A Comprehensive Survey of Deep Learning Approaches in Image Processing.

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.

A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection.

Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations.

18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Large-Vessel Vasculitis During Active and Inactive Disease Stages Is Associated with the Metabolic Profile, but Not the Macrophage-Related Cytokines: A Proof-of-Concept Study.

Giant cell arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial GCA and large-vessel vasculitis (LV)-GCA, involving the thoracic aorta and its branches. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosing patients with systemic disease.

Near-Infrared Spectroscopy for Growth Estimation of Cultures.

The present study proposes the use of Near-Infrared (NIR) spectroscopy as a rapid method for estimating the growth of cultures, avoiding any sample manipulation or pretreatment. NIR spectroscopy in diffuse reflectance mode was used on culture volumes as received, with Principal Component Analysis (PCA) and Partial Least Squares (PLS) linear regression, for developing the calibration model in the wavelength range of 1000-2500 nm, in order to choose the appropriate wavelength to estimate the growth of the microalga. The local reflectance maximum at 1062.

CIDACC: image dataset for automated cell counting.

This CIDACC dataset was created to determine the cell population of microalga during cultivation. has diverse applications, including use as food supplement, biofuel production, and pollutant removal. High resolution images were collected using a microscope and annotated, focusing on computer vision and machine learning models creation for automatic cell detection, counting, size and geometry estimation.

Enhancing Pulmonary Embolism Detection in COVID-19 Patients Through Advanced Deep Learning Techniques.

The intersection of COVID-19 and pulmonary embolism (PE) has posed unprecedented challenges in medical diagnostics. The critical nature of PE and its increased incidence during the pandemic underline the need for improved detection methods. This study evaluates the effectiveness of advanced deep learning techniques in enhancing PE detection in post-COVID-19 patients through Computed Tomography Pulmonary Angiography (CTPA) scans.

Evaluation of ChatGPT's Real-Life Implementation in Undergraduate Dental Education: Mixed Methods Study.

Background: The recent artificial intelligence tool ChatGPT seems to offer a range of benefits in academic education while also raising concerns. Relevant literature encompasses issues of plagiarism and academic dishonesty, as well as pedagogy and educational affordances; yet, no real-life implementation of ChatGPT in the educational process has been reported to our knowledge so far.

Objective: This mixed methods study aimed to evaluate the implementation of ChatGPT in the educational process, both quantitatively and qualitatively.

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images.

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances.

Evaluation of the Performance of Generative AI Large Language Models ChatGPT, Google Bard, and Microsoft Bing Chat in Supporting Evidence-Based Dentistry: Comparative Mixed Methods Study.

Background: The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including dentistry, raises questions about their accuracy.

Objective: This study aims to comparatively evaluate the answers provided by 4 LLMs, namely Bard (Google LLC), ChatGPT-3.5 and ChatGPT-4 (OpenAI), and Bing Chat (Microsoft Corp), to clinically relevant questions from the field of dentistry.

Hydrolytic Activity of Mitochondrial FF-ATP Synthase as a Target for Myocardial Ischemia-Reperfusion Injury: Discovery and and Evaluation of Novel Inhibitors.

FF-ATP synthase is the mitochondrial complex responsible for ATP production. During myocardial ischemia, it reverses its activity, hydrolyzing ATP and leading to energetic deficit and cardiac injury. We aimed to discover novel inhibitors of ATP hydrolysis, accessing the druggability of the target within ischemia(I)/reperfusion(R) injury.

NMR-based metabolomics in giant cell arteritis and polymyalgia rheumatica sequential sera differentiates active and inactive disease.

Objectives: Giant Cell Arteritis-(GCA) is an inflammatory disease following a chronic, relapsing course. The metabolic alterations related to the intense inflammatory process during the active phase and to the rapid impact of steroid treatment, remain unknown. The study aims to investigate the serum metabolome in active and inactive disease state.

Uracil/H Symport by FurE Refines Aspects of the Rocking-bundle Mechanism of APC-type Transporters.

Transporters mediate the uptake of solutes, metabolites and drugs across the cell membrane. The eukaryotic FurE nucleobase/H symporter of Aspergillus nidulans has been used as a model protein to address structure-function relationships in the APC transporter superfamily, members of which are characterized by the LeuT-fold and seem to operate by the so-called 'rocking-bundle' mechanism. In this study, we reveal the binding mode, translocation and release pathway of uracil/H by FurE using path collective variable, funnel metadynamics and rational mutational analysis.

Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon.

Using protein structure to predict function, interactions, and evolutionary history is still an open challenge, with existing approaches relying extensively on protein homology and families. Here, we present Machaon, a data-driven method combining orientation invariant metrics on phi-psi angles, inter-residue contacts and surface complexity. It can be readily applied on whole structures or segments-such as domains and binding sites.

Identification of a Dual Inhibitor of Secreted Phospholipase A (GIIA sPLA) and SARS-CoV-2 Main Protease.

The development of novel agents to combat COVID-19 is of high importance. SARS-CoV-2 main protease (M) is a highly attractive target for the development of novel antivirals and a variety of inhibitors have already been developed. Accumulating evidence on the pathobiology of COVID-19 has shown that lipids and lipid metabolizing enzymes are critically involved in the severity of the infection.

A novel tool that allows interactive screening of PubMed citations showed promise for the semi-automation of identification of Biomedical Literature.

Background And Objectives: Systematic reviews form the basis of evidence-based medicine, but are expensive and time-consuming to produce. To address this burden, we have developed a literature identification system (Pythia) that combines the query formulation and citation screening steps.

Methods: Pythia incorporates a set of natural-language questions with machine-learning algorithms to rank all PubMed citations based on relevance, returning the 100 top-ranked citations for human screening.

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity.

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches.

Population Pharmacokinetic Modelling of the Complex Release Kinetics of Octreotide LAR: Defining Sub-Populations by Cluster Analysis.

The aim of the study is to develop a population pharmacokinetic (PPK) model, of Octreotide long acting repeatable (LAR) formulation in healthy volunteers, which describes the highly variable, multiple peak absorption pattern of the pharmacokinetics of the drug, in individual and population levels. An empirical absorption model, coupled with a one-compartment distribution model with linear elimination was found to describe the data well. Absorption was modelled as a weighted sum of a first order and three transit compartment absorption processes, with delays and appropriately constrained model parameters.

Modeling asset allocations and a new portfolio performance score.

We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper, we focus on two specific questions.

Using gamification and IoT-based educational tools towards energy savings - some experiences from two schools in Italy and Greece.

Climate change and the need for sustainable development have become part of our daily lives. In this context, it is crucial to involve the educational community to the discussion, both students and teachers; by increasing awareness about these issues and the ways school communities can contribute to energy savings, we can kick-start a change towards more sustainable practices in our societies. The Green Awareness in Action (GAIA) H2020 research project implemented an IoT-based approach in several European schools for sustainability awareness and energy efficiency, while at the same time aiming for increasing students' digital skills.

Toward the creation of an ontology for the coupling of atmospheric electricity with biological systems.

Atmospheric electric fields (AEFs) are produced by both natural processes and electrical infrastructure and are increasingly recognized to influence and interfere with various organisms and biological processes, including human well-being. Atmospheric electric fields, in particular electromagnetic fields (EMFs), currently attract a lot of scientific attention due to emerging technologies such as 5G and satellite internet. However, a broader retrospective analysis of available data for both natural and artificial AEFs and EMFs is hampered due to a lack of a semantic approach, preventing data sharing and advancing our understanding of its intrinsic links.

Glossary on atmospheric electricity and its effects on biology.

There is an increasing interest to study the interactions between atmospheric electrical parameters and living organisms at multiple scales. So far, relatively few studies have been published that focus on possible biological effects of atmospheric electric and magnetic fields. To foster future work in this area of multidisciplinary research, here we present a glossary of relevant terms.

Screening of Heteroaromatic Scaffolds against Cystathionine Beta-Synthase Enables Identification of Substituted Pyrazolo[3,4-c]Pyridines as Potent and Selective Orthosteric Inhibitors.

Cystathionine -synthase (CBS) is a key enzyme in the production of the signaling molecule hydrogen sulfide, deregulation of which is known to contribute to a range of serious pathological states. Involvement of hydrogen sulfide in pathways of paramount importance for cellular homeostasis renders CBS a promising drug target. An in-house focused library of heteroaromatic compounds was screened for CBS modulators by the methylene blue assay and a pyrazolopyridine derivative with a promising CBS inhibitory potential was discovered.

Population pharmacokinetics of micafungin over repeated doses in critically ill patients: a need for a loading dose?

Objectives: To study the population pharmacokinetics of micafungin in critically ill patients, evaluate and optimize dosage regimens.

Methods: An HPLC-fluorescence bioassay for micafungin was developed, fully validated and applied to a pharmacokinetic study conducted in 14 ICU patients. Dense blood sampling was performed from days 1 to 7.

Challenges in coupling atmospheric electricity with biological systems.

The atmosphere is host to a complex electric environment, ranging from a global electric circuit generating fluctuating atmospheric electric fields to local lightning strikes and ions. While research on interactions of organisms with their electrical environment is deeply rooted in the aquatic environment, it has hitherto been confined to interactions with local electrical phenomena and organismal perception of electric fields. However, there is emerging evidence of coupling between large- and small-scale atmospheric electrical phenomena and various biological processes in terrestrial environments that even appear to be tied to continental waters.

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations.

Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases.