ethicalabs/Research-Intent-Collab huggingface dataset:
GENETIC TRANSFORMATION IN AGRICULTURE: THE REAL CHANCE FOR ENSURING WORLDWIDE SUSTAINABLE FOOD SECURITY

Obtaining genetically modified (GM) organisms, with superior biological and productive performances, represents the priority objective of modern applied genetics research, oriented towards the development of effective procedures for increasing genetic variability, according to the requirements of breeding programs of economically interesting species. GM crops can contribute essentially to the millennium development goal, that of reducing poverty and increasing food security, by optimizing agricultural productivity. GM plants have improved traits that include herbicide tolerance, disease and pest resistance, drought tolerance, health or nutritional benefits, a longer shelf life, or a more efficient industrial use. Also, the GM crops contribute to sustainable environmental protection by reduction of the pesticides application amount and reduction of CO2 emissions. In this context, the aim of the present paper is the worldwide brief radiography of transgenesis, in terms of surfaces, the main producing countries, but also of the main GM crops and their market. The research method consisted in selecting of several scientific results from the WOS, Clarivate Analytics, Scopus and Springer databases. Also, were accessed several statistical data of the ISAAA, USDA, Research and Market, MADR, etc. The main producing countries of GM crops are USA, Brazil, Argentina, Canada, India, Paraguay and China. The largest GM areas are occupied by soybean, maize, cotton, canola and alfalfa. The global agricultural biotechnology market for transgenic crops is expected to reach 12.07 billion USD in 2026, growing by 18.2% compared with 2021. Genetic transformation and GM agricultural crops represent an effective strategy and real chance to counteract climate change and food insecurity, and recently developed genetic engineering techniques will play an important role in the future.
Arbitration and the World Trade Organization—<i>The Forgotten Provisions of Article 25 of the Dispute Settlement Understanding</i>

This article explores one of the most forgotten provisions in the World Trade Organization’s (WTO’s) large body of agreements, namely, Article 25 of the Dispute Settlement Understanding (DSU) and considers the use of arbitration as an alternative means of dispute settlement under the DSU. Given the intense interest in the WTO dispute settlement mechanism and the unique importance of arbitration, Article 25 is worthy of study. It is a highly useful provision, which, were it invoked more often, could ease the dispute settlement process for the parties involved. This examination of Article 25, its genesis, and current usage patterns, also takes in a background look at the WTO dispute settlement mechanism through a procedural lens and pays particular attention to Arab countries’ participation (or non-participation) in WTO dispute settlement proceedings.
Learning Markov Chain in Unordered Dataset

The assumption that data samples are independently identically distributed is the backbone of many learning algorithms. Nevertheless, datasets often exhibit rich structure in practice, and we argue that there exist some unknown order within the data instances. In this technical report, we introduce OrderNet that can be used to extract the order of data instances in an unsupervised way. By assuming that the instances are sampled from a Markov chain, our goal is to learn the transitional operator of the underlying Markov chain, as well as the order by maximizing the generation probability under all possible data permutations. Specifically, we use neural network as a compact and soft lookup table to approximate the possibly huge, but discrete transition matrix. This strategy allows us to amortize the space complexity with a single model. Furthermore, this simple and compact representation also provides a short description to the dataset and generalizes to unseen instances as well. To ensure that the learned Markov chain is ergodic, we propose a greedy batch-wise permutation scheme that allows fast training. Empirically, we show that OrderNet is able to discover an order among data instances. We also extend the proposed OrderNet to one-shot recognition task and demonstrate favorable results.
Bayesian Deep Learning with Computationally Efficient Uncertainty Quantification

学位の種別:課程博士|審査委員会委員 : （主査）東京大学教授 須田 礼仁, 東京大学教授 佐藤 一誠, 東京大学准教授 横矢 直人, 東京大学准教授 塩谷 亮太, 京都大学准教授 粟野 皓光
Universal health insurance coverage does not eliminate inequities in access to cardiac procedures after acute myocardial infarction

It remains unclear whether socioeconomic status (SES) influences access to invasive cardiac procedures after acute myocardial infarction (AMI) in a universal health care system. The objective of this study was to evaluate the effect of SES on access to cardiac procedure after AMI in a universal health care system.This was an observational cohort study of all patients with a first AMI in the province of Quebec, Canada, between 1985 to 1995. Information on treatment was obtained from the discharge and physicians' claims databases. SES was obtained from census data by linking postal codes. SES-independent predictors of use were identified, then incorporated in hierarchical models to predict use in low, medium, and high SES areas. The main outcome measures were rates of cardiac catheterization, percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG) as a function of SES.SES data were available for 62,364 individuals with a first AMI. Of these, 65% were men and the mean age was 64 +/- 13 years. Rates of cardiac procedures rose with an increase in several SES measures. After adjustment for individual-level predictors of use of cardiac catheterization, average rent, (odds ratio per $100 difference: 1.57, 95% credible interval: 1.36 to 1.80) and proportion of renters, (odds ratio, 2.2; 95% CI: 1.21 to 3.73) in the area were independent SES predictors. Patients in low SES areas (median family income: $ 30,809 CDN) were less likely to undergo cardiac catheterization than patients in high SES areas ($92,169 CDN) (men: 33%; compared with 47%; women: 18%; compared with 47%). However, among patients with cardiac catheterization, SES was not associated with the use of revascularization procedures. For example, PCI rates for men within 90 days after AMI were 26%, compared with 25% in low and high SES areas, respectively. CABG rates were 15%, compared with 19%.We found that in the universal health care system of Canada, access to cardiac catheterization after AMI varied according to SES. Among those with cardiac catheterization, SES did not appear to influence further use of revascularization procedures.
Inclusive Federated Learning for Healthcare Collaboration: Development and Evaluation of a Compliance-Aware Differential Privacy Framework (Preprint)

BACKGROUND                   Federated learning (FL) enables collaborative training of clinical AI models without centralizing sensitive patient data. Despite its promise, adoption in healthcare is limited by privacy concerns, heterogeneous institutional compliance, and resource disparities. In standard differential privacy (DP) methods, DP noise is applied in equal amounts for each client, which can disproportionately penalize well-compliant or under-resourced institutions, reducing model performance.                                                     OBJECTIVE                   This study introduces a compliance-aware FL framework that adapts DP mechanisms based on institutional compliance. Our goals were to (1) design a compliance scoring tool aligned with healthcare and security standards, (2) integrate adaptive DP noise into FL, and (3) evaluate its effectiveness in balancing privacy, compliance, and accuracy.                                                     METHODS                   We developed a compliance scoring system to quantify institutional adherence to key standards and used these scores to guide adaptive DP noise allocation. Higher-compliance institutions received proportionally less noise, while global privacy guarantees were maintained. Experiments on public clinical datasets compared the proposed framework against traditional FL with uniform DP, assessing accuracy, fairness, and robustness across varying compliance distributions.                                                     RESULTS                   The compliance-aware FL framework outperformed standard approaches, yielding up to 15% higher accuracy when combining data from both highly regulated and under-resourced institutions. Adaptive noise allocation reduced performance degradation in compliant sites and promoted equitable participation. The compliance scoring tool provided a transparent measure of readiness, supporting secure and inclusive collaboration.                                                     CONCLUSIONS                   Incorporating compliance into FL offers a practical pathway for advancing privacy-preserving AI in healthcare. Adaptive DP improves accuracy and fairness while maintaining privacy guarantees, making FL more viable for diverse clinical settings. This framework bridges theoretical privacy methods with real-world healthcare needs.
HAUSP Is a Key Epigenetic Regulator of the Chromatin Effector Proteins

HAUSP (herpes virus-associated ubiquitin-specific protease), also known as Ubiquitin Specific Protease 7, plays critical roles in cellular processes, such as chromatin biology and epigenetics, through the regulation of different signaling pathways. HAUSP is a main partner of the “Epigenetic Code Replication Machinery,” ECREM, a large protein complex that includes several epigenetic players, such as the ubiquitin-like containing plant homeodomain (PHD) and an interesting new gene (RING), finger domains 1 (UHRF1), as well as DNA methyltransferase 1 (DNMT1), histone deacetylase 1 (HDAC1), histone methyltransferase G9a, and histone acetyltransferase TIP60. Due to its deubiquitinase activity and its ability to team up through direct interactions with several epigenetic regulators, mainly UHRF1, DNMT1, TIP60, the histone lysine methyltransferase EZH2, and the lysine-specific histone demethylase LSD1, HAUSP positions itself at the top of the regulatory hierarchies involved in epigenetic silencing of tumor suppressor genes in cancer. This review highlights the increasing role of HAUSP as an epigenetic master regulator that governs a set of epigenetic players involved in both the maintenance of DNA methylation and histone post-translational modifications.
The Effectiveness of Peace Building and Conflict Resolution Strategies in Addressing Conflicts and Insecurity in Nigeria

Nigeria has been plagued by conflicts and insecurity for decades, resulting in a significant loss of lives and property. As a result, the Nigerian government and international organisations have implemented various peace building and conflict resolution strategies to address the root causes of conflicts and insecurity in the country. This study aims to examine the effectiveness of these strategies in promoting peace and stability in Nigeria. The study will utilise secondary sources of data. The findings of this study suggest that peace building and conflict resolution strategies have been moderately effective in addressing conflicts and insecurity in Nigeria. The root causes of conflicts and insecurity, such as poverty, unemployment, and political instability, have not been adequately addressed by these strategies. Furthermore, the implementation of these strategies has been hampered by corruption and lack of political will. However, the study also identified some successes in the implementation of peace building and conflict resolution strategies in Nigeria. For example, the establishment of the Niger Delta Development Commission (NDDC) has contributed to reducing conflicts and insecurity in the Niger Delta region. Additionally, the implementation of community-based peace building initiatives has been successful in some areas of the country. Overall, this study highlights the need for a comprehensive approach to peace building and conflict resolution in Nigeria, one that addresses the root causes of conflicts and insecurity. This approach should involve the participation of all stakeholders, including the government, international organisations, civil society organizations, and affected individuals. Additionally, efforts should be made to address corruption and lack of political will, which have hindered the effectiveness of peace building and conflict resolution strategies in Nigeria.
ETHICAL APPROACHES IN PHILOSOPHY OF EDUCATION, SCIENCE, AND TECHNOLOGY: A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE IN EDUCATION LITERATURE BASED ON SCOPUS 2021-2025

This study aims to analyze ethical approaches in the philosophy of education, science, and technology through the literature review. This study is motivated by the increasing use of Artificial Intelligence (AI) technology in the educational realm which raises various ethical and epistemological issues. A total of 15 articles from Scopus-indexed journals (Q1–Q4) for the period 2021–2025 were analyzed based on strict inclusion criteria. The results of the study reveal that the dominant themes include technology ethics, academic integrity, AI philosophy, and the need for more comprehensive ethical guidelines in the use of AI in education. This study emphasizes the importance of integrating ethical values, philosophical thinking, and technology policies so that educational innovation remains humane and responsible. These findings provide a conceptual contribution to the development of a future technology-based educational ethics framework.
The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured.   Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game.   Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure/
Decolonization and the right to self-determination

The ideas about self-determination evolved from the Wilsonian understanding of self-governance - to a norm and drive for decolonization that changed the 20th century’s landscape. Despite its general proclamation as a right to all, the UN applied it as a “principle of saltwater”. Hence, the only legitimate right holder – colonial peoples could realize self-determination under several legal instruments and within the principle of uti possidetis juris that preserved the artificiality of the borders. The legal controversies of the decolonization processes are numerous and its loose end appears to be present up to now since the same principles were applied during the dissolution of the socialistic federations after the fall of the Iron Curtain.
County-scale crop yield prediction by integrating crop simulation with machine learning models

Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model prediction errors. Our results indicated that the prediction accuracy increases by coupling crop modeling with machine learning. The ensemble model overperformed the individual ML models, having a relative root mean square error (RRMSE) of about 9% for the test years (2018, 2019, and 2020), which is comparable to previous studies. In addition, analysis of the sources of error revealed that counties and crop reporting districts with low cropland ratios have high RRMSE. Furthermore, we found that soil input data and extreme weather events were responsible for high errors in some regions. The proposed models can be deployed for large-scale prediction at the county level and, contingent upon data availability, can be utilized for field level prediction.
Denoising Alignment with Large Language Model for Recommendation

The mainstream approach of GNN-based recommendation aggregates high-order ID information associated with the node in the user-item graph. The aggregation pattern using ID as signal has two disadvantages: lack of textual semantics and the impact of interaction noise. These disadvantages pose a threat to effectively learn user preferences, especially in capturing intricate user-item semantic relationships. Although             large language models (LLMs)             allow the integration of rich textual information into recommenders and have had groundbreaking applications in recommender systems, current works need to bridge the gap between different representation spaces. This is because LLM-based methods align the representations of GNN-based models only by using text embedding of LLM, leading to unsatisfactory results. To address this challenge, we propose a             denoising             alignment framework             with LLMs for GNN-based recommenders (DALR)             , which aims to align structural representation with textual representation and mitigate the effects of noise. Specifically, we propose a modeling framework that integrates the representation of graph structure with textual information from LLMs to capture intricate user-item interactions. We also suggest an alignment paradigm to enhance representation performance by aligning semantic signals from LLMs and structural features from GNN models. Additionally, we introduce a contrastive learning scheme to relieve the impact of noise and improve model performance. Extensive experiments on public datasets demonstrate that our model consistently outperforms the state-of-the-art methods. DALR achieves improvements ranging from 2.82% to 12.20% in Recall@5 and from 1.04% to 3.48% in NDCG@5 compared to the strongest baseline model, using the Steam dataset as an example.
Quantum Error Correction Codes and Their Impact on Scalable Quantum Computation: Current Approaches and Future Perspectives

Quantum Error Correction Codes and Their Impact on Scalable Quantum Computation: Current Approaches and Future Perspectives     Mehmet Keçeci  ORCID  : https://orcid.org/0000-0001-9937-9839, İstanbul, Türkiye  Received: 24.05.2025  “Article 3 of the series”  Abstract:      Quantum computers hold the potential to solve complex problems intractable for classical supercomputers. However, the inherent susceptibility of quantum systems to decoherence and environmental noise poses the most significant barrier to realizing this potential. Quantum Error Correction (QEC) codes (Quantum Error Correction (QEC) Codes [Unpublished pre-doctoral III. technical reports]. Gebze Technical University, Kocaeli, Türkiye [319, 461, 481, 482]) aim to preserve the integrity of quantum information by actively detecting and correcting these noisy effects, thereby enabling fault-tolerant, scalable quantum computation. This paper begins with the fundamental concepts in QEC, discussing early seminal approaches such as stabilizer codes, notably the Shor and Steane codes. It then focuses on topological error correction codes, which are currently an intensive area of research, particularly surface codes and color codes. The advantages of these codes, such as high error thresholds and local interaction requirements, are discussed alongside their drawbacks, including physical qubit overhead and challenges in implementing logical gates. The paper also examines alternative approaches like Low-Density Parity-Check (LDPC) codes and their potential benefits. Fundamental challenges in implementing QEC, the practical implications of the threshold theorem, the importance of noise modelling (including non-Markovian and correlated errors), and the role of characterization techniques like quantum process tomography and randomized benchmarking are highlighted. Finally, current research trends such as dynamic encoding, error suppression, and hardware-software co-design are evaluated, along with potential future directions and open problems for QEC strategies. This work aims to underscore the central role of QEC in the future of quantum computing and the importance of continuous progress in this field.     Keywords: Quantum Error Correction, QEC, Decoherence, Stabilizer Codes, Topological Codes, Surface Code, Fault-Tolerant Quantum Computation, Threshold Theorem, Quantum Noise.     Note: Citations and numbering are in continuation of the previous article.