Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo.

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce "RetChemQA", a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 question and answers (Q&As) for each type.

Long-term effects of a prehospital telemedicine system on structural and process quality indicators of an emergency medical service.

The benefits of a telemedical support system for prehospital emergency medical services include high-level emergency medical support at the push of a button: delegation of drug administration, diagnostic assistance, initiation of therapeutic measures, or choice of hospital destination. At various European EMS sites telemedical routine systems are shortly before implementation. The aim of this study was to investigate the long-term effects of implementing a tele-EMS system on the structural and procedural quality indicators and therefore performance of an entire EMS system.

Shaping the Water-Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models.

We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies.

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs.

We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis.

A GPT-4 Reticular Chemist for Guiding MOF Discovery.

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration.

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis.

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself.

[Update tele-emergency medicine : Status quo and perspectives].

Current Status Of Emergency Medicine In Germany: Increasing numbers of rescue missions in recent years have led to a growing staff shortage of paramedics as well as physicians in the emergency medical system (EMS) with an urgent need for optimized usage of resources. One option is the implementation of a tele-EMS physician system, which has been established in the EMS of the City of Aachen since 2014.

Implementation Of Tele-emergency Medicine: In addition to pilot projects, political decisions lead to the introduction of tele-emergency medicine.

Immobilization in Emergency Medical Service - Are CSR and NEXUS-Criteria Considered? A Matched-Pairs Analysis Between Trauma Patients Treated by Onsite EMS Physicians and Patients Treated by Tele-EMS Physicians.

Background: The NEXUS-low-risk criteria (NEXUS) and Canadian C-spine rule (CSR) are clinical decision tools used for the prehospital spinal clearance in trauma patients, intending to prevent over- as well as under immobilization. Since 2014, a holistic telemedicine system is part of the emergency medical service (EMS) in Aachen (Germany). This study aims to examine whether the decisions to immobilize or not by EMS- and tele-EMS physicians are based on NEXUS and the CSR, as well as the guideline adherence concerning the choice of immobilization device.

Estimating the total treatment effect in randomized experiments with unknown network structure.

Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological sciences, from the social sciences to engineering, and from public policy to the technology industry. Here we consider situations where classical methods for estimating the total treatment effect on a target population are considerably biased due to confounding network effects, i.e.

Impacts and Lessons Learned of the First Three COVID-19 Waves on Cross-Border Collaboration in the Field of Emergency Medical Services and Interhospital Transports in the Euregio-Meuse-Rhine: A Qualitative Review of Expert Opinions.

Background: In the Euregio-Meuse-Rhine (EMR), cross-border collaboration is essential for resource-saving and needs-based patient care within the emergency medical service (EMS) systems and interhospital transport (IHT). However, at the onset of the novel coronavirus SARS-COV-2 (COVID-19) pandemic, differing national measures highlighted the fragmentation within the European Union (EU) in its various approaches to combating the pandemic. To assess the consequences of the pandemic in the EMR border area, the aim of this study was to analyze the effects and "lessons learned" regarding cross-border collaboration in EMS and IHT.

[Quality of documentation and treatment in the non-physician staffed ambulance: a retrospective analysis of emergency protocols from the city of Aachen].

Background: Each year there are 7.3 million emergencies for the German rescue service, trend rising and around 59% of the emergency patients are treated by paramedics only; however, most of the studies focus on physicians, while their practical skills at the scene are rarely necessary. Accordingly, the responsibility for the patient lies with the paramedics most of the time.

Perioperative Vitamin C and E levels in Cardiac Surgery Patients and Their Clinical Significance.

Background: Oxidative stress contributes to organ dysfunction after cardiac surgery and still represents a major problem. Antioxidants, such as vitamins C and E might be organ protective.

Methods: The primary objective of this prospective observational study was the description to evaluate the perioperative vitamin C and E levels in 56 patients undergoing cardiac surgery with the use of cardiopulmonary bypass.

A multifactorial model of T cell expansion and durable clinical benefit in response to a PD-L1 inhibitor.

Checkpoint inhibitor immunotherapies have had major success in treating patients with late-stage cancers, yet the minority of patients benefit. Mutation load and PD-L1 staining are leading biomarkers associated with response, but each is an imperfect predictor. A key challenge to predicting response is modeling the interaction between the tumor and immune system.

Sociodemographic differences in linkage error: an examination of four large-scale datasets.

Background: Record linkage is an important tool for epidemiologists and health planners. Record linkage studies will generally contain some level of residual record linkage error, where individual records are either incorrectly marked as belonging to the same individual, or incorrectly marked as belonging to separate individuals. A key question is whether errors in linkage quality are distributed evenly throughout the population, or whether certain subgroups will exhibit higher rates of error.

Protecting Record Linkage Identifiers Using a Language Model for Patient Names.

Linking information across databases fosters new research in the medical sciences. Recent European privacy regulations recommend encrypting personal identifiers used for linking. Bloom filter based methods are an increasingly popular Record Linkage method.

Evaluating privacy-preserving record linkage using cryptographic long-term keys and multibit trees on large medical datasets.

Background: Integrating medical data using databases from different sources by record linkage is a powerful technique increasingly used in medical research. Under many jurisdictions, unique personal identifiers needed for linking the records are unavailable. Since sensitive attributes, such as names, have to be used instead, privacy regulations usually demand encrypting these identifiers.

Combining Different Privacy-Preserving Record Linkage Methods for Hospital Admission Data.

Record linkage (RL) is the process of identifying pairs of records that correspond to the same entity, for example the same patient. The basic approach assigns to each pair of records a similarity weight, and then determines a certain threshold, above which the two records are considered to be a match. Three different RL methods were applied under privacy-preserving conditions on hospital admission data: deterministic RL (DRL), probabilistic RL (PRL), and Bloom filters.

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes.

In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Here, we study the difficult case of networks with discrete weights, where the optimization landscape is very rough even for simple architectures, and provide theoretical and numerical evidence of the existence of rare-but extremely dense and accessible-regions of configurations in the network weight space.

Sharing information to reconstruct patient-specific pathways in heterogeneous diseases.

Advances in experimental techniques resulted in abundant genomic, transcriptomic, epigenomic, and proteomic data that have the potential to reveal critical drivers of human diseases. Complementary algorithmic developments enable researchers to map these data onto protein-protein interaction networks and infer which signaling pathways are perturbed by a disease. Despite this progress, integrating data across different biological samples or patients remains a substantial challenge because samples from the same disease can be extremely heterogeneous.

Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem.

Signaling and regulatory networks are essential for cells to control processes such as growth, differentiation, and response to stimuli. Although many "omic" data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases and to propose new therapeutic strategies.

Finding undetected protein associations in cell signaling by belief propagation.

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles.

Statistical mechanics of steiner trees.

The minimum weight Steiner tree (MST) is an important combinatorial optimization problem over networks that has applications in a wide range of fields. Here we discuss a general technique to translate the imposed global connectivity constrain into many local ones that can be analyzed with cavity equation techniques. This approach leads to a new optimization algorithm for MST and allows us to analyze the statistical mechanics properties of MST on random graphs of various types.

Emergence of tempered preferential attachment from optimization.

We show how preferential attachment can emerge in an optimization framework, resolving a long-standing theoretical controversy. We also show that the preferential attachment model so obtained has two novel features, saturation and viability, which have natural interpretations in the underlying network and lead to a power-law degree distribution with exponential cutoff. Moreover, we consider a generalized version of this preferential attachment model with independent saturation and viability, leading to a broader class of power laws again with exponential cutoff.