNA strands, the first functional peptides such as pore-forming proteins may have formed, thus improving the long-term stability of the vesicles. This model further outlines how these vesicles may have evolved into the earliest cells, with enzymes and larger RNA molecules giving rise to tRNA and ribosomal structures. Shearing forces may have facilitated the first cellular divisions, representing a pre-LUCA stage. Full article

Classification of remote sensing images using machine learning models requires a large amount of training data. Collecting this data is both labor-intensive and time-consuming. In this study, the effectiveness of using pre-existing reference data on land cover gathered as part of the Land Use–Land Cover Area Frame Survey (LUCAS) database of the Copernicus program was analyzed. The classification was carried out in Google Earth Engine (GEE) using Sentinel-2 images that were specially prepared to account for the phenological development of plants. Classification was performed using SVM, RF, and CART algorithms in GEE, with an in-depth accuracy analysis conducted using a custom tool. Attention was given to the reliability of different accuracy metrics, with a particular focus on the widely used machine learning (ML) metric of “accuracy”, which should not be compared with the commonly used remote sensing metric of “overall accuracy”, due to the potential for significant artificial inflation of accuracy. The accuracy of LUCAS 2018 at Level-1 detail was estimated at 86%. Using the updated LUCAS dataset, the best classification result was achieved with the RF method, with an accuracy of 83%. An accuracy overestimation of approximately 10% was observed when reporting the average accuracy ACC metric used in ML instead of the overall accuracy OA metric. Full article

The main objective of this research was to propose a smart technology to record and analyse the attention of operators of transportation devices where human–machine interaction occurs. Four simulators were used in this study: General Aviation (GA), Remotely Piloted Aircraft System (RPAS), AS 1600, and Czajka, in which a spatio-temporal trajectory of system operator attention describing the histogram distribution of cockpit instrument observations was sought. Detection of the position of individual instruments in the video stream recorded by the eyetracker was accomplished using a pre-trained Fast R-CNN deep neural network. The training set for the network was constructed using a modified Kanade–Lucas–Tomasi (KLT) algorithm, which was applied to optimise the labelling of the cockpit instruments of each simulator. A deep neural network allows for sustained instrument tracking in situations where classical algorithms stop their work due to introduced noise. A mechanism for the flexible selection of Area Of Interest (AOI) objects that can be tracked in the recorded video stream was used to analyse the recorded attention using a mobile eyetracker. The obtained data allow for further analysis of key skills in the education of operators of such systems. The use of deep neural networks as a detector for selected instrument types has made it possible to universalise the use of this technology for observer attention analysis when applied to a different objects-sets of monitoring and control instruments. Full article

This paper proposes a novel disturbance rejection control scheme for permanent magnet synchronous motor (PMSM) drives. Based on the framework of modified disturbance observer (DOB)-based control, the final topology of the proposed disturbance rejection proportional–integral (DR-PI) controller includes a pre-filter and a controller in a proportional–integral (PI) form. The proposed DR-PI control scheme is practical with a straightforward gain tuning rule. Note that the gain selection method is the main issue of not only conventional PI controllers but also advanced methods such as DOB-based controllers. In addition, by starting from the framework of modified DOB, this paper also proves that the PI controller with an pre-filter possesses a disturbance rejection ability similar to a DOB-based control method. To the best of our knowledge, this is the first time that such a simple and effective PI controller is designed for the speed control of PMSMs as well as theoretically proven to have a perfect disturbance rejection ability. This paper shows the steps of selecting the parameters of the proposed controller in terms of the parameters of a desired plant model, disturbance observer and compensator. Hence, unlike a traditional DOB case, in this approach, one can simultaneously tune the controller and observer at the same time. The appearance of the pre-filter from the modified DOB scheme solves an overshoot problem, thus the general motor operation is significantly improved, which is validated by experiments. The experimental evidence under two scenarios of load torque change and speed change prove the effectiveness of the proposed method compared to conventional PI and DOB control (DOBC) schemes. All the experiments were implemented on a 300 W PMSM of a setup manufactured by Lucas-Nuelle GmbH with a digital signal processor. Full article

The twenty amino acids in the standard genetic code were fixed prior to the last universal common ancestor (LUCA). Factors that guided this selection included establishment of pathways for their metabolic synthesis and the concomitant fixation of substrate specificities in the emerging aminoacyl-tRNA synthetases (aaRSs). In this conceptual paper, we propose that the chemical reactivity of some amino acid side chains (e.g., lysine, cysteine, homocysteine, ornithine, homoserine, and selenocysteine) delayed or prohibited the emergence of the corresponding aaRSs and helped define the amino acids in the standard genetic code. We also consider the possibility that amino acid chemistry delayed the emergence of the glutaminyl- and asparaginyl-tRNA synthetases, neither of which are ubiquitous in extant organisms. We argue that fundamental chemical principles played critical roles in fixation of some aspects of the genetic code pre- and post-LUCA. Full article

Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R²) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R² = 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment. Full article

The emergence of a primitive genetic code should be considered the most essential event during the origin of life. Almost a complete set of codons (as we know them) should have been established relatively early during the evolution of the last universal common ancestor (LUCA) from which all known organisms descended. Many hypotheses have been proposed to explain the driving forces and chronology of the evolution of the genetic code; however, none is commonly accepted. In the current paper, we explore the features of the genetic code that, in our view, reflect the mechanism and the chronological order of the origin of the genetic code. Our hypothesis postulates that the primordial RNA was mostly GC-rich, and this bias was reflected in the order of amino acid codon assignment. If we arrange the codons and their corresponding amino acids from GC-rich to AU-rich, we find that: 1. The amino acids encoded by GC-rich codons (Ala, Gly, Arg, and Pro) are those that contribute the most to the interactions with RNA (if incorporated into short peptides). 2. This order correlates with the addition of novel functions necessary for the evolution from simple to longer folded peptides. 3. The overlay of aminoacyl-tRNA synthetases (aaRS) to the amino acid order produces a distinctive zonal distribution for class I and class II suggesting an interdependent origin. These correlations could be explained by the active role of the bridge peptide (BP), which we proposed earlier in the evolution of the genetic code. Full article

Background: Cellulite is a condition in which the skin has a dimpled lumpy appearance. The main causes of cellulite development, studied until now, comprehends modified sensitivity to estrogens, the damage of microvasculature present among dermis and hypodermis. The differences of adipose tissue architecture between male and female might make female more susceptible to cellulite. Adipose tissue is seen to be deeply modified during cellulite development. Our study tried to understand the overall features within and surrounding cellulite to apply the best therapeutic approach. Methods: Samples of gluteal femoral area were collected from cadavers and women who had undergone surgical treatment to remove orange peel characteristics on the skin. Samples from cadavers were employed for an accurate study of cellulite using magnetic resonance imaging at 7 Tesla and for light microscopy. Specimens from patients were employed for the proteomic analysis, which was performed using high resolution mass spectroscopy (MS). Stromal vascular fraction (SVF) was obtained from the samples, which was studied using MS and flow cytometry. Results: light and electron microscopy of the cellulite affected area showed a morphology completely different from the other usual adipose depots. In cellulite affected tissues, sweat glands associated with adipocytes were found. In particular, there were vesicles in the extracellular matrix, indicating a crosstalk between the two different components. Proteomic analysis showed that adipose tissue affected by cellulite is characterized by high degree of oxidative stress and by remodeling phenomena. Conclusions: The novel aspects of this study are the peculiar morphology of adipose tissue affected by cellulite, which could influence the surgical procedures finalized to the reduction of dimpling, based on the collagen fibers cutting. The second novel aspect is the role played by the mesenchymal stem cells isolated from stromal vascular fraction of adipose tissue affected by cellulite. Full article

Soil spectra are often measured in the laboratory, and there is an increasing number of large-scale soil spectral libraries establishing across the world. However, calibration models developed from soil libraries are difficult to apply to spectral data acquired from the field or space. Transfer learning has the potential to bridge the gap and make the calibration model transferrable from one sensor to another. The objective of this study is to explore the potential of transfer learning for soil spectroscopy and its performance on soil clay content estimation using hyperspectral data. First, a one-dimensional convolutional neural network (1D-CNN) is used on Land Use/Land Cover Area Frame Survey (LUCAS) mineral soils. To evaluate whether the pre-trained 1D-CNN model was transferrable, LUCAS organic soils were used to fine-tune and validate the model. The fine-tuned model achieved a good accuracy (coefficient of determination (R²) = 0.756, root-mean-square error (RMSE) = 7.07 and ratio of percent deviation (RPD) = 2.26) for the estimation of clay content. Spectral index, as suggested as a simple transferrable feature, was also explored on LUCAS data, but did not performed well on the estimation of clay content. Then, the pre-trained 1D-CNN model was further fine-tuned by field samples collect in the study area with spectra extracted from HyMap imagery, achieved an accuracy of R² = 0.601, RMSE = 8.62 and RPD = 1.54. Finally, the soil clay map was generated with the fine-tuned 1D-CNN model and hyperspectral data. Full article

This paper reports on the various nuances of the origins of life on Earth and highlights the latest findings in that arena as reported at the Network of Researchers on Horizontal Gene Transfer and the Last Universal Common Ancestor (NoR HGT and LUCA) which was held from the 3–4th November 2016 at the Open University, UK. Although the answers to the question of the origin of life on Earth will not be fathomable anytime soon, a wide variety of subject matter was able to be covered, ranging from examining what constitutes a LUCA, looking at viral connections and “from RNA to DNA”, i.e., could DNA have been formed simultaneously with RNA, rather than RNA first and then describing the emergence of DNA from RNA. Also discussed are proteins and the origins of genomes as well as various ideas that purport to explain the origin of life here on Earth and potentially further afield elsewhere on other planets. Full article

The extant genetic machinery revolves around three interrelated polymers: RNA, DNA and proteins. Two evolutionary views approach this vital connection from opposite perspectives. The RNA World theory posits that life began in a cold prebiotic broth of monomers with the de novo emergence of replicating RNA as functionally self-contained polymer and that subsequent evolution is characterized by RNA → DNA memory takeover and ribozyme → enzyme catalyst takeover. The FeS World theory posits that life began as an autotrophic metabolism in hot volcanic-hydrothermal fluids and evolved with organic products turning into ligands for transition metal catalysts thereby eliciting feedback and feed-forward effects. In this latter context it is posited that the three polymers of the genetic machinery essentially coevolved from monomers through oligomers to polymers, operating functionally first as ligands for ligand-accelerated transition metal catalysis with later addition of base stacking and base pairing, whereby the functional dichotomy between hereditary DNA with stability on geologic time scales and transient, catalytic RNA with stability on metabolic time scales existed since the dawn of the genetic machinery. Both approaches are assessed comparatively for chemical soundness. Full article

Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm. Full article