Pivot Technique Research Articles

High Expression of ZFP42 Improves Early Development of Pig Embryos Produced by Handmade Cloning.

Handmade Cloning (HMC) is a pivotal technique for cloning pig embryos. Despite its significance, the low efficiency of this method hampers its widespread application. Although numerous factors and signaling pathways influencing embryo development have been studied, the mechanisms underlying low developmental capacity and insufficient reprogramming of cloned embryos remain elusive. In the present study, we sought to elucidate key regulatory factors involved in the development of pig HMC embryos by comparing and analyzing the gene expression profiles of HMC embryos with those of naturally fertilized (NF) embryos at the 4-cell, 8-cell, and 16-cell stages. The results showed that ZFP42 expression is markedly higher in NF embryos than in cloned counterparts. Subsequent experiments involving the injection of ZFP42 messenger RNA (mRNA) into HMC embryos showed that ZFP42 could enhance the blastocyst formation rate, upregulate pluripotent genes and metabolic pathways. This highlights the potential of ZFP42 as a critical factor in improving the development of pig HMC embryos.

Exploring the Potential of Pre-Trained Language Models of Code for Automated Program Repair

In the realm of software development, automated program repair (APR) emerges as a pivotal technique, autonomously debugging faulty code to boost productivity. Despite the notable advancements of large pre-trained language models of code (PLMCs) in code generation, their efficacy in complex tasks like APR remains suboptimal. This limitation is attributed to the generic development of PLMCs, whose specialized potential for APR is yet be to fully explored. In this paper, we propose a novel approach designed to enhance PLMCs’ APR performance through source code augmentation and curriculum learning. Our approach employs code augmentation operators to generate a spectrum of syntactically varied yet semantically congruent bug-fixing programs, thus enriching the dataset’s diversity. Furthermore, we design a curriculum learning strategy, enabling PLMCs to develop a deep understanding of program semantics from these enriched code variants, thereby refining their APR fine-tuning prowess. We apply our approach across different PLMCs and systematically evaluate it on three benchmarks: BFP-small, BFP-medium, and Defects4J. The experimental results show that our approach outperforms both original models and existing baseline methods, demonstrating the promising future of adapting PLMCs for code debugging in practice.

Novel Phase-Sensitive Full-Waveform Tomography for Seismic Imaging

Abstract Full-waveform tomography (FWT) is increasingly recognized as a pivotal technique for delineating high-resolution subsurface properties. Despite its significant potential, practical applications of FWT encounter persistent challenges, particularly in dealing with local minima and cycle-skipping problems. These difficulties often arise and are intensified by the least-squares (L2) norm’s intrinsic insensitivity to phase mismatches. To address these challenges, we have redefined the traditional L2 norm misfit function by incorporating a time shift within the synthetic waveform. This shift is determined by the temporal discrepancies between the observed and synthetic waveforms, identified through a cross-correlation technique. This approach, termed phase-sensitive FWT, integrates phase differences into the new misfit function, thus significantly mitigating the cycle-skipping problem. Numerical experiments demonstrate that PSFWT reduces dependence on the initial model and achieves more accurate inversion results compared with the traditional L2 norm method, highlighting its potential for enhancing the precision and reliability of seismic imaging.

TriSampler: A Better Negative Sampling Principle for Dense Retrieval

Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance. While existing negative sampling methods have made commendable progress by leveraging hard negatives, a comprehensive guiding principle for constructing negative candidates and designing negative sampling distributions is still lacking. To bridge this gap, we embark on a theoretical analysis of negative sampling in dense retrieval. This exploration culminates in the unveiling of the quasi-triangular principle, a novel framework that elucidates the triangular-like interplay between query, positive document, and negative document. Fueled by this guiding principle, we introduce TriSampler, a straightforward yet highly effective negative sampling method. The keypoint of TriSampler lies in its ability to selectively sample more informative negatives within a prescribed constrained region. Experimental evaluation show that TriSampler consistently attains superior retrieval performance across a diverse of representative retrieval models.

Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective

Estimating 3D full-body pose from sparse sensor data is a pivotal technique employed for the reconstruction of realistic human motions in Augmented Reality and Virtual Reality. However, translating sparse sensor signals into comprehensive human motion remains a challenge since the sparsely distributed sensors in common VR systems fail to capture the motion of full human body. In this paper, we use well-designed Body Pose Graph (BPG) to represent the human body and translate the challenge into a prediction problem of graph missing nodes. Then, we propose a novel full-body motion reconstruction framework based on BPG. To establish BPG, nodes are initially endowed with features extracted from sparse sensor signals. Features from identifiable joint nodes across diverse sensors are amalgamated and processed from both temporal and spatial perspectives. Temporal dynamics are captured using the Temporal Pyramid Structure, while spatial relations in joint movements inform the spatial attributes. The resultant features serve as the foundational elements of the BPG nodes. To further refine the BPG, node features are updated through a graph neural network that incorporates edge reflecting varying joint relations. Our method's effectiveness is evidenced by the attained state-of-the-art performance, particularly in lower body motion, outperforming other baseline methods. Additionally, an ablation study validates the efficacy of each module in our proposed framework.

Evolution of Transfer Learning and Generalization in Machine Learning: Advancements Unveiled

In recent years, transfer learning has emerged as a pivotal technique in machine learning, enabling models to leverage insights gained from one domain to enhance their performance in distinct domains. This shift in approach has catalyzed a surge in research endeavors aimed at uncovering strategies to augment the effectiveness of transfer learning while also addressing the associated challenges. This study delves comprehensively into the multifaceted realm of transfer learning and generalization, exploring a spectrum of methodologies designed to amplify knowledge transfer across diverse contexts. From adapting to varying domains to exploring meta-learning principles, this investigation surveys an array of techniques aimed at bolstering models' capacity to extrapolate their learning to novel tasks and situations. Nevertheless, achieving seamless knowledge transfer encounters challenges such as domain shifts, dataset biases, and high-dimensional data complexities. By critically evaluating these hurdles, this research highlights the impediments that curtail transfer learning's full potential and discusses potential avenues for mitigation. Moreover, in the evolving landscape of machine learning, novel innovations like generative adversarial networks (GANs) and few-shot learning techniques have introduced new dimensions of adaptability and robustness to transfer learning. In summation, this comprehensive exploration underscores the intricate interplay between transfer learning and generalization, contributing to a deeper understanding of effective transfer learning strategies and their associated challenges, while guiding researchers and practitioners to pioneer innovative solutions that advance the frontiers of transfer learning and facilitate the development of more generalized machine learning models.

Crystallinity and mechanical properties of polypropylene products foamed by microcellular injection molding process

AbstractMicrocellular injection molding (MIM) is a pivotal technique for lightweight molding. Polypropylene (PP) is widely used in MIM due to light weight and easy processing. The crystallinity of foamed PP products influences the load‐bearing structures and consequently mechanical properties. To address the deterioration of mechanical properties caused by internal porous structure, a novel method is proposed to regulate the crystallinity by optimizing process parameters, thereby improving mechanical properties. Wide‐angle x‐ray diffraction, mechanical testing, and melt flow simulation are applied to investigate the influence of process parameters on crystallinity and mechanical properties. Parameters are further optimized by establishing a multivariate nonlinear regression model for process parameters and crystallinity. The results show that increasing melt temperature, injection rate, mold temperature, and cooling time stimulate crystallization, enhancing strength and decreasing elongation at break. Conversely, high injection ratio of supercritical fluid obstructs crystallization, reducing strength, and increasing elongation at break. The crystallinity increases by 20.81%; the corresponding tensile, flexural, and shear strength and corresponding modulus increases by 5.39% and 10.28%, 7.60% and 18.50%, 5.00% and 11.37%, and elongation at break decreased by 5.19% through the optimization of regression model. The proposed method is feasible to upgrade the mechanical properties of foamed PP products.

Selection and Validation of qRT-PCR Internal Reference Genes to Study Flower Color Formation in Camellia impressinervis.

Real-time quantitative PCR (qRT-PCR) is a pivotal technique for gene expression analysis. To ensure reliable and accurate results, the internal reference genes must exhibit stable expression across varied experimental conditions. Currently, no internal reference genes for Camellia impressinervis have been established. This study aimed to identify stable internal reference genes from eight candidates derived from different developmental stages of C. impressinervis flowers. We employed geNorm, NormFinder, and BestKeeper to evaluate the expression stability of these candidates, which was followed by a comprehensive stability analysis. The results indicated that CiTUB, a tubulin gene, exhibited the most stable expression among the eight reference gene candidates in the petals. Subsequently, CiTUB was utilized as an internal reference for the qRT-PCR analysis of six genes implicated in the petal pigment synthesis pathway of C. impressinervis. The qRT-PCR results were corroborated by transcriptome sequencing data, affirming the stability and suitability of CiTUB as a reference gene. This study marks the first identification of stable internal reference genes within the entire genome of C. impressinervis, establishing a foundation for future gene expression and functional studies. Identifying such stable reference genes is crucial for advancing molecular research on C. impressinervis.

An Investigation of Gas-Fingering Behavior during CO2 Flooding in Acid Stimulation Formations

Summary CO2 flooding is emerging as a pivotal technique used extensively for carbon capture, utilization, and storage (CCUS) strategies. Acid stimulation is one common technique widely used to improve well-formation connectivity by creating wormholes. This work is motivated to investigate the gas-fingering behavior induced by acid stimulation during CO2 flooding. We present an integrated simulation framework to couple the acid stimulation and CO2 flooding processes, in which the two-scale continuum model is used to model the development of wormhole dissolution patterns. Then, sensitivity case simulations are conducted through the equation of state (EOS)–based compositional model to further analyze the CO2 fingering behavior in acid stimulation formations separately under immiscible and miscible conditions. Results demonstrate that for acid stimulation, the typical dissolution patterns and the optimal acid injection rate corresponding to the minimum acid breakthrough volume observed in the laboratory are prevalent in field-scale simulations. For CO2 flooding simulation, the dissolution patterns trigger CO2 fingering (bypassing due to the high conductivity of wormholes) in the stimulated region, and a lateral boundary effect eliminating fingers exerts its influence over the system through transverse mixing. The optimal acid injection rate varies when the focus of interest changes from the minimum acid breakthrough volume to CO2 flooding performance. The best CO2 flooding performance is always observed in uniform dissolution, and the dissolution patterns have a greater influence on the performance under miscible conditions. This work provides technical and theoretical support for the practical application of acid stimulation and CO2 flooding.

Kinetics Study of PVA Polymer by Model-Free and Model-Fitting Methods Using TGA.

Thermogravimetric Analysis (TGA) serves a pivotal technique for evaluating the thermal behavior of Polyvinyl alcohol (PVA), a polymer extensively utilized in the production of fibers, films, and membranes. This paper targets the kinetics of PVA thermal degradation using high three heating rate range 20, 30, and 40 K min-1. The kinetic study was performed using six model-free methods: Freidman (FR), Flynn-Wall-Qzawa (FWO), Kissinger-Akahira-Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY) for the determination of the activation energy (Ea). TGA showed two reaction stages: the main one at 550-750 K and the second with 700-810 K. But only the first step has been considered in calculating Ea. The average activation energy values for the conversion range (0.1-0.7) are between minimum 104 kJ mol-1 by VY to maximum 199 kJ mol-1 by FR. Model-fitting has been applied by combing Coats-Redfern (CR) with the master plot (Criado's) to identify the most convenient reaction mechanism. Ea values gained by the above six models were very similar with the average value of (126 kJ mol-1) by CR. The reaction order models-Second order (F2) was recommended as the best mechanism reaction for PVA pyrolysis. Mechanisms were confirmed by the compensation effect. Finally, (∆H, ∆G, and ∆S) parameters were presented and proved that the reaction is endothermic.

The Evolution of Lateral Lumbar Interbody Fusion: A Journey from Past to Present.

Lumbar interbody fusion procedures have seen a significant evolution over the years, with various approaches being developed to address spinal pathologies and instability, including posterior lumbar interbody fusion (PLIF), transforaminal lumbar interbody fusion (TLIF), anterior lumbar interbody fusion (ALIF), and lateral lumbar interbody fusion (LLIF). LLIF, a pivotal technique in the field, initially emerged as extreme/direct lateral interbody fusion (XLIF/DLIF) before the development of oblique lumbar interbody fusion (OLIF). To ensure comprehensive circumferential stability, LLIF procedures are often combined with posterior stabilization (PS) using pedicle screws. However, achieving this required repositioning of the patient during the surgical procedure. The advent of single-position surgery (SPS) has revolutionized the procedure by eliminating the need for patient repositioning. With SPS, LLIF along with PS can be performed either in the lateral or prone position, resulting in significantly reduced operative time. Ongoing research endeavors are dedicated to further enhancing LLIF procedures making them even safer and easier. Notably, the integration of robotic technology into SPS has emerged as a game-changer, simplifying surgical processes and positioning itself as a vital asset for the future of spinal fusion surgery. This literature review aims to provide a succinct summary of the evolutionary trajectory of lumbar interbody fusion techniques, with a specific emphasis on its recent advancements.

The Microverse: A Task-Oriented Edge-Scale Metaverse

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.

Fiber Technology in Drug Delivery and Pharmaceuticals.

The field of fiber technology is a dynamic and innovative domain that offers novel solutions for controlled and targeted therapeutic interventions. This abstract provides an overview of key aspects within this field, encompassing a range of techniques, applications, commercial developments, intellectual property, and regulatory considerations. The foundational introduction establishes the significance of fiber-based drug delivery systems. Electrospinning, a pivotal technique, has been explored in this paper, along with its various methods and applications. Monoaxial, coaxial, triaxial, and side-by-side electrospinning techniques each offer distinct advantages and applications. Centrifugal spinning, solution and melt blowing spinning, and pressurized gyration further contribute to the field's diversity. The review also delves into commercial advancements, highlighting marketed products that have successfully harnessed fiber technology. The role of intellectual property is acknowledged, with patents reflecting the innovative strides in fiber-based drug delivery. The regulatory perspective, essential for ensuring safety and efficacy, is discussed in the context of global regulatory agencies' evaluations. This review encapsulates the multidimensional nature of fiber technology in drug delivery and pharmaceuticals, showcasing its potential to revolutionize medical treatments and underscores the importance of continued collaboration between researchers, industry, and regulators for its advancement.

Enhancing growth and yield parameters of Sinapis alba through optimized seed priming techniques

AbstractEarly seed germination significantly influences crop growth and production, prompting the exploration of seed priming as a pivotal technique. Despite the acknowledged importance of faster germination and emergence for successful seedling establishment, oilseed crops like Sinapis alba have received limited attention concerning seed priming methods, unlike cereal and grain crops. This study aimed to bridge this gap by subjecting S. alba seeds to six priming treatments: T1 = control, T2 = distilled water, T3 = NaCl (0.5%), T4 = KNO3 (0.5%), T5 = CaCl2 (0.5%), and T6 = Moringa leaf extract at a 1:30 ratio (30 times diluted), to assess their impact on various growth and yield parameters. The data analysis revealed significant effects of seed priming on various parameters, with exceptions in plant height unaffected by seed soaking. Seed priming notably enhanced germination percentage, germination rate index, and seedling vigor index, while reducing mean germination time to emergence significantly. Furthermore, primed seeds exhibited significant increases in seedling fresh and dry weights, crop growth rate, net assimilation rate, root and shoot lengths, leaf area, chlorophyll content, early flowering, branch numbers, pod counts, and 1000‐seed weight compared to non‐primed seeds. The biological yield was relatively higher in treated seeds. Additionally, seed yield and oil content were found to be higher in primed seeds compared to the control. Among the treatments, KNO3 along with Moringa, displayed the most desirable outcomes across overall seedling parameters, suggesting their recommendation for effective priming treatments to improve the growth and yield of S. alba.

An Interpretable Approach with Explainable AI for Heart Stroke Prediction.

Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making. This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. Our contributions include a meticulously designed model, incorporating pivotal techniques such as resampling, data leakage prevention, feature selection, and emphasizing the model's comprehensibility for healthcare practitioners. This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. Applying these techniques, including model interpretability measures such as permutation importance and explainability methods like LIME, has achieved impressive results. While permutation importance provides insights into feature importance globally, LIME complements this by offering local and instance-specific explanations. Together, they contribute to a comprehensive understanding of the Artificial Neural Network (ANN) model. The combination of these techniques not only aids in understanding the features that drive overall model performance but also helps in interpreting and validating individual predictions. The ANN model has achieved an outstanding accuracy rate of 95%.

Data analysis methods and applications of the eddy current diagnostic system in the Keda Torus eXperiment device

Since the establishment of the eddy current diagnostic system within the Keda Torus eXperiment (KTX) device, it has unveiled many applications. Recent developments have introduced innovative data analysis techniques alongside compelling experimental results, underscoring the necessity for a comprehensive summary of the system's data analysis approaches and broad applications. Notable features of the system encompass exceptional precision, the ability to encompass shell currents on the entirety of the closed boundary, vector detection of shell currents, and measurement of diverse physical quantities. In terms of data analysis methodologies, meticulous scrutiny of the null field region is conducted, and we reveal a distinctive characteristic within the complex shell current signals, namely the asymmetry of the amplitudes of ±n Fourier coefficients. Moreover, the Hodge decomposition emerges as a pivotal technique, allowing for the distinctive separation of shell currents into three orthogonal components based on their distinct spatial topological properties. With regard to practical applications, an in-depth examination of the vector potential and magnetic helicity flux densities are presented in detail, further highlighting the far-reaching utility of the system's capabilities.

Principles of Hanging Drop Method (Spheroid Formation) in Cell Culture.

A type of three-dimensional (3D) cell culture models which is simple and easy is hanging drop method. The hanging drop method emerges as a pivotal technique with diverse applications in cancer research and cell biology. This method facilitates the formation of multicellular spheroids, providing a unique environment for studying cell behavior dynamics. The hanging drop method's theoretical underpinning relies on gravity-enforced self-assembly, allowing for cost-effective, reproducible 3D cell cultures with controlled spheroid sizes. The advantages of this approach include its efficiency in producing cellular heterogeneity, particularly in non-adherent 3D cultures, and its ability to create hypoxic spheroids, making it a suitable model for studying cancer. Moreover, the hanging drop method has proven valuable in investigating various aspects such as tissue structure, signaling pathways, immune activation of cancer cells, and notably, cell proliferation. Researchers have utilized the hanging drop method to explore the dynamics of cell proliferation, studying the effects of mesenchymal stem cells (MSC) secretome on cancer cells. The method's application involves co-culturing different cell lines, assessing spheroid formations, and quantifying their sizes over time. These studies have unveiled intricate cell behavior dynamics, demonstrating how the MSC secretome influences cancer cell growth and viability within a three-dimensional co-culture paradigm.

Ultrasound: Ankle and Foot Blocks

Ankle blocks are regional anaesthetic techniques used for foot surgery and pain management. Traditionally performed with anatomical landmarks, ultrasound-guided ankle and foot blocks have emerged as a pivotal technique in regional anaesthesia, offering enhanced precision and safety over traditional methods. This review article examines the current practices in ultrasound-guided ankle and foot blocks, including the necessary equipment, sonoanatomy, and injection techniques for various nerves. This comprehensive review aims to equip anaesthesiologists with the knowledge to effectively implement ultrasound-guided ankle and foot blocks, ultimately optimizing pain management and procedural success in lower extremity interventions. Keywords: Ankle block, Ultrasound guidance, Foot and ankle surgery

Development of the Mammary Gland in Mouse: A Whole-Mount Microscopic Analysis.

The mammary gland undergoes functional, developmental, and structural changes that are essential for lactation and reproductive processes. An overview of such unique tissue can offer clearer insights into mammary development and tumorigenesis. Compared to traditional methods, mouse mammary gland whole mount is a pivotal technique that provides three-dimensional structural perspectives on gland morphology and developmental stages, offering an inexpensive and accessible approach. This protocol outlines the tissue isolation of the mouse mammary gland and provides detailed instructions for whole-mount staining and analysis. Mammary gland tissues are carefully dissected from euthanized mice and stained with Carmine Alum to highlight the ductal structures, enabling detailed visualization of the branching patterns and morphological changes. Light microscopy is used to capture a panoramic image of the stained mammary gland, enabling the quantitative analysis of terminal end buds (TEBs) and bifurcated TEBs to further investigate mammary gland remodeling. This method can provide invaluable insights, particularly in the study of mammary gland morphogenesis and tumorigenesis, underscoring its significance in both basic research and clinical applications. Key features • Monitor mammary gland development within 2 days using whole-mount staining.

Principles of Indirect Co-culture Method Using Transwell.

The co-culture method is a simple type of cell culture method used to evaluate the effects of communication between various types of cells in an in vitro setting. In the co-culture method, two or more eukaryotic cell types, or eukaryotic and prokaryotic cells, are cultured together. The co-culture method reflects in vivo cell behaviors and thereby emerges as a pivotal technique with diverse applications in cancer research and cell biology. Two categories of co-culture methods (indirect methods and direct methods) are well known. Direct co-culture methods allow physical contact between the various cell types (juxtacrine signaling). In indirect methods, cells are physically separated into two different populations (for example, using a Transwell) that allow communication only via secretory factors (paracrine signaling). Herein, we focus on the principles of the indirect co-culture method. Nowadays, this method is used to explore the effects of mesenchymal stem cell (MSC) secretome on cancer cells. These studies have unveiled intricate cell behavior dynamics, demonstrating how the MSC secretome influences cancer cell proliferation, invasion, apoptosis, and polarity.