Single Pair Research Articles

Dynamic properties of the pinyon pine syngameon.

This article is a Commentary on Buck et al. (2023), 237: 2435–2449.

Whole Field Analysis of Asymmetric Helical Gear Using Photo Stress Method and FEA Method

Objectives: To carry out dynamic state analysis of asymmetrical helical gears for contact stresses using photo stress technique and suitable finite element software. Methods: Currently the Hertz contact stress equation is used to calculate the contact stress which is based on certain speculations. The value of load transmitted by a gear may be known but whether it is taken by a single or more pairs of teeth are based on some assumptions and stresses thus calculated may not be true stresses. To get precise evaluation of stress field in gear tooth it is decided to carry out experimental work on asymmetric helical gear in actual working condition. The photo stress method not only provide magnitude of stress but also gives idea about stress distribution in gear. Finite element method is used for asymmetrical helical gear to validate experimental results. Findings: The stress values obtained by photostress method and FEA method has minimum error of 2.17% and maximum of 8.89% respectively. The error values are less than 10% and within the limit. We can observe close agreement between these two methods. Novelty: This study has provided a more accurate result than theoretical method of stress calculation. Photostress analysis method is one of the best test methods for evaluation of contact stress of asymmetric helical gear with different torque condition in actual working that is dynamic condition. Maximum error of 8.89% has been found with photostress method. Keywords: Asymmetric Gears; Contact Stress; Experimental Method; Reflection Polariscope; PhotoStress; Finite Element Analysis

Transitions of motor neuron activities during Ciona development.

Motor neurons (MNs) are one of the most important components of Central Pattern Generators (CPG) in vertebrates (Brown, Proceedings of The Royal Society B: Biological Sciences (The Royal Society), 1911, 84(572), 308-319). However, it is unclear how the neural activities of these components develop during their embryogenesis. Our previous study revealed that in Ciona robusta (Ciona intestinalis type A), a model organism with a simple neural circuit, a single pair of MNs (MN2L/MN2R) was determining the rhythm of its spontaneous early motor behavior (developmental stage St.22-24). MN2s are known to be one of the main components of Ciona CPG, though the neural activities of MN2s in the later larval period (St.25-) were not yet investigated. In this study, we investigated the neural activities of MN2s during their later stages and how they are related to Ciona's swimming CPG. Long-term simultaneous Ca2+ imaging of both MN2s with GCaMP6s/f (St.22-34) revealed that MN2s continued to determine the rhythm of motor behavior even in their later larval stages. Their activities were classified into seven phases (I-VII) depending on the interval and the synchronicity of MN2L and MN2R Ca2+ transients. Initially, each MN2 oscillates sporadically (I). As they develop into swimming larvae, they gradually oscillate at a constant interval (II-III), then start to synchronize (IV) and fully synchronize (V). Intervals become longer (VI) and sporadic again during the tail aggression period (VII). Interestingly, 76% of the embryos started to oscillate from MN2R. In addition, independent photostimulations on left and right MN2s were conducted. This is the first report of the live imaging of neural activities in Ciona's developing swimming CPG. These findings will help to understand the development of motor neuron circuits in chordate animals.

Multiplexed smFRET Nucleic Acid Sensing Using DNA Nanotweezers

The multiplexed detection of disease biomarkers is part of an ongoing effort toward improving the quality of diagnostic testing, reducing the cost of analysis, and accelerating the treatment processes. Although significant efforts have been made to develop more sensitive and rapid multiplexed screening methods, such as microarrays and electrochemical sensors, their limitations include their intricate sensing designs and semi-quantitative detection capabilities. Alternatively, fluorescence resonance energy transfer (FRET)-based single-molecule counting offers great potential for both the sensitive and quantitative detection of various biomarkers. However, current FRET-based multiplexed sensing typically requires the use of multiple excitation sources and/or FRET pairs, which complicates labeling schemes and the post-analysis of data. We present a nanotweezer (NT)-based sensing strategy that employs a single FRET pair and is capable of detecting multiple targets. Using DNA mimics of miRNA biomarkers specific to triple-negative breast cancer (TNBC), we demonstrated that the developed sensors are sensitive down to the low picomolar range (≤10 pM) and can discriminate between targets with a single-base mismatch. These simple hybridization-based sensors hold great promise for the sensitive detection of a wider spectrum of nucleic acid biomarkers.

Improving the accuracy and the estimation time of inter-area modes in power system based on Tufts-Kumaresan algorithm

ABSTRACT In this paper, a new method based on the Tufts–Kumaresan (TK) algorithm is used to process nonstationary signals and extract system modes. This new algorithm for mode estimation is proposed to improve estimation accuracy under dynamic conditions. The key contribution in TK is the use of multiple orthogonal sliding windows rather than a single pair of sliding windows. Simulation results under various scenarios, such as different types of faults and load changes are used to evaluate the performance of the proposed method. The proposed method will accurately extract system modes. This method will also reduce the required memory and the calculation time of estimation in nonstationary signals. In complement to successful applications in studying the dynamic behavior of the power system, identifying and analyzing low-frequency electromechanical oscillations, and removing signal noise, this method can accurately estimate the modes of a power system. To validate the accuracy of the proposed method, the Wavelet and the Prony methods have been used for comparison. The proposed method is implemented using simulated ringdown data of standard two-area power system and real measurement data of the WSCC system breakup on 10 August 1996.

Орел-могильник в Республике Татарстан — продолжение исследований

Орел-могильник в Республике Татарстан — продолжение исследований

Targeted Mutagenesis of Mycobacterium Strains by Homologous Recombination.

Targeted mutagenesis by homologous recombination (TMHR) is an efficient allelic exchange mutagenesis for bacterial genome engineering in synthetic biology. Unlike other allelic exchange methods, TMHR does not require a heterologous recombinase to insert or excise a selectable marker from the genome. In contrast, positive and negative selection is achieved solely by suicide vector-encoded functional and host cell proteins. Here we describe a concise protocol to knock out and knock in a 3-ketosteroid-1,2-dehydrogenase gene (kstd) in Mycobacterium neoaurum HGMS2 using TMHR approach. The homology arms flanking the kstd gene are amplified by PCR in vitro and then subcloned into a common homologous recombination vector. The vector is then electroporated into the HGMS2 competent cells. The replacement of the kstd gene by homologous recombination produces antibiotic-resistant single-crossover recombination via the first allelic exchange. Double-crossover markerless mutants are directly separated using sucrose-mediated counterselection. These two steps can generate seamless mutations down to a single DNA base pair. The whole process takes less than 2weeks.

The cohesive properties and pyrolysis mechanism of an aprotic ionic liquid tetrabutylammonium bis(trifluoromethanesulfonyl)imide.

As the cohesive properties (such as the enthalpy of sublimation) of solid organic salts (or ionic liquids, ILs) are unmeasurable, a method of their indirect determination is proposed in this paper. For this purpose, the thermogravimetric analysis (TGA) and differential scanning calorimetric analysis (DSC) were carried out over a wide range of temperatures. In this study, the mathematical relationship of the thermodynamic properties between the liquid and solid phases of ILs is established using the Born-Fajans-Haber cycle, in which the sum of the vaporization enthalpy of ILs, melting enthalpy and the enthalpy of solid-solid phase transition is regarded as the sublimation enthalpy of solid organic salts. With this method, the cohesive properties of tetrabutylammonium bis(trifluoromethanesulfonyl)imide ([N4444][NTf2]), which is an aprotic IL, were successfully obtained. Additionally, the difference between the lattice energy and the cohesive energy was employed to quantitatively calculate the charge separation distance of single ion pair (r12) in the gas phase of ionic liquids for the first time, which can serve as a standard methodology to measure the closeness in distance between the anion and the cation in a gas phase ion pair. The pyrolysis mechanism of [N4444][NTf2] was also explored.

3-D Reconstruction Method for a Multiview System Based on Global Consistency

Structured light-based micro three-dimensional (3D) imaging technology is widely used in industrial applications. Using a well-calibrated camera and a projector, it is easy to obtain 3D surface topography of objects. However, single camera-projector pairs suffer from problems caused by shadows and their accuracy is limited due to the restricted field of view. To overcome these issues, this article proposes a multi-projector vision system consisting of a central camera and four surrounding projectors. The global geometry constraint scheme is applied to ensure consistency and stability across multiple projectors, which is achieved by board-to-board pose optimization. In addition, instead of directly using the iso-phase fringe line, geometric plane constraints are used in the calibration process to estimate corresponding projector pixel coordinates. Finally, a triangulation-based 3D reconstruction and data fusion approach is proposed to obtain high precision and shadow-reduced 3D surface topography. Experimental results are presented and discussed that demonstrate the good consistency and measurement accuracy of the proposed approach. The global geometry constraint scheme allows relative standard deviation and root-mean-square error in the measurements to be significantly reduced.

Cyclin-dependent kinases in cancer: Role, regulation, and therapeutic targeting.

Regulated cell division is one of the fundamental phenomena which is the basis of all life on earth. Even a single base pair mutation in DNA leads to the production of the dysregulated protein that can have catastrophic consequences. Cell division is tightly controlled and orchestrated by proteins called cyclins and cyclin-dependent kinase (CDKs), which serve as licensing factors during different phases of cell division. Dysregulated cell division is one of the most important hallmarks of cancer and is commonly associated with a mutation in cyclins and CDKs along with tumor suppressor proteins. Therefore, targeting the component of the cell cycle which leads to these characteristics would be an effective strategy for treating cancers. Specifically, Cyclin-dependent kinases (CDKs) involved in cell cycle regulation have been identified to be overexpressed in many cancers. Many studies indicate that oncogenesis occurs in cancerous cells by the overactivity of different CDKs, which impact cell cycle progression and checkpoint dysregulation which is responsible for development of tumor. The development of CDK inhibitors has emerged as a promising and novel approach for cancer treatment in both solid and hematological malignancies. Some of the novel CDK inhibitors have shown remarkable results in clinical trials, such as-Ribociclib®, Palbociclib® and Abemaciclib®, which are CDK4/6 inhibitors and have received FDA approval for the treatment of breast cancer. In this chapter, we discuss the molecular mechanism through which cyclins and CDKs regulate cell cycle progression and the emergence of cyclins and CDKs as rational targets in cancer. We also discuss recent advances in developing CDK inhibitors, which have emerged as a novel class of inhibitors, and their associated toxicities in recent years.

A NUMERICAL STUDY OF DROPLET GROUPING AND ITS POTENTIAL CONTROL USING ACOUSTIC STANDING WAVES

In monodisperse droplet streams, the inter-droplet distances can change in a manner that brings pairs or triplets of droplets closer to one another, a process known as grouping. In the advanced stages of this process, droplet pairs can coalesce to form larger droplets. The grouping mechanisms in these droplet streams are not yet fully understood. Potentially, such a process can be controlled by an acoustic field. In the present study, computational fluid dynamics (CFD) simulations of isopropanol droplet streams in air are performed in ANSYS Fluent using the Eulerian-Lagrangian approach to analyze this process and to provide insight into grouping mechanisms. User-defined functions (UDFs) are used to tailor the code to the problems addressed here. Three scenarios are investigated. For the case of a single stream of droplet pairs, the mechanism of drag coefficient differences between the leading and trailing droplets enables reproduction of the results of longitudinal grouping experiments. For the case of two parallel streams, the lift force enables reproduction of lateral grouping trends, which are observed in experiments. Finally, for a single droplet stream in an acoustic standing wave, the experimentally observed sequence of single droplets and droplet pairs, induced by the acoustic wave, is reproduced computationally. It is found that the acoustic field significantly affects both grouping behavior and the droplet distribution in the computational domain, thereby either enhancing or delaying grouping tendencies. These results strongly indicate the potential that lies in employing an acoustic field to exercise control over how, where, and if droplet grouping occurs.

BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image segmentation and recognition. Fusing low-resolution HSIs with high-resolution RGB images to reconstruct high-resolution HSIs has attracted great research attention recently. In this paper, we propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model. The experimental results demonstrate the effectiveness of our method both in simulations and in our real experiments. The proposed method outperforms other state-of-the-art nonblind and blind fusion methods on two popular HSI datasets. Our related code and data is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CPREgroup/Real-Spec-RGB-Fusion</uri> .

StereoPairFree: Self-Constructed Stereo Correspondence Network From Natural Images

This article proposes a variant of a convolutional neural network-based stereo matching method, StereoPairFree, that requires only normal and not left or right stereo images. The corresponding image patches for training stereo matching cost networks look similar, apart from some properties, such as different illumination, occlusion, distortion, and foreshortening. We propose a method for generating synthesized pairs of corresponding image patches for a given image patch. We also propose a multipatch matching cost network that exploits various input patch sizes. The proposed matching cost network is optimized by cross-based cost aggregation and semiglobal matching, followed by consistency checks and bilateral filtering. Hence, StereoPairFree does not require a single stereo pair but standard images to build a deep learning stereo matching method. It is the first stereo matching method constructed from normal images and significantly outperforms monocular stereo matching approaches (Hur and Roth, 2020), (Hur and Roth, 2021), (Schuster et al., 2020). We evaluated StereoPairFree with the KITTI-2012, KITTI-2015, and Middlebury datasets. It significantly outperformed the baseline and several deep learning-based methods using these datasets.

Two independent loss‐of‐function mutations in anthocyanidin synthase homeologous genes are responsible for the all‐green phenotype of sweet basil

Sweet basil, Ocimum basilicum L., is an important culinary herb grown worldwide. Although basil is green, many landraces, breeding lines, and exotic cultivars have purple stems and flowers. This anthocyanin pigmentation is unacceptable in traditional Italian basil used for Pesto sauce production. In the current study, we aimed to resolve the genetics that underlines the different colors. We used the recently published sweet basil genome to map quantitative trait loci (QTL) for flower and stem color in a bi-parental F2 population. It was found that the pigmentation is governed by a single QTL, harboring an anthocyanidin synthase (ANS) gene (EC 1.14.20.4). Further analysis revealed that the basil genome harbors two homeologous ANS genes, each carrying a loss-of-function mutation. ObANS1 carries a single base pair insertion resulting in a frameshift, and ObANS2 carries a missense mutation within the active site. In the purple-flower parent, ANS1 is functional, and ANS2 carries a nonsense mutation. The functionality of the ObANS1 active allele was validated by complementation assay in an Arabidopsis ANS mutant. Moreover, we have restored the functionality of the missense-mutated ObANS2 using site-directed activation. We found that the non-functional alleles were expressed to similar levels as the functional allele, suggesting polyploids invest futile effort in expressing non-functional genes, offsetting their advantageous redundancy. This work demonstrated the usefulness of the genomics and genetics of basil to understand the basic mechanism of metabolic traits and raise fundamental questions in polyploid plant biology.

A Genetic Algorithm based Domain Adaptation Framework for Classification of Disaster Topic Text Tweets

The ability to post short text and media messages on Social media platforms like Twitter, Facebook, etc., plays a huge role in the exchange of information following a mass emergency event like hurricane, earthquake, tsunami etc. Disaster victims, families, and other relief operation teams utilize social media to help and support one another. Despite the benefits offered by these communication media, the disaster topic related posts (posts that indicate conversations about the disaster event in the aftermath of the disaster) gets lost in the deluge of posts since there would be a surge in the amount of data that gets exchanged following a mass emergency event. This hampers the emergency relief effort, which in turn affects the delivery of useful information to the disaster victims. Research in emergency coordination via social media has received growing interest in recent years, mainly focusing on developing machine learning-based models that can separate disaster-related topic posts from non-disaster related topic posts. Of these, supervised machine learning approaches performed well when the machine learning model trained using source disaster dataset and target disaster dataset are similar. However, in the real world, it may not be feasible as different disasters have different characteristics. So, models developed using supervised machine learning approaches do not perform well in unseen disaster datasets. Therefore, domain adaptation approaches, which address the above limitation by learning classifiers from unlabeled target data in addition to source labelled data, represent a promising direction for social media crisis data classification tasks. The existing domain adaptation techniques for the classification of disaster tweets are experimented with using single disaster event dataset pairs; then, self-training is performed on the source target dataset pairs by considering the highly confident instances in subsequent iterations of training. This could be improved with better feature engineering. Thus, this research proposes a Genetic Algorithm based Domain Adaptation Framework (GADA) for the classification of disaster tweets. The proposed GADA combines the power of 1) Hybrid Feature Selection component using the Genetic Algorithm and Chi-Square Feature Evaluator for feature selection and 2) the Classifier component using Random Forest to classify disaster-related posts from noise on Twitter. The proposed framework addresses the challenge of the lack of labeled data in the target disaster event by proposing a Genetic Algorithm based approach. Experimental results on Twitter datasets corresponding to four disaster domain pair shows that the proposed framework improves the overall performance of the previous supervised approaches and significantly reduces the training time over the previous domain adaptation techniques that do not use the Genetic Algorithm (GA) for feature selection.

Whole-Mount In Situ Hybridization for Detection of Migrating Zebrafish Endodermal Cells.

One of the most important events in early vertebrate development is the formation and positioning of the endoderm, the embryonic progenitor cell population that gives rise to the internal organs. Recent years have seen renewed interest in the mechanisms underlying the specification and migration of endodermal progenitor cells. The zebrafish is a well-established, accessible, and powerful model to study this cell population. Zebrafish endodermal cells are specified around 4h after fertilization and subsequently migrate as evenly spaced single cells in a stereotypical manner in the next 6h. Given the large numbers of fertilized eggs that can be obtained from a single breeding pair and the ease of chemical and genetic perturbations, the zebrafish is an excellent model to study mechanisms underlying endoderm specification and migration. An easy approach to visualizing and quantitating endodermal cells and their migratory routes is by whole-mount in situ hybridization (WISH) on fixed embryos, collected in time series. This chapter provides basic information on the organization and staging of the embryos, with an emphasis on the migrating endodermal cell population. In addition, optimized protocols for the isolation and fixation of staged embryos are provided as well as detailed probe synthesis and WISH protocols, specific for migrating endoderm. Finally, details are provided on how to approach these experiments quantitatively, and some common pitfalls are discussed.

Differential Gene Expression Prediction by Ensemble Deep Networks on Histone Modification Data.

Predicting differential gene expression (DGE) from Histone modifications (HM) signal is crucial to understand how HM controls cell functional heterogeneity through influencing differential gene regulation. Most existing prediction methods use fixed-length bins to represent HM signals and transmit these bins into a single machine learning model to predict differential expression genes of single cell type or cell type pair. However, the inappropriate bin length may cause the splitting of the important HM segment and lead to information loss. Furthermore, the bias of single learning model may limit the prediction accuracy. Considering these problems, in this paper, we proposes an Ensemble deep neural networks framework for predicting Differential Gene Expression (EnDGE). EnDGE employs different feature extractors on input HM signal data with different bin lengths and fuses the feature vectors for DGE prediction. Ensemble multiple learning models with different HM signal cutting strategies helps to keep the integrity and consistency of genetic information in each signal segment, and offset the bias of individual models. Besides the popular feature extractors, we also propose a new Residual Network based model with higher prediction accuracy to increase the diversity of feature extractors. Experiments on the real datasets from the Roadmap Epigenome Project (REMC) show that for all cell type pairs, EnDGE significantly outperforms the state-of-the-art baselines for differential gene expression prediction.

Predicting Chromatin Interactions from DNA Sequence Using DeepC.

The genome 3D structure is central to understanding how disease-associated genetic variants in the noncoding genome regulate their target genes. Genome architecture spans large-scale structures determined by fine-grained regulatory elements, making it challenging to predict the effects of sequence and structural variants. Experimental approaches for chromatin interaction mapping remain costly and time-consuming, limiting their use for interrogating changes of chromatin architecture associated with genomic variation at scale. Computational models to predict chromatin interactions have either interpreted chromatin at coarse resolution or failed to capture the long-range dependencies of larger sequence contexts. To bridge this gap, we previously developed deepC, a deep neural network approach to predict chromatin interactions from DNA sequence at megabase scale. deepC employs dilated convolutional layers to achieve simultaneously a large sequence context while interpreting the DNA sequence at single base pair resolution. Using transfer learning of convolutional weights trained to predict a compendium of chromatin features across cell types allows deepC to predict cell type-specific chromatin interactions from DNA sequence alone. Here, we present a detailed workflow to predict chromatin interactions with deepC. We detail the necessary data pre-processing steps, guide through deepC model training, and demonstrate how to employ trained models to predict chromatin interactions and the effect of sequence variations on genome architecture.

Орёл-могильник на юго-востоке Казахстана в 2022–2023 годах: распространение, численность, угрозы

Орёл-могильник на юго-востоке Казахстана в 2022–2023 годах: распространение, численность, угрозы

Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS

We report a new workflow for background-oriented schlieren (BOS), termed “physics-informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and distorted images. Our method uses a physics-informed neural network (PINN) to produce flow fields that simultaneously satisfy the measurement data and governing equations. For the high-speed, approximately inviscid flows of interest in this work, we specify a physics loss based on the Euler and irrotationality equations. BOS is a quantitative fluid visualization technique that is commonly used to characterize compressible flow. Images of a background pattern, positioned behind the measurement volume, are processed with computer vision and tomography algorithms to determine the density field. Crucially, BOS features a series of ill-posed inverse problems that require supplemental information (i.e., in addition to the images) to accurately reconstruct the flow. Current methods for BOS rely upon interpolation of the images or a penalty term to promote a globally- or piecewise-smooth solution. However, these algorithms are invariably incompatible with the flow physics, leading to errors in the density field. Physics-informed BOS directly reconstructs all the flow fields using a PINN that includes the BOS measurement model and governing equations. This procedure improves the accuracy of density estimates and also yields velocity and pressure data, which were not previously available. We demonstrate our approach by reconstructing synthetic data that corresponds to analytical and numerical phantoms as well as a single pair of experimental measurements. Our physics-informed reconstructions are significantly more accurate than conventional BOS estimates. Furthermore, to the best of our knowledge, this work represents the first use of a PINN to reconstruct a supersonic flow from experimental data of any kind.