Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.Comment: Accepted at International Conference on Wirtschaftsinformatik, 202

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification

AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, and meteorological agencies in building digital twins of the Earth. Recent approaches using transformers, physics-informed machine learning, and graph neural networks have demonstrated state-of-the-art performance on relatively narrow spatiotemporal scales and specific tasks. With the recent success of generative artificial intelligence (AI) using pre-trained transformers for language modeling and vision with prompt engineering and fine-tuning, we are now moving towards generalizable AI. In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks. Despite this progress, we are still in the nascent stages of a generalizable AI model for global Earth system models, regional climate models, and mesoscale weather models. Here, we review current state-of-the-art AI approaches, primarily from transformer and operator learning literature in the context of meteorology. We provide our perspective on criteria for success towards a family of foundation models for nowcasting and forecasting weather and climate predictions. We also discuss how such models can perform competitively on downstream tasks such as downscaling (super-resolution), identifying conditions conducive to the occurrence of wildfires, and predicting consequential meteorological phenomena across various spatiotemporal scales such as hurricanes and atmospheric rivers. In particular, we examine current AI methodologies and contend they have matured enough to design and implement a weather foundation model.Comment: 44 pages, 1 figure, updated Fig.

The Outer Solar System Origins Survey : I. Design and First-Quarter Discoveries

We report the discovery, tracking, and detection circumstances for 85 trans-Neptunian objects (TNOs) from the first 42 deg(2) of the Outer Solar System Origins Survey. This ongoing r-band solar system survey uses the 0.9 deg(2) field of view MegaPrime camera on the 3.6m Canada-France-Hawaii Telescope. Our orbital elements for these TNOs are precise to a fractional semimajor axis uncertaintyPeer reviewe

Non-Vitamin K Oral Anticoagulants (NOAC) Versus Vitamin K Antagonists (VKA) for Atrial Fibrillation with Elective or Urgent Percutaneous Coronary Intervention: A Meta-Analysis with a Particular Focus on Combination Type

Background: Our study aims to perform a meta-analysis of benefits and risks associated with the use of non-vitamin K oral anticoagulants (NOAC) versus vitamin K antagonists (VKA) in patients with a percutaneous coronary intervention (PCI) with a particular focus on the combination type: dual vs. dual antithrombotic therapy (DAT: NOAC + single antiplatelet therapy (SAPT) vs. DAT: VKA + SAPT), dual vs. triple antithrombotic therapy (DAT: NOAC + SAPT vs. TAT: VKA + dual antiplatelet therapy (DAPT)) or triple vs. triple antithrombotic therapy (TAT: NOAC+DAPT vs. TAT: VKA+DAPT). Methods: PubMed, EMBASE, and Cochrane databases were searched to identify randomized controlled trials comparing antithrombotic regimens. Four randomized studies (n = 10.969; PIONEER AF-PCI, RE-DUAL PCI, AUGUSTUS, and ENTRUST-AF PCI) were included. The primary outcome was the composite of major bleeding defined by the International Society on Thrombosis and Hemostasis (ISTH) and clinically relevant bleeding requiring medical intervention (CRNM). Secondary outcomes included all-cause mortality, major adverse cardiovascular events (MACE), myocardial infarction (MI), stroke, and stent thrombosis (ST). Results: Combination strategies with NOACs were associated with reduced risk of major bleeding events across different combination strategies as compared to VKA, with the most significant risk reduction when DAT was compared with TAT, namely DAT with NOAC + SAPT was associated with a 37% relative risk reduction (RRR) of major bleeding events as compared to TAT with VKA + DAPT (RR 0.63; 95% CI, 0.50-0.80). The reduction of major bleeding risks is a class effect of NOACs. Combination strategies of NOACs vs. VKAs resulted in a comparable risk of MACE, MI, stroke, ST, or death. Conclusions: Antithrombotic combinations of NOACs (as DAT or TAT) are safer than VKAs with respect to bleeding risk and result in a satisfactory efficacy with no increase of ischemic or thrombotic events in patients undergoing PCI

MicroRNA as Potential Biomarkers of Platelet Function on Antiplatelet Therapy: A Review

MicroRNAs (miRNAs) are small, non-coding RNAs, able to regulate cellular functions by specific gene modifications. Platelets are the major source for circulating miRNAs, with significant regulatory potential on cardiovascular pathophysiology. MiRNAs have been shown to modify the expression of platelet proteins influencing platelet reactivity. Circulating miRNAs can be determined from plasma, serum, or whole blood, and they can be used as diagnostic and prognostic biomarkers of platelet reactivity during antiplatelet therapy as well as novel therapeutic targets in cardiovascular diseases (CVDs). Herein, we review diagnostic and prognostic value of miRNAs levels related to platelet reactivity based on human studies, presenting its interindividual variability as well as the substantial role of genetics. Furthermore, we discuss antiplatelet treatment in the context of miRNAs alterations related to pathways associated with drug response

The role of miRNAs in regulation of platelet activity and related diseases - a bioinformatic analysis

MicroRNAs (miRNAs) are small, non-coding RNAs, able to regulate cellular functions by induction of mRNA degradation and post-transcriptional repression of gene expression. Platelets are the major source of circulating miRNAs, with significant regulatory potential on cardiovascular pathophysiology and other diseases. MiRNAs have been shown to modify the expression of platelet proteins, which influence the platelets reactivity. Circulating miRNAs can be determined from plasma, serum, or whole blood, and they can be used as diagnostic and prognostic biomarkers as well as therapeutic targets including cardiovascular diseases (CVDs). Herein, we present original results from bioinformatic analyses, which identified top 22 platelet-related miRNAs including hsa-miR-320a, hsa-miR-16-5p, hsa-miR-106a-5p, hsa-miR-320b, hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-92a-3p as widely involved in platelet reactivity and associated diseases, including CVDs, Alzheimer’s and cerebrovascular diseases, cancer and hypertension. Analysis focused on the identification of the highly regulatory targets shared between those miRNAs identified 43 of them. Best ranked genes associated with overall platelet activity and most susceptible for noncoding regulation were PTEN, PIK3R1, CREB1, APP, and MAPK1. Top targets also strongly associated with CVDs were VEGFA, IGF1, ESR1, BDNF, and PPARG. Top targets associated with other platelet-related diseases including cancer identified in our study were TP53, KRAS, and CCND1. The most affected pathways by top miRNAs and top targets included diseases of signal transduction by Growth Factor Receptors (GDFRs) and second messengers, platelet activation, signaling, and aggregation, signaling by VEGF, MAPK family signaling cascades, and signaling by Interleukins. Terms specific only for platelet-related miRNAs included coronary artery disease, platelet degranulation, and neutrophil degranulation, while for the top platelet-related genes it was Estrogen Signaling Receptor (ESR) mediated signaling, extra-nuclear estrogen signaling, and endometriosis. Our results show the novel features of platelet physiology and may provide a basis for further clinical studies focused on platelet reactivity. They also show in which aspects miRNAs can be promising biomarkers of platelet-related pathological processes

MicroRNAs as Biomarkers of Systemic Changes in Response to Endurance Exercise—A Comprehensive Review

Endurance sports have an unarguably beneficial influence on cardiovascular health and general fitness. Regular physical activity is considered one of the most powerful tools in the prevention of cardiovascular disease. MicroRNAs are small particles that regulate the post-transcription gene expression. Previous studies have shown that miRNAs might be promising biomarkers of the systemic changes in response to exercise, before they can be detected by standard imaging or laboratory methods. In this review, we focused on four important physiological processes involved in adaptive changes to various endurance exercises (namely, cardiac hypertrophy, cardiac myocyte damage, fibrosis, and inflammation). Moreover, we discussed miRNAs’ correlation with cardiopulmonary fitness parameter (VO2max). After a detailed literature search, we found that miR-1, miR-133, miR-21, and miR-155 are crucial in adaptive response to exercise

The Importance of Non-Coding RNAs in Neurodegenerative Processes of Diabetes-Related Molecular Pathways

Diabetes mellitus (DM) is a complex condition and serious health problem, with growing occurrence of DM-associated complications occurring globally. Persistent hyperglycemia is confirmed as promoting neurovascular dysfunction leading to irreversible endothelial cell dysfunction, increased neuronal cell apoptosis, oxidative stress and inflammation. These collaboratively and individually result in micro- and macroangiopathy as well as neuropathy demonstrated by progressive neuronal loss. Recently, major efforts have been pursued to select not only useful diagnostic and prognostic biomarkers, but also novel therapeutic approaches. Both microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) belong to a class of non-coding RNAs identified in most of the body fluids i.e., peripheral blood, cerebrospinal fluid, brain tissue and neurons. Numerous miRNAs, lncRNAs and their target genes are able to modulate signaling pathways known to play a role in the pathophysiology of progressive neuronal dysfunction. Therefore, they pose as promising biomarkers and treatment for the vast majority of neurodegenerative disorders. This review provides an overall assessment of both miRNAs’ and lncRNAs’ utility in decelerating progressive nervous system impairment, including neurodegeneration in diabetic pathways