Academic Articles & Research Papers

Quantifying Uncertainties in Natural Language Processing Tasks

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...

Correction: Quantification of Unencapsulated Drug in Target Tissues Demonstrates Pharmacological Properties and Therapeutic Effects of Liposomal Topotecan (FF-10850).

Establishment of a Pre-vascularized 3D Lung Cancer Model in Fibrin Gel—Influence of Hypoxia and Cancer-Specific Therapeutics

Lung cancer is the most frequently diagnosed cancer worldwide and the one that causes the highest mortality. In order to understand the disease and to develop new treatments, in vitro human lung cancer model systems which imitate the physio...

SERS of Bottled Water: Automated PET Nanoplastic Detection and Quantification.

Challenges in myeloperoxidase quantification: the inadequacies of current colorimetric approaches

Coal mine methane emissions quantification based on vehicle-based monitoring

Obtaining accurate emissions of methane (CH_4), one of the most important non-carbon-dioxide greenhouse gases, is the basis for formulating and validating emission reduction policies. In terms of shortcomings from the "bottom-up" approach, ...

Microstructure-based variational deep material networks for robust uncertainty quantification in materials digital twins

Abstract Aleatoric uncertainties – irremovable variability in microstructure morphology, constituent behavior, and processing conditions – pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational D...

Stable isotope approaches for CNS lipid metabolism: From precision quantification to metabolic tracing.

Les structures argumentatives de la locution adverbiale polyvalente jusqu’à un certain point

This paper sketches out possible argumentation structures that are activated by the use of the adverbial jusqu’à un certain point as found in a corpus of examples taken from Le Monde. It is in fact a multifunctional adverbial with both quan...

Natural Language Processing in Oncology

IMPORTANCE: Natural language processing (NLP) has the potential to accelerate translation of cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of personalized medicine. This technology can harvest im...

Fully automated AI-based quantification of tumour volume on PSMA PET-CT images is associated with overall survival in prostate cancer.

Aphid-ResNetSwin: An Image Recognition Method with Improved Attention Mechanism for Graded Identification of <i>Myzus persicae</i>

Myzus persicae is the most devastating piercing-sucking pest threatening tobacco production. Precise quantification of infestation severity is a prerequisite for precision pest management, making the integration of visual image analysis hig...

Computer vision and machine‐learning techniques for quantification and predictive modeling of intracellular anticancer drug delivery by nanocarriers

Abstract The field of cancer nanomedicine has made significant progress, but its clinical translation is impeded by many challenges, such as the difficulty in analyzing intracellular anticancer drug release by the nanocarriers due to the la...

Deep metabolome: Applications of deep learning in metabolomics

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using con...

Deep learning approaches to biomedical image segmentation

The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identificat...

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning SystemResearch in context

Summary: Background: Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it...

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images

Abstract Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability...

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems...

A Protein‐Centric Strategy Coupled with Match‐Between‐Run Glycoproteomics Enables Discovery of Robust Site‐Specific Glycan Biomarkers for Hepatocellular Carcinoma

ABSTRACT Dysregulated protein glycosylation is a hallmark of cancer, and systematic investigation of glycosylation patterns is crucial for identifying biomarkers. However, current glycoproteomic studies are constrained by the limited quanti...