Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks

In digital rock physics, analysing microstructures from CT and SEM scans is crucial for estimating properties like porosity and pore connectivity. Traditional segmentation methods like thresholding and CNNs often fall short in accurately detailing rock microstructures and are prone to noise. U-Net improved segmentation accuracy but required many expert-annotated samples, a laborious and error-prone process due to complex pore shapes. Our study employed an advanced generative AI model, the diffusion model, to overcome these limitations. This model generated a vast dataset of CT/SEM and binary segmentation pairs from a small initial dataset. We assessed the efficacy of three neural networks: U-Net, Attention-U-net, and TransUNet, for segmenting these enhanced images. The diffusion model proved to be an effective data augmentation technique, improving the generalization and robustness of deep learning models. TransU-Net, incorporating Transformer structures, demonstrated superior segmentation accuracy and IoU metrics, outperforming both U-Net and Attention-U-net. Our research advances rock image segmentation by combining the diffusion model with cutting-edge neural networks, reducing dependency on extensive expert data and boosting segmentation accuracy and robustness. TransU-Net sets a new standard in digital rock physics, paving the way for future geoscience and engineering breakthroughs

Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment Anything Model

Accurate image segmentation is crucial in reservoir modelling and material characterization, enhancing oil and gas extraction efficiency through detailed reservoir models. This precision offers insights into rock properties, advancing digital rock physics understanding. However, creating pixel-level annotations for complex CT and SEM rock images is challenging due to their size and low contrast, lengthening analysis time. This has spurred interest in advanced semi-supervised and unsupervised segmentation techniques in digital rock image analysis, promising more efficient, accurate, and less labour-intensive methods. Meta AI's Segment Anything Model (SAM) revolutionized image segmentation in 2023, offering interactive and automated segmentation with zero-shot capabilities, essential for digital rock physics with limited training data and complex image features. Despite its advanced features, SAM struggles with rock CT/SEM images due to their absence in its training set and the low-contrast nature of grayscale images. Our research fine-tunes SAM for rock CT/SEM image segmentation, optimizing parameters and handling large-scale images to improve accuracy. Experiments on rock CT and SEM images show that fine-tuning significantly enhances SAM's performance, enabling high-quality mask generation in digital rock image analysis. Our results demonstrate the feasibility and effectiveness of the fine-tuned SAM model (RockSAM) for rock images, offering segmentation without extensive training or complex labelling

Cross-Level Moderation of Team Cohesion in Individuals’ Utilitarian and Hedonic Information Processing: Evidence in the Context of Team-Based Gamified Training

Firms currently use teams extensively to accomplish organizational objectives. Furthermore, gamification has recently attracted much attention as a means of persuading employees and customers to engage in desired behaviors. Despite the importance of teams and the growing interest in gamification as a persuasion tool, past researchers have paid little attention to team-based gamification from a multilevel perspective. Based on motivational consistency theories, we hypothesize that at the team level, team performance has a positive effect on team cohesion. Drawing on the elaboration likelihood model (ELM), we further hypothesize two cross-level effects in the context of team-based gamified training: first, that team cohesion positively moderates the relationship between utilitarian perceptions (i.e., perceived quality of learning) and attitude; and second, that team cohesion negatively moderates the relationship between hedonic perceptions (i.e., perceived enjoyment of learning) and attitude. We tested our research model using an enterprise resource planning (ERP) simulation game involving 232 participants in 78 teams. The results of ordinary least squares and hierarchical linear modeling analysis support our hypotheses. This study makes three substantive contributions to the team literature and to the ELM in the context of team-based gamified training. First, it theorizes and empirically tests the effect of team performance on team cohesion at the team level. Second, it extends the ELM by examining the cross-level moderation of team cohesion on human information processing. Third, it demonstrates that the utilitarian and hedonic aspects of information technology do not influence user attitudes equally

Relaxed 2-D Principal Component Analysis by LpL_p Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure

NO signaling and S-nitrosylation regulate PTEN inhibition in neurodegeneration

<p>Abstract</p> <p>Background</p> <p>The phosphatase PTEN governs the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway which is arguably the most important pro-survival pathway in neurons. Recently, PTEN has also been implicated in multiple important CNS functions such as neuronal differentiation, plasticity, injury and drug addiction. It has been reported that loss of PTEN protein, accompanied by Akt activation, occurs under excitotoxic conditions (stroke) as well as in Alzheimer's (AD) brains. However the molecular signals and mechanism underlying PTEN loss are unknown.</p> <p>Results</p> <p>In this study, we investigated redox regulation of PTEN, namely S-nitrosylation, a covalent modification of cysteine residues by nitric oxide (NO), and H₂O₂-mediated oxidation. We found that S-nitrosylation of PTEN was markedly elevated in brains in the early stages of AD (MCI). Surprisingly, there was no increase in the H₂O₂-mediated oxidation of PTEN, a modification common in cancer cell types, in the MCI/AD brains as compared to normal aged control. Using several cultured neuronal models, we further demonstrate that S-nitrosylation, in conjunction with NO-mediated enhanced ubiquitination, regulates both the lipid phosphatase activity and protein stability of PTEN. S-nitrosylation and oxidation occur on overlapping and distinct Cys residues of PTEN. The NO signal induces PTEN protein degradation via the ubiquitin-proteasome system (UPS) through NEDD4-1-mediated ubiquitination.</p> <p>Conclusion</p> <p>This study demonstrates for the first time that NO-mediated redox regulation is the mechanism of PTEN protein degradation, which is distinguished from the H₂O₂-mediated PTEN oxidation, known to only inactivate the enzyme. This novel regulatory mechanism likely accounts for the PTEN loss observed in neurodegeneration such as in AD, in which NO plays a critical pathophysiological role.</p

Positional Signaling and Expression of ENHANCER OF TRY AND CPC1 Are Tuned to Increase Root Hair Density in Response Phosphate Deficiency in Arabidopsis thaliana

Phosphate (Pi) deficiency induces a multitude of responses aimed at improving the acquisition of Pi, including an increased density of root hairs. To understand the mechanisms involved in Pi deficiency-induced alterations of the root hair phenotype in Arabidopsis (Arabidopsis thaliana), we analyzed the patterning and length of root epidermal cells under control and Pi-deficient conditions in wild-type plants and in four mutants defective in the expression of master regulators of cell fate, CAPRICE (CPC), ENHANCER OF TRY AND CPC 1 (ETC1), WEREWOLF (WER) and SCRAMBLED (SCM). From this analysis we deduced that the longitudinal cell length of root epidermal cells is dependent on the correct perception of a positional signal (‘cortical bias’) in both control and Pi-deficient plants; mutants defective in the receptor of the signal, SCM, produced short cells characteristic of root hair-forming cells (trichoblasts). Simulating the effect of cortical bias on the time-evolving probability of cell fate supports a scenario in which a compromised positional signal delays the time point at which non-hair cells opt out the default trichoblast pathway, resulting in short, trichoblast-like non-hair cells. Collectively, our data show that Pi-deficient plants increase root hair density by the formation of shorter cells, resulting in a higher frequency of hairs per unit root length, and additional trichoblast cell fate assignment via increased expression of ETC1

Positivity of the English language

Over the last million years, human language has emerged and evolved as a fundamental instrument of social communication and semiotic representation. People use language in part to convey emotional information, leading to the central and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languages neutrally, positively, or negatively biased? Here, we report that the human-perceived positivity of over 10,000 of the most frequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of word positivity for four large and distinct corpora, demonstrating that their form is broadly invariant with respect to frequency of word use.Comment: Manuscript: 9 pages, 3 tables, 5 figures; Supplementary Information: 12 pages, 3 tables, 8 figure

Consensus clustering in complex networks

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.Comment: 11 pages, 12 figures. Published in Scientific Report

Inhibitory Effects of Hwangryunhaedok-Tang in 3T3-L1 Adipogenesis by Regulation of Raf/MEK1/ERK1/2 Pathway and PDK1/Akt Phosphorylation

Hwangryunhaedok-tang (HRT) has been long used as traditional medicine in Asia. However, inhibitory role of HRT is unclear in early stage of 3T3-L1 adipocyte differentiation related to signaling. In the present study, we investigated the inhibitory effects of HRT on upstream signaling of peroxisome proliferation-activity receptor-γ (PPAR-γ) and CCAAT/enhancer binding protein-β (C/EBP-β) expression in differentiation of 3T3-L1 preadipocytes. We found that HRT significantly inhibited the adipocyte differentiation by downregulating several adipocyte-specific transcription factors including PPAR-γ, C/EBP-α, and C/EBP-β in 3T3-L1 preadipocytes. Furthermore, we observed that HRT markedly inhibited the differentiation media-mediated phosphorylation of Raf/extracellular mitogen-activated protein kinase 1 (MEK1)/signal-regulated protein kinase 1/2 (ERK1/2) and phosphorylation of phosphoinositide-dependent kinase 1 (PDK1)/Akt. These results indicate that anti-adipogenesis mechanism involves the downregulation of the major transcription factors of adipogenesis including PPAR-γ and C/EBP-α through inhibition of Raf/MEK1/ERK1/2 phosphorylation and PDK1/Akt phosphorylation by HRT. Furthermore, high performance liquid chromatography (HPLC) analysis showed HRT contains active antiobesity constituents such as palmatine, berberine, geniposide, baicalin, baicalein, and wogonin. Taken together, this study suggested that anti-adipogenesis effects of HRT were accounted by downregulation of Raf/MEK1/ERK1/2 pathway and PDK1/Akt pathway during 3T3-L1 adipocyte differentiation

Location-aware online learning for top-k recommendation

We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message. © 2016 Elsevier B.V