ethicalabs/Research-Intent-Collab huggingface dataset:
Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction

Protein function prediction is essential for elucidating biological processes and accelerating drug discovery. However, the vast number of unannotated protein sequences and the limited availability of experimentally validated functional data remain major challenges. Although deep learning models based on protein sequences or protein-protein interaction networks have shown promise, their performance is still restricted, particularly for proteins without interaction data. Furthermore, many existing approaches treat sequence and structural information separately, potentially resulting in suboptimal feature representations. To address these limitations, we propose an improved graph-based framework that integrates two key innovations: (i) ESM-2, a state-of-the-art protein language model, to generate semantically rich sequence embeddings; and (ii) a hybrid pooling mechanism within graph convolutional blocks to better capture both global and local structural features from AlphaFold2-predicted structures. Experiments on the human proteome demonstrate that our model consistently outperforms existing methods in predicting molecular function, cellular component, and biological process annotations. These findings highlight the advantages of combining advanced sequence representations with enhanced structural learning for accurate and generalizable protein function prediction.
Investigation on the mechanical stresses in a muffler mounting bracket using Root Cause Failure Analysis (RCFA), finite element analysis and experimental validation

Abstract   In the present work, experimental investigation on the failure of a muffler mounting bracket attached to commercial vehicle is done. Cracks are identified at the welded location of muffler mount which shows that weld joint has better strength than the muffler/bracket body. To understand the possible root causes of the failure, fishbone diagram was used, which helped in determining the major causes of the failure by a graphical representation. Further, the three parameter Weibull distribution was also developed to determine the Mean Time to Failure (MTTF) life which was found to be 15,172 km. In addition, tensile testing of sheet metal was performed on the sheets which was used in the manufacturing of Muffler. Furthermore, a Thermo-Mechanical coupled analysis was carried out using commercial code, ANSYS 16.0, which adapts Finite Element Analysis (FEA) formulation. The thermal loads on muffler were imported to structural analysis along with a static load of 4 g acceleration were imposed on the muffler body to simulate the effects of high impact loads. The effect of temperatures resulted in high von-misses stresses which were in agreement with practical observations. It is observed that the stresses at bracket region were at the same location both from the experimental and finite element analysis. Further, by considering different mounting design concepts, multiple analyses were carried out to determine the most suitable brackets for the muffler. The present work attempts to diagnose the cause of failure and to make the fail-safe design.
Communicating to and engaging with the public in regulatory science[Link]

This paper presents selected highlights from the 'Engaging with society' session of EFSA's third Scientific Conference 'Science, Food and Society' (Parma, Italy, 18-21 September 2018). The social dimension for scientific advisory bodies largely concerns science communication and public engagement. The political, economic and technological transformation of contemporary societies is challenging conventional structures and approaches in these areas. The disintermediation of communication and the proliferation of misinformation, it is argued, herald the onset of the post-truth society. A better understanding of the way individuals consume information today has led to the development of tools to guide mediators such as journalists and communication specialists in countering these trends. Public engagement can reinforce confidence in regulatory bodies and potentially contribute to the quality of the scientific process. Scientific advisory bodies in Europe have created strategies and mechanisms to engage the public that are designed to increase transparency and representativeness. To be effective, several engagement mechanisms are needed, although factors such as resource constraints, institutional culture and public/stakeholder attitudes may limit their development. In conclusion, a more vigorous role for social research is needed to place scientific risk assessment within broader socio-economic and political contexts. Social science expertise can help to define more impactful public information strategies and to explore the potential opportunities that engaged stakeholders and citizens can make to sustain and strengthen regulatory science.
Unforgettable Generalization in Language Models

When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change? We study the behavior of transformer LMs in which tasks have been forgotten via fine-tuning on randomized labels. Such LMs learn to generate near-random predictions for individual examples in the "training'' set used for forgetting. Across tasks, however, LMs exhibit extreme variability in whether LM predictions change on examples outside the training set. In some tasks (like entailment classification), forgetting generalizes robustly, and causes models to produce uninformative predictions on new task instances; in other tasks (like physical commonsense reasoning and scientific question answering) forgetting affects only the training examples, and models continue to perform the "forgotten'' task accurately even for examples very similar to those that appeared in the training set. Dataset difficulty is not predictive of whether a behavior can be forgotten; instead, generalization in forgetting is (weakly) predicted by the confidence of LMs' initial task predictions and the variability of LM representations of training data, with low confidence and low variability both associated with greater generalization. Perhaps most surprisingly, random-label forgetting appears to be somewhat insensitive to the contents of the training set: for example, models trained on science questions with random labels continue to answer other science questions accurately, but begin to produce random labels on entailment classification tasks. Finally, we show that even generalizable forgetting is shallow: linear probes trained on LMs' representations can still perform tasks reliably after forgetting. Our results highlight the difficulty and unpredictability of performing targeted skill removal from models via fine-tuning.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective

Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs. However, most of the existing methods heavily rely on domain experts, require excessive computational cost, or suffer from sub-optimal performance. On the other hand, Large Language Models (LLMs), like ChatGPT, have shown remarkable performance in various cross-modal tasks due to their powerful capabilities in natural language understanding, generalization, and in-context learning (ICL), which provides unprecedented opportunities to advance molecule discovery. Despite several previous works trying to apply LLMs in this task, the lack of domain-specific corpus and difficulties in training specialized LLMs still remain challenges. In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning. MolReGPT leverages the principle of molecular similarity to retrieve similar molecules and their text descriptions from a local database to enable LLMs to learn the task knowledge from context examples. We evaluate the effectiveness of MolReGPT on molecule-caption translation, including molecule understanding and text-based molecule generation. Experimental results show that compared to fine-tuned models, MolReGPT outperforms MolT5-base and is comparable to MolT5-large without additional training. To the best of our knowledge, MolReGPT is the first work to leverage LLMs via in-context learning in molecule-caption translation for advancing molecule discovery. Our work expands the scope of LLM applications, as well as providing a new paradigm for molecule discovery and design.
An EU seat at the Security Council: reflections on formal and informal models of EU participation at the United Nations

peer reviewed
Thermodynamic Uncertainty Relation and Thermodynamic Speed Limit in Deterministic Chemical Reaction Networks

9 pages, 4 figures
Promotion of Pre- and Post-Transplant Physical Exercise in the Emilia-Romagna Region: The Network of the Program “Transplantation, Physical Activity, and Sport”

Following the positive experience of the national project "A transplant...and now it's time for sport," the Transplant Reference Center of the Emilia-Romagna Region has pursued the promotion of pre- and post-transplant physical exercise by developing a network.The path involved the transplant centers and operative units (UU.OO) who wanted to target transplant and waiting list patients, who are clinically stable, to perform personalized exercise through a program (supervised or not) prescribed by a specialist in sports medicine. With the collaboration of the Collective Prevention and Public Health Service, the network was established, consisting of the sports medicine centers and the gyms that promote health for adapted physical activity (PS-AMA). To implement the network, training courses for all the professionals involved (doctors, nurses, exercise specialists) and operational meetings in the transplant centers-nephrology units with patients' associations have been organized.To date, there are 14 transplant centers and UU.OO, 9 sports medicine centers, and 45 PS-AMA involved in this network. Seven training courses were organized with the participation of 193 health professionals. Since January 2016, there have been 65 transplanted patients and 5 patients on the waiting list who practice the prescribed exercise. Of these, 45 carry out supervised exercise in PS-AMA; 25 perform autonomous exercise. Each patient is monitored every 6 months. No problems related to the exercise performance were recorded.The development of a network of professionals and associations is the key element to raise awareness of physical activity among transplanted and waiting-for-transplant patients, reducing the pathologies associated with a sedentary lifestyle.
Study of muons from ultrahigh energy cosmic ray air showers measured with the Telescope Array experiment

One of the uncertainties in interpretation of ultra-high energy cosmic ray (UHECR) data comes from the hadronic interaction models used for air shower Monte Carlo (MC) simulations. The number of muons observed at the ground from UHECR-induced air showers is expected to depend upon the hadronic interaction model. One may therefore test the hadronic interaction models by comparing the measured number of muons with the MC prediction. In this paper, we present the results of studies of muon densities in UHE extensive air showers obtained by analyzing the signal of surface detector stations which should have high $\it{muon \, purity}$. The muon purity of a station will depend on both the inclination of the shower and the relative position of the station. In 7 years' data from the Telescope Array experiment, we find that the number of particles observed for signals with an expected muon purity of $\sim$65% at a lateral distance of 2000 m from the shower core is $1.72 \pm 0.10{\rm (stat.)} \pm 0.37 {\rm (syst.)}$ times larger than the MC prediction value using the QGSJET II-03 model for proton-induced showers. A similar effect is also seen in comparisons with other hadronic models such as QGSJET II-04, which shows a $1.67 \pm 0.10 \pm 0.36$ excess. We also studied the dependence of these excesses on lateral distances and found a slower decrease of the lateral distribution of muons in the data as compared to the MC, causing larger discrepancy at larger lateral distances.
HEALTH TECHNOLOGY ASSESSMENT – IS IT THE WAY TO SEARCH FOR COST – EFFECTIVE MEDICINES IN CENTRAL AND EASTERN EUROPE?

Health Technology Assessment (HTA) is a multidisciplinary process that summarizes information about medical, social, economic and ethical issues related to the use of a health technology in a systematic, transparent, unbiased, robust manner. HTA constitutes an integral part of a decision making process covering, among others, pricing and reimbursement of drugs and medical devices in Central and Eastern Europe. In this review I describe the main definitions and elements of HTA placed in a context of healthcare systems’ reforms with a special emphasis on budget control tools in the Region. I also give information on some model solutions, brief history, main limitations, challenges and conditions of further development of the domain, together with some discussion from the pharmaceutical industry’s perspective.
Effect of workplace organizational culture on occupational health and safety with the mediating role of job satisfaction

Introduction: Occupational health and safety issues have consistently been of particular importance in workplaces. Creating healthy and safe workplaces not only preserves employee wellbeing, but also enhances their productivity and job satisfaction. In this regard, organizational culture serves as  a significant influence on employee behavior and enhancement of working conditions. This study investigated the effect of organizational culture in the workplace on occupational health and safety of employees in the healthcare network of Yazd City and analyzes the mediating role of job satisfaction in this regard.  Materials and Methods: The present study was applied in terms of purpose and descriptive-analytical in terms of method, and structural equation analysis was used to examine the relationships among variables. The data collection tool was a standard questionnaire with verified validity and reliability, and the data analysis was performed using SMART-PLS software.  Results: The results of the study indicated that the organizational culture of the workplace positively and significantly impacts employees’ occupational health and safety, while job satisfaction as a mediator enhances the connection between organizational culture and occupational safety. The path coefficient between organizational culture at the workplace to occupational safety stood 0.534, with a t statistic of 7.283, indicating the favorable effect of this variable on the occupational safety of employees.  Conclusion: The results of the study show the significant effect of organizational culture on the job satisfaction of employees and the effect of job satisfaction on improving the occupational safety situation. This study generally emphasizes that improving organizational culture and paying attention to the job satisfaction of employees can create significant improvements in occupational health and safety.
Assessing the digital well-being of educational technologies supported by learning analytics

The accelerating adoption of digital technologies creates a direct relationship between the status of individual and societal well-being on one hand, and the state of the digital technologies that underpin human life experiences on the other. This technological shift is represented in the domain of Education by Technology-Enhanced Learning (TEL), a field of research and practice. The potential of technological advances to enhance learning has been well explored, as well as many of the challenges to the effective use of technology in education. Less research has been undertaken, however, on the ethical considerations related to TEL use and research. When data analytics are merged, which is present in TEL in the form of Learning Analytics (LA), the ethical concerns about the impact of digital technologies on human well-being become more relevant. This doctoral thesis aims at exploring indicators and methods that can support the assessment of the well-being impacts of LA-supported educational technologies. Value-sensitive design (VSD) methodology and elements of the IEEE P7010 Well-being Impact Assessment (WIA) were followed to achieve the objectives of this thesis. The term well-being was conceptualized in the TEL field through workshops with LA tool’s developers and a systematic literature review, resulting in around 70 well-being indictors covering twelve life domains. The findings indicated that the well-being impact of TEL is more scoped in four main domains: affect, community, psychology, and education. Samples of users were consulted about the well-being indicators of two cases of LA-supported educational technologies, which were learning design community platforms and a computer-supported collaborative learning (CSCL) tool. Data collection scenarios were applied to enable the assessment of the CSCL tool on certain aspects of teachers’ and learners’ well-being, particularly teacher-perceived stress and students’ basic psychological needs satisfaction. The findings shed light on how the tool’s interface support the student’s basis psychological needs of autonomy, relatedness, and competence, indicating that relatedness and completeness are more perceived by the students who participated in collaborative learning activities supported by the tool. The triggers of teacher-perceived stress when orchestration those activities were found to be either technological difficulties, actions by students or time-related issues.
US employer agrees to stop genetic testing

A US freight railway company has agreed to stop requiring the genetic testing of employees who file claims for a wrist condition called the carpal tunnel syndrome. The US Equal Employment Opportunity Commission had filed a lawsuit against Burlington Northern Santa Fe alleging that the policy violated the Americans With Disabilities Act.  A railway worker who refused to provide a blood sample after filing an injury claim was threatened with dismissal, the commission said, in its first legal challenge against genetic testing by employers. A spokesman …
Computational Complexity of the Interleaving Distance

The interleaving distance is arguably the most prominent distance measure in topological data analysis. In this paper, we provide bounds on the computational complexity of determining the interleaving distance in several settings. We show that the interleaving distance is NP-hard to compute for persistence modules valued in the category of vector spaces. In the specific setting of multidimensional persistent homology we show that the problem is at least as hard as a matrix invertibility problem. Furthermore, this allows us to conclude that the interleaving distance of interval decomposable modules depends on the characteristic of the field. Persistence modules valued in the category of sets are also studied. As a corollary, we obtain that the isomorphism problem for Reeb graphs is graph isomorphism complete.
Multivariate Time Series Data Mining in Ship Monitoring Database

A framework is presented for data mining in multivariate time series collected over hours of ship operation to extract vessel states from the data. The measurements made by a ship monitoring system lead to a collection of time-organized in-service data. Usually, these time series datasets are big, complicated, and highly dimensional. The purpose of time-series data mining is to bridge the gap between a massive database and meaningful information hidden behind the data. An important aspect of the framework proposed is selecting relevant variables, eliminating unnecessary information or noises, and extracting the essential features of the problem so that the vessel behavior can be identified reliably. Principal component analysis (PCA) is employed to address the issues of multicollinearity in the data and dimensionality reduction. The data mining approach itself is established on unsupervised data clustering using self-organizing map (SOM) and k-means, and k-nearest neighbors search (k-NNS) for searching and recovering specific information from the database. As a case study, the results are based on onboard monitoring data of the Norwegian University of Science and Technology (NTNU) research vessel, “Gunnerus.” The scope of this work is limited to detecting ship maneuvers. However, it is extendable to a wide range of smart marine applications. As illustrated in the results, this approach is effective in identifying the prior unknown states of the ship with acceptable accuracy.
Contextualizing Risk Perception and Trust in the Community-Based Response to Ebola Virus Disease in Liberia

The 2014–15 Ebola Virus Disease (EVD) outbreaks in Western Africa became widespread in primarily three countries, Guinea, Liberia, and Sierra Leone. Unlike all previous outbreaks in Central and East Africa, which were confined to rural areas, the virus spread rapidly through West Africa as a result of transmission through high-density urban centres coupled with the effects of public distrust in outbreak response teams and local government officials. Objective: In this study, we examine the EVD epidemic in Liberia, the first country to implement a community-based response that led to changes in the trajectory of the epidemic. The focus on the role of community-based initiatives in outbreak response is often neglected in conventional epidemiological accounts. In this light, we consider the manner in which community-based strategies enabled a more effective response based on the establishment of better trust relations and an enhanced understanding of the risks that EVD posed for the community. Methodology: We conducted qualitative research in five distinct communities in Liberia three years after the outbreaks subsided. Data collection procedures consisted of semi-structured interviews and focus group discussions with residents. Results: We found that the implementation of a community-based response, which included the participation of Ebola survivors and local leaders, helped curb and ultimately end the EVD epidemic in Liberia. As community members became more directly involved in the EVD response, the level of trust between citizens, local officials, and non-governmental organization response teams increased. In turn, this led to greater acceptance in abiding to safety protocols, greater receptiveness to risk information, and changes in mobility patterns—all of which played a significant role in turning the tide of the epidemic.
Solar wind-magnetosphere drivers of space weather

Abstract   Space weather is a term which refers to the dynamic, highly variable conditions in the geospace environment. This includes conditions on the sun, in the interplanetary medium, and in the magnetosphere-ionosphere-thermosphere system. Adverse changes in the near-Earth space environment can diminish the performance and reliability of both spacecraft and ground-based systems. This, in turn, can spell major losses due to communication, navigation, power system, and reconnaissance satellite operational problems. This paper discusses some of the principal adverse space environmental effects presently known including trapped magnetospheric radiation, solar energetic particles, geomagnetic storms, and magnetospheric substorms. Methods for predicting changes in space weather are also considered. It is concluded that practical and effective methods of predicting and mitigating space weather effects are close at hand.
Self-Supervised Contrastive Learning for Cross-Domain Hyperspectral Image Representation

Accepted to ICASSP 2022