ethicalabs/Research-Intent-Collab huggingface dataset:
Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning

Cell type annotation is a key task in analyzing the heterogeneity of single-cell RNA sequencing data. Although recent foundation models automate this process, they typically annotate cells independently, without considering batch-level cellular context or providing explanatory reasoning. In contrast, human experts often annotate distinct cell types for different cell clusters based on their domain knowledge. To mimic this workflow, we introduce the CellPuzzles task, where the objective is to assign unique cell types to a batch of cells. This benchmark spans diverse tissues, diseases, and donor conditions, and requires reasoning across the batch-level cellular context to ensure label uniqueness. We find that off-the-shelf large language models (LLMs) struggle on CellPuzzles, with the best baseline (OpenAI's o1) achieving only 19.0% batch-level accuracy. To fill this gap, we propose Cell-o1, a 7B LLM trained via supervised fine-tuning on distilled reasoning traces, followed by reinforcement learning with batch-level rewards. Cell-o1 achieves state-of-the-art performance, outperforming o1 by over 73% and generalizing well across contexts. Further analysis of training dynamics and reasoning behaviors provides insights into batch-level annotation performance and emergent expert-like reasoning. Code and data are available at https://github.com/ncbi-nlp/cell-o1.
Algorithms for Constructing Optimal Networks for Transport Logistics Problems in the Case of a Heterogeneous Environment

We study the problem of constructing an optimal network of centers in the problem of transport logistics. It is believed that the network is designed to serve the area M of the territory with a complex terrain. The cost of moving a transport on an elementary section of the path in the vicinity of any point depends on the coordinates of the point. An optimal network S is one for  which the cost of transporting from any given point in M to any given point in S is minimal. A nonEuclidean metric is proposed, the distance in which is equal to the minimum cost of transportation from one point to another along one of the routes. We have introduced a differential inclusion whose reachability sets coincide with circles in the new metric. The algorithms are based on partitioning the set M into areas of influence of current points from S and finding for each area a point that provides minimal costs. At the same time, the coordinates of new logistics centers are calculated based on the optimal trajectories connecting the current center with the points farthest from it in the non-Euclidean metric from its area of influence. We also have created a software package that implements the developed algorithms for building a network of centers, with the function of visualizing the areas of influence of network nodes S. An example of solving the problem for an area in which the relief defines the function of transportation costs, the level lines of which are ellipses with a common center, is considered.
Statistical inference for discretely sampled stochastic functional differential equations with small noise

AbstractEstimating parameters of drift and diffusion coefficients for multidimensional stochastic delay equations with small noise are considered. The delay structure is written as an integral form with respect to a delay measure. Our contrast function is based on a local-Gauss approximation to the transition probability density of the process. We show consistency and asymptotic normality of the minimum-contrast estimator when a small dispersion coefficient $$\varepsilon \rightarrow 0$$                                        ε                     →                     0                                     and sample size $$n\rightarrow \infty $$                                        n                     →                     ∞                                     simultaneously.
Biological Control as Part of the Soybean Integrated Pest Management (IPM): Potential and Challenges

Soybean production is usually performed on large scales, requiring simple but efficient pest management to be successful. Soybean fields are inhabited by several species of arthropods, demanding constant development of management practices to prevent pest outbreaks. More recently, stink bugs have become the most important pest group of soybeans in the Neotropics, responsible for up to 60% of the applied insecticides in Brazil. Natural enemies represent an important mortality factor that can keep the damage caused by stink bugs below the economic threshold levels without additional control actions. Thus, Conservation Biological Control (CBC) strategies can be adopted to preserve or even promote the increase in such natural enemies in the fields, or alternatively, massive releases of biocontrol agents in Augmentative Biological Control (ABC) programs could be adopted. Simple practices such as reducing insecticide use (with the adoption of economic thresholds), prioritizing harmless insecticides or biopesticides, and planting resistant soybean cultivars have been adopted in Brazil with positive results. The challenges to increasing the adoption of more complex stink bug management in commodity crops such as soybean may be overcome using the more recent economic incentives in the global agenda of decarbonized agriculture. The potential and challenges of conservation and augmentative biological control are further discussed in this review.
Extraction of social determinants of health from electronic health records using natural language processing

Purpose: Social Determinants of Health (SDoH) have a significant impact on hu- man health outcomes and disparities. Collecting SDoH from electronic health records can facilitate decision-making and downstream research. With thousands of clinical records, automated extraction methods using Artificial Intelligence (AI) would be more efficient and cost-effective. This study aims to autonomously extract comprehensive SDoH details from Electronic Health Records (EHR) using a Natural Language Processing (NLP) based AI pipeline. Methods: One thousand documents from BC Cancer with concentrated SDoH infor- mation were carefully selected and labeled to provide the ground truth for training and assessing the NLP models. Two pipelines were applied for SDoH extraction: an open- source pipeline trained on the BC Cancer dataset and an industrial pre-trained solution used as a benchmark. To optimize the performance of the first pipeline, three experiments were conducted to justify the effect of including subtype word positions during training on the extraction performance. The results of two pipelines were compared and the best- performing one was subsequently employed for the extraction of SDoH information from a total of 13,258 oncology documents. Results: The open-source pipeline gained an average F1 score of 0.88 on the validation dataset for extracting 13 SDoH factors, outperforming the benchmark by 5%. This pipeline also demonstrated a notably superior capability to extract detailed subtypes compared with the benchmark. The benchmark was advantageous in identifying rarely documented SDoH types in the data for extraction in this work. Subsequently, the pipeline was applied to the oncology documents from BC Cancer to extract 60,717 SDoH factors and associated details. The most frequently extracted SDoH were Tobacco Use, Employment Status, Marital Status, Alcohol Consumption, and Living Status, which occurred from 8k to 12k times. Conclusion: The NLP pipeline successfully extracted a wide array of SDoH factors from clinical notes, achieving commendable performance despite being trained on a rela- tively small labeled dataset.
Monodense Deep Neural Model for Determining Item Price Elasticity

Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network.   (1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model   We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.
Optimizing enzymatic catalysts for rapid turnover of substrates with low enzyme sequestration

We analyse the mechanism of enzyme-substrate catalysis from the perspective of minimizing the load on the enzymes through sequestration, whilst maintaining at least a minimum reaction flux. In particular, we ask: which binding free energies of the enzyme-substrate and enzyme-product reaction intermediates minimize the fraction of enzymes sequestered in complexes, while sustaining at a certain minimal flux? Under reasonable biophysical assumptions, we find that the optimal design will saturate the bound on the minimal flux, and reflects a basic trade-off in catalytic operation. If both binding free energies are too high, there is low sequestration, but the effective progress of the reaction is hampered. If both binding free energies are too low, there is high sequestration, and the reaction flux may also be suppressed in extreme cases. The optimal binding free energies are therefore neither too high nor too low, but in fact moderate. Moreover, the optimal difference in substrate and product binding free energies, which contributes to the thermodynamic driving force of the reaction, is in general strongly constrained by the intrinsic free-energy difference between products and reactants. Both the strategies of using a negative binding free-energy difference to drive the catalyst-bound reaction forward, and of using a positive binding free-energy difference to enhance detachment of the product, are limited in their efficacy.
Revolutionizing Cancer Care: The Role of Artificial Intelligence in Diagnosis, Prognosis, and Personalized Medicine

cancer remains a leading cause of morbidity and mortality worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. The increasing burden of cancer, driven by population growth and aging, necessitates innovative solutions to improve diagnosis, prognosis, and treatment outcomes. Artificial Intelligence has emerged as a transformative tool in oncology, offering significant potential in cancer detection, diagnosis, and personalized treatment strategies. This review explores the real-world applications of Artificial Intelligence in oncology, focusing on lung cancer and  breast cancer, two of the most prevalent and deadly cancers globally. Artificial Intelligence-driven technologies, particularly in imaging, pathology, and genomics, have demonstrated remarkable success in enhancing early detection, diagnostic accuracy, and treatment planning. In lung cancer, Artificial Intelligence-powered imaging tools, such as deep learning models, have shown high sensitivity and specificity in detecting small pulmonary nodules, often missed by traditional methods. Similarly, in breast cancer, Artificial Intelligence has proven effective in mammography interpretation, reducing false positives and false negatives, and alleviating the workload of radiologists. Despite its promising potential, the integration of Artificial Intelligence into clinical practice faces several challenges, including issues related to data quality, algorithmic biases, and ethical considerations. The "black box" nature of many Artificial Intelligence systems poses a significant barrier to clinical acceptance, highlighting the need for explainable Artificial Intelligence to provide transparent and interpretable decision-making processes. Furthermore, the successful implementation of Artificial Intelligence in oncology requires robust regulatory frameworks and standardized protocols to ensure patient safety and data security. This review underscores the transformative potential of Artificial Intelligence in revolutionizing cancer care, emphasizing the importance of addressing key challenges to harness its full potential. By enhancing early detection, reducing diagnostic errors, and enabling personalized treatment strategies, Artificial Intelligence has the potential to significantly improve patient outcomes and reduce the global burden of cancer. However, its successful integration into clinical practice will depend on interdisciplinary collaboration, ethical considerations, and a commitment to responsible implementation.
Book review: David palmeter & Petros C. Mavroides, Dispute settlement in the World Trade Organization; Practice and Procedure

TBA
Project-based learning course on metabolic network modelling in computational systems biology

Project-based learning (PBL) is a dynamic student-centred teaching method that encourages students to solve real-life problems while fostering engagement and critical thinking. Here, we report on a PBL course on metabolic network modelling that has been running for several years within the Master in Integrated Systems Biology (MISB) at the University of Luxembourg. This 2-week full-time block course comprises an introduction into the core concepts and methods of constraint-based modelling (CBM), applied to toy models and large-scale networks alongside the preparation of individual student projects in week 1 and, in week 2, the presentation and execution of these projects. We describe in detail the schedule and content of the course, exemplary student projects, and reflect on outcomes and lessons learned. PBL requires the full engagement of students and teachers and gives a rewarding teaching experience. The presented course can serve as a role model and inspiration for other similar courses.
4 The section on materials and methods in published reports of randomized controlled trials (RCTs) does not provide sufficient information to allow clinical replicability of complex interventions: a cochrane rehabilitation methodological paper

Objectives  To study if Randomized Controlled Trials (RCTs) on complex interventions published in top journals include all the practical details needed to replicate the intervention in everyday clinical practice (clinical replicability). We chose rehabilitation as a case-study because the World Health Organization calls for its development within health services, and due to its intrinsic complexities.  Method  Online survey of a pre-defined sample of clinical expert teams from different world regions with diverse rehabilitation competences. Forty-seven clinicians from 7 Physical and Rehabilitation Medicine (PRM) teams (Belgium, Italy, Malaysia, Pakistan, Poland, Puerto Rico, USA), including 20 physicians, 12 physiotherapists, 6 occupational therapists, 6 psychologists and 3 others. The team leaders were active researchers. All RCTs published between January and July 2017 in the top PRM journals (76 RCTs) were reviewed by each team leader. 14 questions developed using CONSORT and TIDIeR checklists through consensus and piloting.  Results  The response rate was 99%. Inter-rater agreement was moderate/good. All participants considered unanimously 12 (16%) RCTs clinically replicable and none not replicable. Of the other, 56 (74%) RCTs have been considered replicable and 45 (59%) not replicable by at least one complete team. At least one ‘absent’ information was found by all participants in 60 RCTs (79%), and by a minimum of 85% in the remaining 16 (21%). Information considered to be less well described (8-19% ‘perfect’ information) included two providers (skills, experience) and two delivery (cautions, relationships) items. The best described (50-79% ‘perfect’) were the classic methodological items included in CONSORT (descending order: participants, materials, procedures, setting and intervention).  Conclusions  This rehabilitation case-study shows problems of clinical replicability of RCTs on complex interventions and suggests the need to better define some clinical items not described by classical methodological checklists like CONSORT.
WAYS OF REPORTING CORPORATE SOCIAL RESPONSABILITY CASE STUDY: THE STATE OF REPORTING OF COMPANIES OPERATING IN THE FIELD OF TEXTILES AND APPAREL

In this paper we have analysed the current state of reporting CSR activity of companies activating in the textiles and apparel industry. Noticing that the number of companies that invest in CSR programs is increasing on a world-wide level and in the last years even in Romania, we wanted to analyse the situation of the companies from a field of activity less involved in CSR programs. The importance of CSR programs has increased more and more in recent times, from a reputation point of view but mostly of reasons of a stronger involvement of companies in the communities where they are present, but also in educating their own employees which become more motivated if they can participate in various actions undertaken through CSR programs. The implementation of such programs adds value to the businesses developed by the company, taking into account that more segments of consumers have expressed their availability to buy products made by companies that promote such programs. The visibility of CSR projects is made by presenting the programs that the company promotes in sustainability reports, by using means and indicators defined under the Global Reporting Initiative. Following the study we have made, we found out that although world-wide the number of companies that have CSR programs and make sustainability reports is increasing, the number of companies in the textiles and apparel industry that make such reports is very small.
Stochastic thermodynamics of chemical reaction networks

For chemical reaction networks in a dilute solution described by a master equation, the authors define energy and entropy on a stochastic trajectory and develop a consistent nonequilibrium thermodynamic description along a single stochastic trajectory of reaction events. A first-law like energy balance relates internal energy, applied (chemical) work, and dissipated heat for every single reaction. Entropy production along a single trajectory involves a sum over changes in the entropy of the network itself and the entropy of the medium. The latter is given by the exchanged heat identified through the first law. Total entropy production is constrained by an integral fluctuation theorem for networks arbitrarily driven by time-dependent rates and a detailed fluctuation theorem for networks in the steady state. Further exact relations such as a generalized Jarzynski relation and a generalized Clausius inequality are discussed. The authors illustrate these results for a three-species cyclic reaction network which exhibits nonequilibrium steady states as well as transitions between different steady states.
Here Come the Judges: A Judicial Response to Anticipated Concerns over a Statewide Criminal Sentencing Database—Aligning Algorithmic Risk Assessments with Criminal Justice Values

Abstract                We are Ohio state court judges with actual experience in imposing and/or reviewing criminal felony sentences. We are unaware of any expressed objection by Ohio’s judiciary to having more relevant information available to a sentencing court.                Sentencing discretion is conferred upon trial courts by the General Assembly through its statutory enactments, and the trial courts have full discretion to impose a prison sentence that falls within the prescribed statutory range. With such wide-ranging discretion the database will provide additional information to a sentencing judge, which will result in a more informed sentence.                Justice requires that judges get it “right.” Why prevent useful, relevant, and important information from being considered by trial judges in achieving this goal? Once the criminal sentencing database is in place, for the first time in the state’s history all stakeholders, including judges, will have access to relevant information that can inform their sentencing decisions while keeping biases, implicit or otherwise, in check. This will usher in a fairer, more transparent system that we should all support.                As stewards of the greatest system ever designed to resolve disputes and, therefore, as those empowered to see that justice is achieved, we can’t afford to pretend that the problem doesn’t exist. We must seize this moment and take this long-overdue step for the betterment of our judicial system and, therefore, our society.
Coping, Quality of Life, Depression, and Hopelessness in Cancer Patients in a Curative and Palliative, End-of-Life Care Setting

Coping strategies may be important factors influencing quality of life (QOL), depression, and hopelessness. However, most studies on this issue were performed in patients still undergoing anticancer treatment. Unknown is which coping strategies are of importance for palliative-cancer patients who no longer receive treatment.The objectives of this study were to assess coping strategies in curatively treated and palliative-cancer patients no longer receiving anticancer treatment and to examine the relation of these coping strategies with QOL, depression, and hopelessness.A descriptive research design was used. Ninety-two curative and 59 palliative patients filled out the COPE-Easy abbreviated version, the European Organisation for Research-and-Treatment of Cancer QOL-Questionnaire version 2.0, Beck Depression Inventory for Primary Care, and Beck Hopelessness Scale.In both curative and palliative patients, active coping strategies and acceptance were beneficial in terms of QOL, depression, and hopelessness, unlike avoidant coping strategies and venting of emotions. Palliative patients scored higher on the coping strategy, seeking moral support. For the outcome variable, emotional functioning, significant interactions were observed between the variable, curative/palliative care setting, and the coping strategy, seeking moral support. For the outcome variable, role functioning, significant interactions were observed between the variable, curative/palliative care setting, and the coping strategy, waiting.Coping strategies were significantly correlated to QOL, depression, and hopelessness. However, this correlation differed in the curative and palliative, end-of-life care setting.The observed relations between coping strategies, QOL, depression, and hopelessness give room to cognitive-behavioral nursing interventions. Specific attention is needed for differences in coping strategies between curative and palliative patients.