Photo-Fenton and TiO Photocatalytic Inactivation of Model Microorganisms under UV-A; Comparative Efficacy and Optimization.

Photocatalytic inactivation of pathogens in aqueous waste is gaining increasing attention. Several homogeneous and heterogeneous photocatalytic protocols exist using the Fenton's reagent and TiO, respectively. A comprehensive study of homogeneous and heterogeneous photocatalysis on a range of microorganisms will significantly establish the most efficient method.

The transmittable through stinging microbiota differs between honeybees and wasps: a potentially greater microbial risk of the wasp sting for humans.

The present research investigated whether accidental contact through stinging with honeybees, wasps, and hornets could represent a microbial hazard for humans. It has been previously suggested that such contact may transmit pathogens causing infections that could even be fatal for some susceptible individuals. Stinging simulation experiments were performed in the lab with live insects collected from the environment in Lemnos Island (north-eastern Greece), while different selective agar media targeting some clinically important bacteria (i.

Real-time threat assessment based on hidden Markov models.

An essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime- and terrorism-related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model-based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events.

A Survey of Robotic Harvesting Systems and Enabling Technologies.

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning.

Background: Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples.

Effect of Steam to Carbon Dioxide Ratio on the Performance of a Solid Oxide Cell for HO/CO Co-Electrolysis.

The mixture of H and CO, the so-called syngas, is the value-added product of HO and CO co-electrolysis and the feedstock for the production of value-added chemicals (mainly through Fischer-Tropsch). The H/CO ratio determines the process in which syngas will be utilized and the type of chemicals it will produce. In the present work, we investigate the effect of HO/CO (steam/carbon dioxide, S/C) ratio of 0.

The Effect of Gap Reduction on Fluid Dynamics and Mass Transfer in Membrane Narrow Channels Filled with Novel Spacers-A Detailed Computational Study.

The effect of gap thickness reduction 2h (i.e., the reduction h at each membrane surface) is studied on the flow field and mass transfer, in membrane channels filled with novel spacers, under conditions representative of those prevailing in RO desalination modules.

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets.

ITS Metabarcoding Reveals the Effects of Oregano Essential Oil on and Other Fungal Species in Soil Samples.

The destructive effects of wilts are known to affect the production of many crops. The control of and other soilborne pathogens was mainly based on soil fumigation (methyl bromide), which has long been prohibited and, nowadays, is based on a limited number of available fungicides due to legislation restrictions on residue tolerances and environmental impacts. Alternatively, natural and environmentally safe compounds, such as essential oils, are being investigated for their efficacy in the control of soilborne diseases.

Evidence of somatic hypermutation in the antigen binding sites of patients with CLL harboring IGHV genes with 100% germline identity.

Classification of patients with chronic lymphocytic leukemia (CLL) based on the somatic hypermutation (SHM) status of the clonotypic immunoglobulin heavy variable (IGHV) gene has established predictive and prognostic relevance. The SHM status is assessed based on the number of mutations within the IG heavy variable domain sequence, albeit only over the rearranged IGHV gene excluding the variable heavy complementarity determining region 3 (VH CDR3). This may lead to an underestimation of the actual impact of SHM, in fact overlooking the most critical region for antigen-antibody interactions, i.

A novel next-generation sequencing capture-based strategy to report somatic hypermutation status using genomic regions downstream to immunoglobulin rearrangements.

The somatic hypermutation (SHM) status of the clonotypic, rearranged immunoglobulin heavy variable (IGHV) gene is an established prognostic and predictive marker in chronic lymphocytic leukemia (CLL). Analysis of SHM is generally performed by polymerase chain reaction (PCR)-amplification of clonal IGHV-IGHD-IGHJ gene rearrangements followed by sequencing to identify IGHV gene sequences and germline identity. Targeted-hybridization next-generation sequencing (NGS) can simultaneously assess clonality and other genetic aberrations.

Different prognostic impact of recurrent gene mutations in chronic lymphocytic leukemia depending on IGHV gene somatic hypermutation status: a study by ERIC in HARMONY.

Recent evidence suggests that the prognostic impact of gene mutations in patients with chronic lymphocytic leukemia (CLL) may differ depending on the immunoglobulin heavy variable (IGHV) gene somatic hypermutation (SHM) status. In this study, we assessed the impact of nine recurrently mutated genes (BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1) in pre-treatment samples from 4580 patients with CLL, using time-to-first-treatment (TTFT) as the primary end-point in relation to IGHV gene SHM status. Mutations were detected in 1588 (34.

Revisiting Riemannian geometry-based EEG decoding through approximate joint diagonalization.

The wider adoption of Riemannian geometry in electroencephalography (EEG) processing is hindered by two factors: (a) it involves the manipulation of complex mathematical formulations and, (b) it leads to computationally demanding tasks. The main scope of this work is to simplify particular notions of Riemannian geometry and provide an efficient and comprehensible scheme for neuroscientific explorations.To overcome the aforementioned shortcomings, we exploit the concept of approximate joint diagonalization in order to reconstruct the spatial covariance matrices assuming the existence of (and identifying) a common eigenspace in which the application of Riemannian geometry is significantly simplified.

Decision Support System for Emergencies in Microgrids.

The usual operation of a microgrid (MG) may often be challenged by emergencies related to extreme weather conditions and technical issues. As a result, the operator often needs to adapt the MG's management by either: (i) excluding disconnected components, (ii) switching to islanded mode or (iii) performing a black start, which is required in case of a blackout, followed by either direct reconnection to the main grid or islanded operation. The purpose of this paper is to present an optimal Decision Support System (DSS) that assists the MG's operator in all the main possible sorts of emergencies, thus providing an inclusive solution.

A novel multi-atlas segmentation approach under the semi-supervised learning framework: Application to knee cartilage segmentation.

Background And Objective: Multi-atlas based segmentation techniques, which rely on an atlas library comprised of training images labeled by an expert, have proven their effectiveness in multiple automatic segmentation applications. However, the usage of exhaustive patch libraries combined with the voxel-wise labeling incur a large computational cost in terms of memory requirements and execution times.

Methods: To confront this shortcoming, we propose a novel two-stage multi-atlas approach designed under the Semi-Supervised Learning (SSL) framework.

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.

Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state.

Exploring Step-Heating and Lock-In Thermography NDT Using One-Sided Inspection on Low-Emissivity Composite Structures for New Rail Carbodies.

This paper aims to explore the qualification of step- and lock-in heating thermography as techniques capable of inspecting new composite rail carbodies following input and inspection requirements set by the rail manufacturing industry. Specifically, we studied (a) a monolithic CFRP sample (20 mm thickness) and (b) a CFRP-PET foam-CFRP sandwich (40 mm total thickness) component, that were manufactured with artificial defects, to replicate the side wall sections of a carbody. The samples proved to be very challenging to test using only one-sided inspection due to (1) exhibiting significant thickness compared to existing literature, (2) low surface emissivity and (3) that the foam core of the sandwich sample was a thermal insulating material.

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.

Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain-computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection.

PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets.

AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans.

Understanding the Upgrading of Sewage Sludge-Derived Hydrothermal Liquefaction Biocrude via Advanced Characterization.

Hydrothermal liquefaction (HTL) can thermochemically transform sewage sludge into a biocrude with high energy content, high chemical complexity, and high O and N content. The development of an efficient upgrading process for such complex feedstocks necessitates detailed knowledge of the molecular composition and the specific heteroatom-containing compounds to understand and optimize the hydrotreating reactions. In this study, we present the upgrading of sewage sludge-derived HTL biocrude via a two-stage hydrotreatment process and perform advanced chemical characterization of the feedstock, intermediate, and final upgraded products with gas chromatography-mass spectrometry (GC-MS) and Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS).

Introducing the FAIR Principles for research software.

Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value.